The Banking Business…Note Quote

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Why is lending indispensable to banking? This not-so new question has garnered a lot of steam, especially in the wake of 2007-08 crisis. In India, however, this question has become quite a staple of CSOs purportedly carrying out research and analysis in what has, albeit wrongly, begun to be considered offshoots of neoliberal policies of capitalism favoring cronyism on one hand, and marginalizing priority sector focus by nationalized banks on the other. Though, it is a bit far-fetched to call this analysis mushrooming on artificially-tilled grounds, it nevertheless isn’t justified for the leaps such analyses assume don’t exist. The purpose of this piece is precisely to demystify and be a correctional to such erroneous thoughts feeding activism. 

The idea is to launch from the importance of lending practices to banking, and why if such practices weren’t the norm, banking as a business would falter. Monetary and financial systems are creations of double entry-accounting, in that, when banks lend, the process is a creation of a matrix/(ces) of new assets and new liabilities. Monetary system is a counterfactual, which is a bookkeeping mechanism for the intermediation of real economic activity giving a semblance of reality to finance capitalism in substance and form. Let us say, a bank A lends to a borrower. By this process, a new asset and a new liability is created for A, in that, there is a debit under bank assets, and a simultaneous credit on the borrower’s account. These accounting entries enhance bank’s and borrower’s  respective categories, making it operationally different from opening bank accounts marked by deposits. The bank now has an asset equal to the amount of the loan and a liability equal to the deposit. Put a bit more differently, bank A writes a cheque or draft for the borrower, thus debiting the borrower’s loan account and crediting a payment liability account. Now, this borrower decides to deposit this cheque/draft at a different bank B, which sees the balance sheet of B grow by the same amount, with a payment due asset and a deposit liability. This is what is a bit complicated and referred to as matrix/(ces) at the beginning of this paragraph. The obvious complication is due to a duplication of balance sheet across the banks A and B, which clearly stands in need of urgent resolution. This duplication is categorized under the accounting principle of ‘Float’, and is the primary requisite for resolving duplicity. Float is the amount of time it takes for money to move from one account to another. The time period is significant because it’s as if the funds are in two places at once. The money is still in the cheque writer’s account, and the cheque recipient may have deposited funds to their bank as well. The resolution is reached when the bank B clears the cheque/draft and receives a reserve balance credit in exchange, at which point the bank A sheds both reserve balances and its payment liability. Now, what has happened is that the systemic balance sheet has grown by the amount of the original loan and deposit, even if these are domiciles in two different banks A and B. In other words, B’s balance sheet has an increased deposits and reserves, while A’s balance sheet temporarily unchanged due to loan issued offset reserves decline. It needs to be noted that here a reserve requirement is created in addition to a capital requirement, the former with the creation of a deposit, while the latter with the creation of a loan, implying that loans create capital requirement, whereas deposits create reserve requirement.  Pari Passu, bank A will seek to borrow new funding from money markets and bank B could lend funds into these markets. This is a natural reaction to the fluctuating reserve distribution created at banks A and B. This course of normalization of reserve fluctuations is a basic function of commercial bank reserve management. Though, this is a typical case involving just two banks, a meshwork of different banks, their counterparties, are involved in such transactions that define present-day banking scenario, thus highlighting complexity referred to earlier. 

Now, there is something called the Cash Reserve Ratio (CRR), whereby banks in India (and elsewhere as well) are required to hold a certain proportion of their deposits in the form of cash. However, these banks don’t hold these as cash with themselves for they deposit such cash (also known as currency chests) with the Reserve Bank of India (RBI). For example, if the bank’s deposits increase by Rs. 100, and if the CRR is 4% (this is the present CRR stipulated by the RBI), then the banks will have to hold Rs. 4 with the RBI, and the bank will be able to use only Rs. 96 for investments and lending, or credit purpose. Therefore, higher the CRR, lower is the amount that banks will be able to use for lending and investment. CRR is a tool used by the RBI to control liquidity in the banking system. Now, if the bank A lends out Rs. 100, it incurs a reserve requirement of Rs. 4, or in other words, for every Rs. 100 loan, there is a simultaneous reserve requirement of Rs. 4 created in the form of reserve liability. But, there is a further ingredient to this banking complexity in the form of Tier-1 and Tier-2 capital as laid down by BASEL Accords, to which India is a signatory. Under the accord, bank’s capital consists of tier-1 and tier-2 capital, where tier-1 is bank’s core capital, while tier-2 is supplementary, and the sum of these two is bank’s total capital. This is a crucial component and is considered highly significant by regulators (like the RBI, for instance), for the capital ratio is used to determine and rank bank’s capital adequacy. tier-1 capital consists of shareholders’ equity and retained earnings, and gives a measure of when the bank must absorb losses without ceasing business operations. BASEL-3 has capped the minimum tier-1 capital ratio at 6%, which is calculated by dividing bank’s tier-1 capital by its total risk-based assets. Tier-2 capital includes revaluation reserves, hybrid capital instruments and subordinated term debt, general loan-loss revenues, and undisclosed reserves. tier-2 capital is supplementary since it is less reliable than tier-1 capital. According to BASEL-3, the minimum total capital ratio is 8%, which indicates the minimum tier-2 capital ratio at 2%, as opposed to 6% for the tier-1 capital ratio. Going by these norms, a well capitalized bank in India must have a 8% combined tier-1 and tier-2 capital ratio, meaning that for every Rs. 100 bank loan, a simultaneous regulatory capital liability of Rs. 8 of tier-1/tier-2 is generated. Further, if a Rs. 100 loan has created a Rs. 100 deposit, it has actually created an asset of Rs. 100 for the bank, while at the same time a liability of Rs. 112, which is the sum of deposits and required reserves and capital. On the face of it, this looks like a losing deal for the bank. But, there is more than meets the eye here. 

Assume bank A lends Mr. Amit Modi Rs. 100, by crediting Mr. Modi’s deposit account held at A with Rs. 100. Two new liabilities are immediately created that need urgent addressing, viz. reserve and capital requirement. One way to raise Rs. 8 of required capital, bank A sells shares, or raise equity-like debt or retain earnings. The other way is to attach an origination fee of 10% (sorry for the excessively high figure here, but for sake of brevity, let’s keep it at 10%). This 10% origination fee helps maintain retained earnings and assist satisfying capital requirements. Now, what is happening here might look unique, but is the key to any banking business of lending, i.e. the bank A is meeting its capital requirement by discounting a deposit it created of its own loan, and thereby reducing its liability without actually reducing its asset. To put it differently, bank A extracts a 10% fee from Rs. 100 it loans, thus depositing an actual sum of only Rs. 90. With this, A’s reserve requirement decrease by Rs. 3.6 (remember 4% is the CRR). This in turn means that the loan of Rs. 100 made by A actually creates liabilities worth Rs. Rs. 108.4 (4-3.6 = 0.4 + 8). The RBI, which imposes the reserve requirement will follow up new deposit creation with a systemic injection sufficient to accommodate the requirement of bank B that has issued the deposit. And this new requirement is what is termed the targeted asset for the bank. It will fund this asset in the normal course of its asset-liability management process, just as it would any other asset. At the margin, the bank actually has to compete for funding that will draw new reserve balances into its position with the RBI. This action of course is commingled with numerous other such transactions that occur in the normal course of reserve management. The sequence includes a time lag between the creation of the deposit and the activation of the corresponding reserve requirement against that deposit. A bank in theory can temporarily be at rest in terms of balance sheet growth, and still be experiencing continuous shifting in the mix of asset and liability types, including shifting of deposits. Part of this deposit shifting is inherent in a private sector banking system that fosters competition for deposit funding. The birth of a demand deposit in particular is separate from retaining it through competition. Moreover, the fork in the road that was taken in order to construct a private sector banking system implies that the RBI is not a mere slush fund that provides unlimited funding to the banking system.  

The originating accounting entries in the above case are simple, a loan asset and a deposit liability. But this is only the start of the story. Commercial bank ‘asset-liability management’ functions oversee the comprehensive flow of funds in and out of individual banks. They control exposure to the basic banking risks of liquidity and interest rate sensitivity. Somewhat separately, but still connected within an overarching risk management framework, banks manage credit risk by linking line lending functions directly to the process of internal risk assessment and capital allocation. Banks require capital, especially equity capital, to take risk, and to take credit risk in particular. Interest rate risk and interest margin management are critical aspects of bank asset-liability management. The asset-liability management function provides pricing guidance for deposit products and related funding costs for lending operations. This function helps coordinate the operations of the left and the right hand sides of the balance sheet. For example, a central bank interest rate change becomes a cost of funds signal that transmits to commercial bank balance sheets as a marginal pricing influence. The asset-liability management function is the commercial bank coordination function for this transmission process, as the pricing signal ripples out to various balance sheet categories. Loan and deposit pricing is directly affected because the cost of funds that anchors all pricing in finance has been changed. In other cases, a change in the term structure of market interest rates requires similar coordination of commercial bank pricing implications. And this reset in pricing has implications for commercial bank approaches to strategies and targets for the compositional mix of assets and liabilities. The life of deposits is more dynamic than their birth or death. Deposits move around the banking system as banks compete to retain or attract them. Deposits also change form. Demand deposits can convert to term deposits, as banks seek a supply of longer duration funding for asset-liability matching purposes. And they can convert to new debt or equity securities issued by a particular bank, as buyers of these instruments draw down their deposits to pay for them. All of these changes happen across different banks, which can lead to temporary imbalances in the nominal matching of assets and liabilities, which in turn requires active management of the reserve account level, with appropriate liquidity management responses through money market operations in the short term, or longer term strategic adjustment in approaches to loan and deposit market share. The key idea here is that banks compete for deposits that currently exist in the system, including deposits that can be withdrawn on demand, or at maturity in the case of term deposits. And this competition extends more comprehensively to other liability forms such as debt, as well as to the asset side of the balance sheet through market share strategies for various lending categories. All of this balance sheet flux occurs across different banks, and requires that individual banks actively manage their balance sheets to ensure that assets are appropriately and efficiently funded with liabilities and equity. The ultimate purpose of reserve management is not reserve positioning per se. The end goal is balance sheets are in balance. The reserve system records the effect of this balance sheet activity. And even if loan books remain temporarily unchanged, all manner of other banking system assets and liabilities may be in motion. This includes securities portfolios, deposits, debt liabilities, and the status of the common equity and retained earnings account. And of course, loan books don’t remain unchanged for very long, in which case the loan/deposit growth dynamic comes directly into play on a recurring basis. 

Commercial banks’ ability to create money is constrained by capital. When a bank creates a new loan, with an associated new deposit, the bank’s balance sheet size increases, and the proportion of the balance sheet that is made up of equity (shareholders’ funds, as opposed to customer deposits, which are debt, not equity) decreases. If the bank lends so much that its equity slice approaches zero, as happened in some banks prior to the financial crisis, even a very small fall in asset prices is enough to render it insolvent. Regulatory capital requirements are intended to ensure that banks never reach such a fragile position. In contrast, central banks’ ability to create money is constrained by the willingness of their government to back them, and the ability of that government to tax the population. In practice, most central bank money these days is asset-backed, since central banks create new money when they buy assets in open market operations or Quantitative Easing, and when they lend to banks. However, in theory a central bank could literally spirit money from thin air without asset purchases or lending to banks. This is Milton Friedman’s famous helicopter drop. The central bank would become technically insolvent as a result, but provided the government is able to tax the population, that wouldn’t matter. The ability of the government to tax the population depends on the credibility of the government and the productive capacity of the economy. Hyperinflation can occur when the supply side of the economy collapses, rendering the population unable and/or unwilling to pay taxes. It can also occur when people distrust a government and its central bank so much that they refuse to use the currency that the central bank creates. Distrust can come about because people think the government is corrupt and/or irresponsible, or because they think that the government is going to fall and the money it creates will become worthless. But nowhere in the genesis of hyperinflation does central bank insolvency feature….

 

Skeletal of the Presentation on AIIB and Blue Economy in Mumbai during the Peoples’ Convention on 22nd June 2018

Main features in AIIB Financing

  1. investments in regional members
  2. supports longer tenors and appropriate grace period
  3. mobilize funding through insurance, banks, funds and sovereign wealth (like the China Investment Corporation (CIC) in the case of China)
  4. funds on economic/financial considerations and on project benefits, eg. global climate, energy security, productivity improvement etc.

Public Sector:

  1. sovereign-backed financing (sovereign guarantee)
  2. loan/guarantee

Private Sector:

  1. non-sovereign-backed financing (private sector, State Owned Enterprises (SOEs), sub-sovereign and municipalities)
  2. loans and equity
  3. bonds, credit enhancement, funds etc.

—— portfolio is expected to grow steadily with increasing share of standalone projects from 27% in 2016 to 39% in 2017 and 42% in 2018 (projected)

—— share of non-sovereign-backed projects has increased from 1% in 2016 to 36% of portfolio in 2017. share of non-sovereign-backed projects is projected to account for about 30% in 2018

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Why would AIIB be interested in the Blue Economy?

  1. To appropriate (expropriate) the potential of hinterlands
  2. increasing industrialization
  3. increasing GDP
  4. increasing trade
  5. infrastructure development
  6. Energy and Minerals in order to bring about a changing landscape
  7. Container: regional collaboration and competition

AIIB wishes to change the landscape of infrastructure funding across its partner countries, laying emphasis on cross-country and cross-sectoral investments in the shipping sector — Yee Ean Pang, Director General, Investment Operations, AIIB.

He also opined that in the shipping sector there is a need for private players to step in, with 40-45 per cent of stake in partnership being offered to private players.

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Projects aligned with Sagarmala are being considered for financial assistance by the Ministry of Shipping under two main headings:

1. Budgetary Allocations from the Ministry of Shipping

    a. up to 50% of the project cost in the form of budgetary grant

    b. Projects having high social impact but low/no Internal Rate of Return (IRR) may be provided funding, in convergence with schemes of other central line ministries. IRR is a metric used in capital budgeting to estimate the profitability of potential investments. It is a discount rate that makes the net present value (NPV) of all cash flows from a particular project equal to zero. NPV is the difference between the present value of cash inflows and present value of cash outflows over a period of time. IRR is sometimes referred to as “economic rate of return” or “discounted cash flow rate of return.” The use of “internal” refers to the omission of external factors, such as the cost of capital or inflation, from the calculation.

2. Funding in the form of equity by Sagarmala Development Co. Ltd.

    a. SDCL to provide 49% equity funding to residual projects

    b. monitoring is to be jointly done by SDCL and implementing agency at the SPV level

    c.  project proponent to bear operation and maintenance costs of the project

     i. importantly, expenses incurred for project development to be treated as part of SDCL’s equity contribution

     ii. preferences to be given to projects where land is being contributed by the project proponent

What are the main financing issues?

  1. Role of MDBs and BDBs for promotion of shipping sector in the country
  2. provision of long-term low-cost loans to shipping companies for procurement of vessels
  3. PPPs (coastal employment zones, port connectivity projects), EPCs, ECBs (port expansion and new port development), FDI in Make in India 2.0 of which shipping is a major sector identified, and conventional bank financing for port modernization and port connectivity

the major constraining factors, however, are:

  1. uncertainty in the shipping sector, cyclical business nature
  2. immature financial markets

Tranche Declension.

800px-CDO_-_FCIC_and_IMF_Diagram

With the CDO (collateralized debt obligation) market picking up, it is important to build a stronger understanding of pricing and risk management models. The role of the Gaussian copula model, has well-known deficiencies and has been criticized, but it continues to be fundamental as a starter. Here, we draw attention to the applicability of Gaussian inequalities in analyzing tranche loss sensitivity to correlation parameters for the Gaussian copula model.

We work with an RN-valued Gaussian random variable X = (X1, … , XN), where each Xj is normalized to mean 0 and variance 1, and study the equity tranche loss

L[0,a] = ∑m=1Nlm1[xm≤cm] – {∑m=1Nlm1[xm≤cm] – a}

where l1 ,…, lN > 0, a > 0, and c1,…, cN ∈ R are parameters. We thus establish an identity between the sensitivity of E[L[0,a]] to the correlation rjk = E[XjXk] and the parameters cj and ck, from where subsequently we come to the inequality

∂E[L[0,a]]/∂rjk ≤ 0

Applying this inequality to a CDO containing N names whose default behavior is governed by the Gaussian variables Xj shows that an increase in name-to-name correlation decreases expected loss in an equity tranche. This is a generalization of the well-known result for Gaussian copulas with uniform correlation.

Consider a CDO consisting of N names, with τj denoting the (random) default time of the jth name. Let

Xj = φj-1(Fjj))

where Fj is the distribution function of τj (relative to the market pricing measure), assumed to be continuous and strictly increasing, and φj is the standard Gaussian distribution function. Then for any x ∈ R we have

P[Xj ≤ x] = P[τj ≤ Fj-1j(x))] = Fj(Fj-1j(x))) = φj(x)

which means that Xj has standard Gaussian distribution. The Gaussian copula model posits that the joint distribution of the Xj is Gaussian; thus,

X = (X1, …., Xn)

is an RN-valued Gaussian variable whose marginals are all standard Gaussian. The correlation

τj = E[XjXk]

reflects the default correlation between the names j and k. Now let

pj = E[τj ≤ T] = P[Xj ≤ cj]

be the probability that the jth name defaults within a time horizon T, which is held constant, and

cj = φj−1(Fj(T))

is the default threshold of the jth name.

In schematics, when we explore the essential phenomenon, the default of name j, which happens if the default time τis within the time horizon T, results in a loss of amount lj > 0 in the CDO portfolio. Thus, the total loss during the time period [0, T] is

L = ∑m=1Nlm1[xm≤cm]

This is where we are essentially working with a one-period CDO, and ignoring discounting from the random time of actual default. A tranche is simply a range of loss for the portfolio; it is specified by a closed interval [a, b] with 0 ≤ a ≤ b. If the loss x is less than a, then this tranche is unaffected, whereas if x ≥ b then the entire tranche value b − a is eaten up by loss; in between, if a ≤ x ≤ b, the loss to the tranche is x − a. Thus, the tranche loss function t[a, b] is given by

t[a, b](x) = 0 if x < a; = x – a, if x ∈ [a, b]; = b – a; if x > b

or compactly,

t[a, b](x) = (x – a)+ – (x – b)+

From this, it is clear that t[a, b](x) is continuous in (a, b, x), and we see that it is a non-decreasing function of x. Thus, the loss in an equity tranche [0, a] is given by

t[0,a](L) = L − (L − a)+

with a > 0.

Banking and Lending/Investment. How Monetary Policy Becomes Decisive? Some Branching Rumination.

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Among the most notoriously pernicious effects of asset price inflation is that it offers speculators the prospect of gain in excess of the costs of borrowing the money to buy the asset whose price is being inflated. This is how many unstable Ponzi financing structures begin. There are usually strict regulations to prevent or limit banks’ direct investment in financial instruments without any assured residual liquidity, such as equity or common stocks. However, it is less easy to prevent banks from lending to speculative investors, who then use the proceeds of their loans to buy securities or to limit lending secured on financial assets. As long as asset markets are being inflated, such credit expansions also conceal from banks, their shareholders and their regulators the disintermediation that occurs when the banks’ best borrowers, governments and large companies, use bills and company paper instead of bank loans for their short-term financing. As long as the boom proceeds, banks can enjoy the delusion that they can replace the business of governments and large companies with good lending secured on stocks.

In addition to undermining the solvency of the banking system, and distracting commerce and industry with the possibilities of lucrative corporate restructuring, capital market inflation also tends to make monetary policy ineffective. Monetary policy is principally the fixing of reserve requirements, buying and selling short-term paper or bills in the money or inter-bank markets, buying and selling government bonds and fixing short-term interest rates. As noted in the previous section, with capital market inflation there has been a proliferation of short-term financial assets traded in the money markets, as large companies and banks find it cheaper to issue their own paper than to borrow for banks. This disintermediation has extended the range of short-term liquid assets which banks may hold. As a result of this it is no longer possible for central banks, in countries experiencing capital market inflation, to control the overall amount of credit available in the economy: attempts to squeeze the liquidity of banks in order to limit their credit advances by, say, open market operations (selling government bonds) are frustrated by the ease with which banks may restore their liquidity by selling bonds or their holdings of short-term paper or bills. In this situation central banks have been forced to reduce the scope of their monetary policy to the setting of short-term interest rates.

Economists have long believed that monetary policy is effective in controlling price inflation in the economy at large, as opposed to inflation of securities prices. Various rationalizations have been advanced for this efficacy of monetary policy. For the most part they suppose some automatic causal connection between changes in the quantity of money in circulation and changes in prices, although the Austrian School of Economists (here, here, here, and here) tended on occasion to see the connection as being between changes in the rate of interest and changes in prices.

Whatever effect changes in the rate of interest may have on the aggregate of money circulating in the economy, the effect of such changes on prices has to be through the way in which an increase or decrease in the rate of interest causes alterations in expenditure in the economy. Businesses and households are usually hard-headed enough to decide their expenditure and financial commitments in the light of their nominal revenues and cash outflows, which may form their expectations, rather than in accordance with their expectations or optimizing calculations. If the same amount of money continues to be spent in the economy, then there is no effective reason for the business-people setting prices to vary prices. Only if expenditure in markets is rising or falling would retailers and industrialists consider increasing or decreasing prices. Because price expectations are observable directly with difficulty, they may explain everything in general and therefore lack precision in explaining anything in particular. Notwithstanding their effects on all sorts of expectations, interest rate changes affect inflation directly through their effects on expenditure.

The principal expenditure effects of changes in interest rates occur among net debtors in the economy, i.e., economic units whose financial liabilities exceed their financial assets. This is in contrast to net creditors, whose financial assets exceed their liabilities, and who are usually wealthy enough not to have their spending influenced by changes in interest rates. If they do not have sufficient liquid savings out of which to pay the increase in their debt service payments, then net debtors have their expenditure squeezed by having to devote more of their income to debt service payments. The principal net debtors are governments, households with mortgages and companies with large bank loans.

With or without capital market inflation, higher interest rates have never constrained government spending because of the ease with which governments may issue debt. In the case of indebted companies, the degree to which their expenditure is constrained by higher interest rates depends on their degree of indebtedness, the available facilities for additional financing and the liquidity of their assets. As a consequence of capital market inflation, larger companies reduce their borrowing from banks because it becomes cheaper and more convenient to raise even short- term finance in the booming securities markets. This then makes the expenditure of even indebted companies less immediately affected by changes in bank interest rates, because general changes in interest rates cannot affect the rate of discount or interest paid on securities already issued. Increases in short-term interest rates to reduce general price inflation can then be easily evaded by companies financing themselves by issuing longer-term securities, whose interest rates tend to be more stable. Furthermore, with capital market inflation, companies are more likely to be over-capitalized and have excessive financial liabilities, against which companies tend to hold a larger stock of more liquid assets. As inflated financial markets have become more unstable, this has further increased the liquidity preference of large companies. This excess liquidity enables the companies enjoying it to gain higher interest income to offset the higher cost of their borrowing and to maintain their planned spending. Larger companies, with access to capital markets, can afford to issue securities to replenish their liquid reserves.

If capital market inflation reduces the effectiveness of monetary policy against product price inflation, because of the reduced borrowing of companies and the ability of booming asset markets to absorb large quantities of bank credit, interest rate increases have appeared effective in puncturing asset market bubbles in general and capital market inflations in particular. Whether interest rate rises actually can effect an end to capital market inflation depends on how such rises actually affect the capital market. In asset markets, as with anti-inflationary policy in the rest of the economy, such increases are effective when they squeeze the liquidity of indebted economic units by increasing the outflow of cash needed to service debt payments and by discouraging further speculative borrowing. However, they can only be effective in this way if the credit being used to inflate the capital market is short term or is at variable rates of interest determined by the short-term rate.

Keynes’s speculative demand for money is the liquidity preference or demand for short-term securities of rentiers in relation to the yield on long-term securities. Keynes’s speculative motive is ‘a continuous response to gradual changes in the rate of interest’ in which, as interest rates along the whole maturity spectrum decline, there is a shift in rentiers’ portfolio preference toward more liquid assets. Keynes clearly equated a rise in equity (common stock) prices with just such a fall in interest rates. With falling interest rates, the increasing preference of rentiers for short-term financial assets could keep the capital market from excessive inflation.

But the relationship between rates of interest, capital market inflation and liquidity preference is somewhat more complicated. In reality, investors hold liquid assets not only for liquidity, which gives them the option to buy higher-yielding longer-term stocks when their prices fall, but also for yield. This marginalizes Keynes’s speculative motive for liquidity. The motive was based on Keynes’s distinction between what he called ‘speculation’ (investment for capital gain) and ‘enterprise’ (investment long term for income). In our times, the modern rentier is the fund manager investing long term on behalf of pension and insurance funds and competing for returns against other funds managers. An inflow into the capital markets in excess of the financing requirements of firms and governments results in rising prices and turnover of stock. This higher turnover means greater liquidity so that, as long as the capital market is being inflated, the speculative motive for liquidity is more easily satisfied in the market for long-term securities.

Furthermore, capital market inflation adds a premium of expected inflation, or prospective capital gain, to the yield on long-term financial instruments. Hence when the yield decreases, due to an increase in the securities’ market or actual price, the prospective capital gain will not fall in the face of this capital appreciation, but may even increase if it is large or abrupt. Rising short-term interest rates will therefore fail to induce a shift in the liquidity preference of rentiers towards short-term instruments until the central bank pushes these rates of interest above the sum of the prospective capital gain and the market yield on long-term stocks. Only at this point will there be a shift in investors’ preferences, causing capital market inflation to cease, or bursting an asset bubble.

This suggests a new financial instability hypothesis, albeit one that is more modest and more limited in scope and consequence than Minsky’s Financial Instability Hypothesis. During an economic boom, capital market inflation adds a premium of expected capital gain to the market yield on long-term stocks. As long as this yield plus the expected capital gain exceed the rate of interest on short-term securities set by the central bank’s monetary policy, rising short-term interest rates will have no effect on the inflow of funds into the capital market and, if this inflow is greater than the financing requirements of firms and governments, the resulting capital market inflation. Only when the short-term rate of interest exceeds the threshold set by the sum of the prospective capital gain and the yield on long-term stocks will there be a shift in rentiers’ preferences. The increase in liquidity preference will reduce the inflow of funds into the capital market. As the rise in stock prices moderates, the prospective capital gain gets smaller, and may even become negative. The rentiers’ liquidity preference increases further and eventually the stock market crashes, or ceases to be active in stocks of longer maturities.

At this point, the minimal or negative prospective capital gain makes equity or common stocks unattractive to rentiers at any positive yield, until the rate of interest on short-term securities falls below the sum of the prospective capital gain and the market yield on those stocks. When the short-term rate of interest does fall below this threshold, the resulting reduction in rentiers’ liquidity preference revives the capital market. Thus, in between the bursting of speculative bubbles and the resurrection of a dormant capital market, monetary policy has little effect on capital market inflation. Hence it is a poor regulator for ‘squeezing out inflationary expectations’ in the capital market.

Collateral Debt Obligations. Thought of the Day 111.0

A CDO is a general term that describes securities backed by a pool of fixed-income assets. These assets can be bank loans (CLOs), bonds (CBOs), residential mortgages (residential- mortgage–backed securities, or RMBSs), and many others. A CDO is a subset of asset- backed securities (ABS), which is a general term for a security backed by assets such as mortgages, credit card receivables, auto loans, or other debt.

To create a CDO, a bank or other entity transfers the underlying assets (“the collateral”) to a special-purpose vehicle (SPV) that is a separate legal entity from the issuer. The SPV then issues securities backed with cash flows generated by assets in the collateral pool. This general process is called securitization. The securities are separated into tranches, which differ primarily in the priority of their rights to the cash flows coming from the asset pool. The senior tranche has first priority, the mezzanine second, and the equity third. Allocation of cash flows to specific securities is called a “waterfall”. A waterfall is specified in the CDO’s indenture and governs both principal and interest payments.

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1: If coverage tests are not met, and to the extent not corrected with principal proceeds, the remaining interest proceeds will be used to redeem the most senior notes to bring the structure back into compliance with the coverage tests. Interest on the mezzanine securities may be deferred and compounded if cash flow is not available to pay current interest due.

One may observe that the creation of a CDO is a complex and costly process. Professionals such as bankers, lawyers, rating agencies, accountants, trustees, fund managers, and insurers all charge considerable fees to create and manage a CDO. In other words, the cash coming from the collateral is greater than the sum of the cash paid to all security holders. Professional fees to create and manage the CDO make up the difference.

CDOs are designed to offer asset exposure precisely tailored to the risk that investors desire, and they provide liquidity because they trade daily on the secondary market. This liquidity enables, for example, a finance minister from the Chinese government to gain exposure to the U.S. mortgage market and to buy or sell that exposure at will. However, because CDOs are more complex securities than corporate bonds, they are designed to pay slightly higher interest rates than correspondingly rated corporate bonds.

CDOs enable a bank that specializes in making loans to homeowners to make more loans than its capital would otherwise allow, because the bank can sell its loans to a third party. The bank can therefore originate more loans and take in more origination fees. As a result, consumers have more access to capital, banks can make more loans, and investors a world away can not only access the consumer loan market but also invest with precisely the level of risk they desire.

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1: To the extent not paid by interest proceeds.

2: To the extent senior note coverage tests are met and to the extent not already paid by interest proceeds. If coverage tests are not met, the remaining principal proceeds will be used to redeem the most senior notes to bring the structure back into compliance with the coverage tests. Interest on the mezzanine securities may be deferred and compounded if cash flow is not available to pay current interest due.

The Structured Credit Handbook provides an explanation of investors’ nearly insatiable appetite for CDOs:

Demand for [fixed income] assets is heavily bifurcated, with the demand concentrated at the two ends of the safety spectrum . . . Prior to the securitization boom, the universe of fixed-income instruments issued tended to cluster around the BBB rating, offering neither complete safety nor sizzling returns. For example, the number of AA and AAA-rated companies is quite small, as is debt issuance of companies rated B or lower. Structured credit technology has evolved essentially in order to match investors’ demands with the available profile of fixed-income assets. By issuing CDOs from portfolios of bonds or loans rated A, BBB, or BB, financial intermediaries can create a larger pool of AAA-rated securities and a small unrated or low-rated bucket where almost all the risk is concentrated.

CDOs have been around for more than twenty years, but their popularity skyrocketed during the late 1990s. CDO issuance nearly doubled in 2005 and then again in 2006, when it topped $500 billion for the first time. “Structured finance” groups at large investment banks (the division responsible for issuing and managing CDOs) became one of the fastest-growing areas on Wall Street. These divisions, along with the investment banking trading desks that made markets in CDOs, contributed to highly successful results for the banking sector during the 2003–2007 boom. Many CDOs became quite liquid because of their size, investor breadth, and rating agency coverage.

Rating agencies helped bring liquidity to the CDO market. They analyzed each tranche of a CDO and assigned ratings accordingly. Equity tranches were often unrated. The rating agencies had limited manpower and needed to gauge the risk on literally thousands of new CDO securities. The agencies also specialized in using historical models to predict risk. Although CDOs had been around for a long time, they did not exist in a significant number until recently. Historical models therefore couldn’t possibly capture the full picture. Still, the underlying collateral could be assessed with a strong degree of confidence. After all, banks have been making home loans for hundreds of years. The rating agencies simply had to allocate risk to the appropriate tranche and understand how the loans in the collateral base were correlated with each other – an easy task in theory perhaps, but not in practice.

The most difficult part of valuing a CDO tranche is determining correlation. If loans are uncorrelated, defaults will occur evenly over time and asset diversification can solve most problems. With low correlation, an AAA-rated senior tranche should be safe and the interest rate attached to this tranche should be close to the rate for AAA-rated corporate bonds. High correlation, however, creates nondiversifiable risk, in which case the senior tranche has a reasonable likelihood of becoming impaired. Correlation does not affect the price of the CDO in total because the expected value of each individual loan remains the same. Correlation does, however, affect the relative price of each tranche: Any increase in the yield of a senior tranche (to compensate for additional correlation) will be offset by a decrease in the yield of the junior tranches.

Long Term Capital Management. Note Quote.

Long Term Capital Management, or LTCM, was a hedge fund founded in 1994 by John Meriwether, the former head of Salomon Brothers’s domestic fixed-income arbitrage group. Meriwether had grown the arbitrage group to become Salomon’s most profitable group by 1991, when it was revealed that one of the traders under his purview had astonishingly submitted a false bid in a U.S. Treasury bond auction. Despite reporting the trade immediately to CEO John Gutfreund, the outcry from the scandal forced Meriwether to resign.

Meriwether revived his career several years later with the founding of LTCM. Amidst the beginning of one of the greatest bull markets the global markets had ever seen, Meriwether assembled a team of some of the world’s most respected economic theorists to join other refugees from the arbitrage group at Salomon. The board of directors included Myron Scholes, a coauthor of the famous Black-Scholes formula used to price option contracts, and MIT Sloan professor Robert Merton, both of whom would later share the 1997 Nobel Prize for Economics. The firm’s impressive brain trust, collectively considered geniuses by most of the financial world, set out to raise a $1 billion fund by explaining to investors that their profoundly complex computer models allowed them to price securities according to risk more accurately than the rest of the market, in effect “vacuuming up nickels that others couldn’t see.”

One typical LTCM trade concerned the divergence in price between long-term U.S. Treasury bonds. Despite offering fundamentally the same (minimal) default risk, those issued more recently – known as “on-the-run” securities – traded more heavily than those “off-the-run” securities issued just months previously. Heavier trading meant greater liquidity, which in turn resulted in ever-so-slightly higher prices. As “on-the-run” securities become “off-the-run” upon the issuance of a new tranche of Treasury bonds, the price discrepancy generally disappears with time. LTCM sought to exploit that price convergence by shorting the more expensive “on-the-run” bond while purchasing the “off- the-run” security.

By early 1998 the intellectual firepower of its board members and the aggressive trading practices that had made the arbitrage group at Salomon so successful had allowed LTCM to flourish, growing its initial $1 billion of investor equity to $4.72 billion. However, the miniscule spreads earned on arbitrage trades could not provide the type of returns sought by hedge fund investors. In order to make transactions such as these worth their while, LTCM had to employ massive leverage in order to magnify its returns. Ultimately, the fund’s equity component sat atop more than $124.5 billion in borrowings for total assets of more than $129 billion. These borrowings were merely the tip of the ice-berg; LTCM also held off-balance-sheet derivative positions with a notional value of more than $1.25 trillion.

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The fund’s success began to pose its own problems. The market lacked sufficient capacity to absorb LTCM’s bloated size, as trades that had been profitable initially became impossible to conduct on a massive scale. Moreover, a flood of arbitrage imitators tightened the spreads on LTCM’s “bread-and-butter” trades even further. The pressure to continue delivering returns forced LTCM to find new arbitrage opportunities, and the fund diversified into areas where it could not pair its theoretical insights with trading experience. Soon LTCM had made large bets in Russia and in other emerging markets, on S&P futures, and in yield curve, junk bond, merger, and dual-listed securities arbitrage.

Combined with its style drift, the fund’s more than 26 leverage put LTCM in an increasingly precarious bubble, which was eventually burst by a combination of factors that forced the fund into a liquidity crisis. In contrast to Scholes’s comments about plucking invisible, riskless nickels from the sky, financial theorist Nassim Taleb later compared the fund’s aggressive risk taking to “picking up pennies in front of a steamroller,” a steamroller that finally came in the form of 1998’s market panic. The departure of frequent LTCM counterparty Salomon Brothers from the arbitrage market that summer put downward pressure on many of the fund’s positions, and Russia’s default on its government-issued bonds threw international credit markets into a downward spiral. Panicked investors around the globe demonstrated a “flight to quality,” selling the risky securities in which LTCM traded and purchasing U.S. Treasury securities, further driving up their price and preventing a price convergence upon which the fund had bet so heavily.

None of LTCM’s sophisticated theoretical models had contemplated such an internationally correlated credit market collapse, and the fund began hemorrhaging money, losing nearly 20% of its equity in May and June alone. Day after day, every market in which LTCM traded turned against it. Its powerless brain trust watched in horror as its equity shrank to $600 million in early September without any reduction in borrowing, resulting in an unfathomable 200 leverage ratio. Sensing the fund’s liquidity crunch, Bear Stearns refused to continue acting as a clearinghouse for the fund’s trades, throwing LTCM into a panic. Without the short-term credit that enabled its entire trading operations, the fund could not continue and its longer-term securities grew more illiquid by the day.

Obstinate in their refusal to unwind what they still considered profitable trades hammered by short-term market irrationality, LTCM’s partners refused a buyout offer of $250 million by Goldman Sachs, ING Barings, and Warren Buffet’s Berkshire Hathaway. However, LTCM’s role as a counterparty in thousands of derivatives trades that touched investment firms around the world threatened to provoke a wider collapse in international securities markets if the fund went under, so the U.S. Federal Reserve stepped in to maintain order. Wishing to avoid the precedent of a government bailout of a hedge fund and the moral hazard it could subsequently encourage, the Fed invited every major investment bank on Wall Street to an emergency meeting in New York and dictated the terms of the $3.625 billion bailout that would preserve market liquidity. The Fed convinced Bankers Trust, Barclays, Chase, Credit Suisse First Boston, Deutsche Bank, Goldman Sachs, Merrill Lynch, J.P. Morgan, Morgan Stanley, Salomon Smith Barney, and UBS – many of whom were investors in the fund – to contribute $300 million apiece, with $125 million coming from Société Générale and $100 million from Lehman Brothers and Paribas. Eventually the market crisis passed, and each bank managed to liquidate its position at a slight profit. Only one bank contacted by the Fed refused to join the syndicate and share the burden in the name of preserving market integrity.

That bank was Bear Stearns.

Bear’s dominant trading position in bonds and derivatives had won it the profitable business of acting as a settlement house for nearly all of LTCM’s trading in those markets. On September 22, 1998, just days before the Fed-organized bailout, Bear put the final nail in the LTCM coffin by calling in a short-term debt in the amount of $500 million in an attempt to limit its own exposure to the failing hedge fund, rendering it insolvent in the process. Ever the maverick in investment banking circles, Bear stubbornly refused to contribute to the eventual buyout, even in the face of a potentially apocalyptic market crash and despite the millions in profits it had earned as LTCM’s prime broker. In typical Bear fashion, James Cayne ignored the howls from other banks that failure to preserve confidence in the markets through a bailout would bring them all down in flames, famously growling through a chewed cigar as the Fed solicited contributions for the emergency financing, “Don’t go alphabetically if you want this to work.”

Market analysts were nearly unanimous in describing the lessons learned from LTCM’s implosion; in effect, the fund’s profound leverage had placed it in such a precarious position that it could not wait for its positions to turn profitable. While its trades were sound in principal, LTCM’s predicted price convergence was not realized until long after its equity had been wiped out completely. A less leveraged firm, they explained, might have realized lower profits than the 40% annual return LTCM had offered investors up until the 1998 crisis, but could have weathered the storm once the market turned against it. In the words of economist John Maynard Keynes, the market had remained irrational longer than LTCM could remain solvent. The crisis further illustrated the importance not merely of liquidity but of perception in the less regulated derivatives markets. Once LTCM’s ability to meet its obligations was called into question, its demise became inevitable, as it could no longer find counterparties with whom to trade and from whom it could borrow to continue operating.

The thornier question of the Fed’s role in bailing out an overly aggressive investment fund in the name of market stability remained unresolved, despite the Fed’s insistence on private funding for the actual buyout. Though impossible to foresee at the time, the issue would be revisited anew less than ten years later, and it would haunt Bear Stearns. With negative publicity from Bear’s $38.5 million settlement with the SEC regarding charges that it had ignored fraudulent behavior by a client for whom it cleared trades and LTCM’s collapse behind it, Bear Stearns continued to grow under Cayne’s leadership, with its stock price appreciating some 600% from his assumption of control in 1993 until 2008. However, a rapid-fire sequence of negative events began to unfurl in the summer of 2007 that would push Bear into a liquidity crunch eerily similar to the one that felled LTCM.

Financial Analysis of the Blue Economy: Sagarmala’s Case in Point

Let us begin with the question. Why is infrastructure even important? Extensive and efficient infrastructure is critical for ensuring the effective functioning of the economy, as it is an important factor determining the location of economic activity and the kinds of activities or sectors that can develop in a particular economy. Well-developed infrastructure reduces the effect of distance between regions, integrating the national market and connecting it at low cost to markets in other countries and regions. In addition, the quality and extensiveness of infrastructure networks significantly impact economic growth and affect income inequalities and poverty in a variety of ways. A well- developed transport and communications infrastructure network is a prerequisite for the access of less-developed communities to core economic activities and services. Effective modes of transport, including quality roads, railroads, ports, and air transport, enable entrepreneurs to get their goods and services to market in a secure and timely manner and facilitate the movement of workers to the most suitable jobs. Economies also depend on electricity supplies that are free of interruptions and shortages so that businesses and factories can work unimpeded. Finally, a solid and extensive communications network allows for a rapid and free flow of information, which increases overall economic efficiency by helping to ensure that businesses can communicate and decisions are made by economic actors taking into account all available relevant information. There is an existing correlation between infrastructure and economic activity through which the economic effects originate in the construction phase and rise during the usage phase. The construction phase is associated with the short-term effects and are a consequence of the decisions in the public sector that could affect macroeconomic variables: GDP, employment, public deficit, inflation, among others. The public investment expands the aggregate demand, yielding a boost to the employment, production and income. The macroeconomic effects at a medium and long term, associated with the utilization phase are related to the increase of productivity in the private sector and its effects over the territory. Both influence significantly in the competitiveness degree of the economy. In conclusion, investing in infrastructure constitutes one of the main mechanisms to increase income, employment, productivity and consequently, the competitiveness of an economy. Is this so? Well, thats what the economics textbook teaches us, and thus governments all over the world turn to infrastructure development as a lubricant to maintain current economic output at best and it can also be the basis for better industry which contributes to better economic output. So far, so good, but then, so what? This is where social analysts need to be incisive in unearthing facts from fiction and this faction is what constitutes the critique of development, a critique that is engineered against a foci on GDP-led growth model.

Rewinding back to earlier this year in April, when the occasion was the inauguration of the 2nd annual meeting of New Development Bank (NDB) by the five member BRICS (Brazil-Russia-India-China-South Africa) countries in New Delhi, Finance Minister Arun Jaitley stressed that India has a huge unmet need for investment in infrastructure, estimated to the tune of Rs 43 trillion or about $646 billion over the next five years, 70% of which will be required in the power, roads and urban infrastructure sectors. He reiterated that in emerging markets and developing economies (EMDEs), the overall growth is picking up, although growth prospects diverge across countries. Further,

But there are newer challenges, most notably a possible shift towards inward-looking policy platforms and protectionism, a sharper than expected tightening in global financial conditions that could interact with balance sheet weaknesses in parts of the euro area and increased geopolitical tensions, including unpredictable economic policy of USA. Most importantly, the EMDEs need to carry out this huge investment in a sustainable manner. The established Multi-lateral Development Banks are now capital constrained, and with their over emphasis on processes, are unable to meet this financing challenge. We shall work with the NDB to develop a strong shelf of projects in specific areas such as Smart Cities, renewable energy, urban transport, including Metro Railways, clean coal technology, solid waste management and urban water supply.

This is the quote that reflects the policy-direction of the Government at the centre. Just a month prior to Jaitley’s address, it was the Prime Minister Narendra Modi, who instantiated the need for overhauling the infrastructure in a manner hitherto not conceived of, even though policies for such an overhauling were doing the rounds in the pipeline ever since he was elected to the position in May 2014. Modi emphasized that the Government would usher in a ‘Blue Revolution’ by developing India’s coastal regions and working for the welfare of fishing communities in a string of infrastructure projects. that such a declaration came in the pilgrim town of Somnath in Gujarat isn’t surprising, for the foundations of a smart city spread over an area of about 1400 acres was laid at Kandla, the port city. The figures he cited during his address were all the more staggering making one wonder about the source of resources. For instance, the smart city would provide employment to about 50000 people. The Blue Revolution would be initiated through the Government’s flagship Sagarmala Project attracting an investment to the tune of Rs. 8 lakh crore and creating industrial and tourism development along the coast line of the entire country. Not just content with such figures already, he also promised that 400 ports and fishing sites would be developed under the project, of which the state of Gujarat along would account for 40 port projects with an investment of about Rs. 45000 crore.

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The Government, moreover plans to help the fishermen buy fishing boats at 50% subsidized rates, where five poor fishermen could form a cooperative and avail 50% subsidy and Rs 1 crore loan from Mudra scheme1. Fishermen can buy a fishing trawler with cold storage facility and increase their income (emphasis mine).

One would obviously wonder at how tall are these claims? Clearly Modi and his cohorts are no fan of Schumacher’s “Small is Beautiful” due to their obsession with “Bigger is Better”. What’s even more surprising is that these reckless followers of capitalism haven’t even understood what is meant by “Creative Destruction” both macro- or micro- economically. The process of Joseph Schumpeter’s creative destruction (restructuring) permeates major aspects of macroeconomic performance, not only long-run growth but also economic fluctuations, structural adjustment and the functioning of factor markets. At the microeconomic level, restructuring is characterized by countless decisions to create and destroy production arrangements. These decisions are often complex, involving multiple parties as well as strategic and technological considerations. The efficiency of those decisions not only depends on managerial talent but also hinges on the existence of sound institutions that provide a proper transactional framework. Failure along this dimension can have severe macroeconomic consequences once it interacts with the process of creative destruction. Quite unfortunately, India is heading towards an economic mess, if such policies are to slammed onto people under circumstances when neither the macroeconomic not the microeconomic apparatuses in the country are in shape to withstand cyclonic shocks. Moreover, these promotional doctrines come at a humungous price of gross violations of human and constitutional rights of the people lending credibility once again to the warnings of Schumacher’s Small is Beautiful: A Study of Economics as if People Mattered (emphasis mine). After this pretty long sneak peek via introduction, let us turn to Blue Economy/Blue Revolution.

Blue Economy or Blue Revolution?

What exactly do these terms mean? What is the difference between the ocean economy and the blue or sustainable ocean economy? Is it simply that a sustainable ocean economy is one where the environmental risks of, and ecological damage from, economic activity are mitigated, or significantly reduced? Is it enough that future economic activity minimizes harm to the ocean, or rather, should aim to restore its health? These are pressing questions, and thus a working definition of what blue Economy is, or rather more aptly how Blue Economy is conceived the world over is an imperative.

A sustainable ocean economy emerges when economic activity is in balance with the long-term capacity of ocean ecosystems to support this activity and remain resilient and healthy.

The ocean is becoming a new focal point in the discourse on growth and sustainable development, both at national and international levels. The world is in many ways at a turning point in setting its economic priorities in the ocean. How this is done in the next years and decades, in a period when human activities in the ocean are expected to accelerate significantly, will be a key determinant of the ocean’s health and of the long- term benefits derived by all from healthy ocean ecosystems. The idea of the “blue economy” or “blue growth” has become synonymous with the “greening” of the ocean economy, and the frame by which governments, NGOs and others refer to a more sustainable ocean economy – one, broadly, where there is a better alignment between economic growth and the health of the ocean. Increasingly, national ocean development strategies reference the blue economy as a guiding principle, while policy-makers busy themselves filling in the gaps. These gaps are very considerable. Stimulating growth in the ocean economy could be comparatively straightforward; but what is not always clear is what a sustainable ocean economy should look like, and under what conditions it is most likely to develop. It is at this point where the Government and the communities dependent on oceans for life and livelihoods come to friction, and most of the time, it is the communities which find themselves at the receiving end for whenever policies pertaining to oceans and economies thereof are blue-printed, these communities seldom have a representation, or a representational voice. As could be made amply clear from the above description of what Blue Economy entails, it is the financialization of and economics of the ocean, that gets the prerogative over pretty much everything else. Indian schema on the Blue Economy is no different, and in fact it takes the theory into a derailed practice with Sagarmala Project.

What then is Blue Revolution? As has been the customary practice of the Government of India in changing names, this ‘Revolution’ too has come in to substitute ‘Economy’ in Blue Economy. This runs concomitantly with the major principles of Blue Economy in recognizing the potential and possibilities of the fisheries sector by unlocking the country’s latent potential through an integrated approach. In the words of the Government2, the Blue Revolution, in its scope and reach, focuses on creating an enabling environment for an integrated and holistic development and management of fisheries for the socio economic development of the fishers and fish farmers. Thrust areas have been identified for enhancing fisheries production from 10.79 mmt (2014-15) to 15 mmt in 2020-21. Greater emphasis will be on infrastructure with an equally strong focus on management and conservation of the resources through technology transfer to increase in the income of the fishers and fish farmers. Productivity enhancement shall also be achieved through employing the best global innovations and integration of various production oriented activities such as: Production of quality fish seeds, Cost effective feed and adoption of technology etc.

The restructured Plan Scheme on Blue Revolution3 – Integrated Development and Management of Fisheries has been approved at a total central outlay of Rs 3000 crore for implementation during a period of five years (2015-16 to 2019-20)4. The Ministry of Agriculture and Farmers Welfare, Department of Animal Husbandry, Dairying & Fisheries has restructured the scheme by merging all the ongoing schemes under an umbrella of Blue Revolution. The restructured scheme provides focused development and management of fisheries, covering inland fisheries, aquaculture, marine fisheries including deep sea fishing, mariculture and all activities undertaken by the National Fisheries Development Board (NFDB).

The Blue Revolution scheme has the following components:

  1. 1  National Fisheries Development Board (NFDB) and its activities
  2. Development of Inland Fisheries and Aquaculture
  3. Development of Marine Fisheries, Infrastructure and Post-Harvest Operations
  4. Strengthening of Database & Geographical Information System of the Fisheries Sector
  5. Institutional Arrangement for Fisheries Sector
  6. Monitoring, Control and Surveillance (MCS) and other need-based Interventions
  7. National Scheme of Welfare of Fishermen

One cannot fail to but notice that this schema is a gargantuan one, and the haunting accompaniment in the form of resources to bring this to effectuation would form the central theme of this paper. Though, there is enough central assistance, the question remains: where would these resources be raised/generated? Broad patterns of Central funding for new projects broadly fall under four components,

  1. National Fisheries Development Board (NFDB) and its activities,
  2. Development of Inland Fisheries and Aquaculture,
  3. Development of Marine Fisheries, Infrastructure and Post- Harvest Operations and
  4. National Scheme of Welfare of Fishermen are as below:
  • 50% of the project/unit cost for general States, leaving the rest to State agencies/ organizations, corporations, federations, boards, Fishers cooperatives, private entrepreneurs, individual beneficiaries.
  • 80% of the project/unit cost for North-Eastern/Hilly States leaving the rest to State agencies/Organizations, Cooperatives, individual beneficiaries etc.

  • 100% for projects directly implemented by the Government of India through its institutes/organizations and Union Territories.

Projects under the remaining three components scheme namely (i) Strengthening of Database & Geographical Information System of the Fisheries Sector, (ii) Institutional Arrangement for the Fisheries Sector and (iii) Monitoring, Control and Surveillance (MCS) and other need-based interventions shall be implemented with 100% central funding. Individual beneficiaries, entrepreneurs and cooperatives/collectives of the Union Territories shall also be provided Central financial assistance at par and equal to such beneficiaries in General States. As far as the implementing agencies are concerned, such wouldn’t really be an eye-opener.

  • Central Government, Central Government Institutes/Agencies, NFDB, ICAR Institutes etc.
  • State Governments and Union Territories
  • State Government Agencies, Organizations, Corporations, Federations, Boards, Panchayats and Local Urban Bodies
  • Fishers Cooperatives/Registered Fishers Bodies
  • Individual beneficiaries/fishers, Entrepreneurs, Scheduled Castes(SCs), Scheduled Tribes (STs) Groups, Women and their Co-operatives, SHG’s and Fish Farmers and miscellaneous Fishermen Bodies.

But, there is more than meets the eye here, for Blue Revolution isn’t all about fisheries, despite having some irreparable damages caused to the fisher community inhabiting the coastline for centuries. This revolution is purported to usher in industrialization, tourism and eventually growth through a criss-cross of industrial corridors, port up gradations and connectivities, raw material landing zones, coastal economic zones through what the adversaries of Blue Economy/Revolution refer to as Ocean Grabbing.5 In order to understand the implications of what ‘Ocean Grabbing’ refers to, one must look at it in tandem with Sagarmala Project, the flagship project of the Government of India, and a case study of instantiation of Blue Economy/Revolution. Here is where we turn to in the next section.

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Sagarmala

The contours of Sagarmala Project were laid out in the April 2016 perspective plan of the Ministry of Shipping. The plan involves a four-pronged approach that includes port modernization, port connectivity and port-led industrialization. It identifies Coastal Economic Zones (CEZ) and industrial clusters to be developed around port facilities mirroring the Chinese or European port infrastructure. The ambitious programmes spread across 14 ports is aimed to make domestic manufacturing and EXIM sector more competitive.

The project falls in line with Blue Revolution’s coastal community development. By “improving and matching the skills” of coastal communities, the plan seeks to ensure “sustainable development”. The plan seeks to improve the lives of coastal communities, implying that there is no contradiction between these objectives of port-led development and that of enhancing the lives of coastal residents. This seemingly win-win agenda is also endorsed by NITI Aayog’s mapping of schemes that are to help India achieve its Sustainable Development Goals (SDGs). Sagarmala is one of the ways Goal 14 will be met by 2030. Goal 14 is to conserve and sustainably use oceans, seas and marine resources.

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The Kutch coast of Gujarat tops the list of potential coastal economic zones (CEZs)6 in the 2016 perspective plan. The region is not unaccustomed to the consequences of this vision of port-centred development. The environmental impact assessment (EIA) for a large port development project in Mundra, Kutch, described the lands on which the SEZ was to be set up as “non-agricultural, waste, barren or weed infested land.” But that was far from the truth. These statements have been contested by local residents through endless administrative complaints and court cases. The litigation challenging projects in coastal Gujarat have brought up elaborate arguments regarding the complex web of valuable land uses that were blotted out to make this transformation possible.

Notwithstanding the contestations over such plans for coastal land use transformation in several regions like in Kutch, the Sagarmala plan document lays out its goals as if the coast has been an empty or unproductive space, and is now poised to be a “gateway” to growth. India’s coastline currently has about 3200 marine fishing villages. Nearly half of this population (over 1.6 million people) is engaged in active fishing and fishery-related activities. While such statistics may be quoted in the plan, the official proposal views these as mere numbers or as a population that will simply toe the line and play the role assigned to them in the plan.

Port expansions involve massive dredging into the sea that destroys large stretches of fertile fishing grounds and destabilizes jetties. Fishing associations bring out a range of concerns.7 Over the years there is reduced parking space for small artisanal boats, curtailed access to fishing harbours, and unpredictable fish catch. These changes keep them in a state of permanent anxiety or turn them into cheap industrial and cargo handling labour. These families also suffer the impacts of living next to mineral handling facilities and groundwater exhaustion. India has had laws to regulate environmental impacts, but these have been mostly on paper. So, the Minister for Road Transport & Highways, Shipping and Water Resources, River Development & Ganga Rejuvenation, Nitin Gadkari’s assurances that all air and water pollution norms will be met in the implementation of the Sagarmala plan may not cut any ice with coastal dwellers. Can India afford such an imagination of ‘frictionless development’? After all, we don’t yet have a Chinese model of governance.8 Running after the Chinese model of development9 is a still a dream in the corridors of power in New Delhi.

Late last year, the Minister Nitin Gadkari exuded confidence in promoting the port-led development by confidently asserting that the project would fetch 10-15 lakh crore capital investments, generate direct and indirect employment for around two crore people and provide a huge fillip to the country’s economic growth. Gadkari, when he inaugurated the Sagarmala Development Company, a unit under his ministry, and which would act as a nodal agency for the Project said Rs 8-lakh crore investment is expected as industrial investment while an additional Rs 4 lakh crore might go into port-led connectivity. It must be noted that Sagarmala Development Company10 has Rs. 1000 crore initial-authorized capital and is registered under the Companies Act, 2013. That the Government is going ahead full steam with the implementation of the Project, howsoever ill-conceived and fuzzy it might be could be gauged by the fact that a significant portion of the Project needs to be underway till the General elections of 2019. Gadkari said that projects worth Rs 1 lakh crore under the Sagarmala programme are already under various stages of implementation and by the completion of the present dispensation’s current tenure in 2019, projects worth Rs 5 lakh crore are expected to commence. He further stressed that a national perspective plan under the Sagarmala project has been prepared and projects worth Rs 8 lakh crore have been identified. That there is a lack of transparency in garnering these funds in the public domain could easily be decipherable from what Nitin Gadkari said,

I don’t have any problem with financial resources. We have already appointed an agency to help us raise funds.

What this agency is is anyone’s guess, or maybe locating the coordinates of it is as hard as finding a needle in the haystack. But, probably, here is the clue. In order to have effective mechanism at the state level for coordinating and facilitating Sagarmala related projects, the State Governments will be suggested to set up State Sagarmala Committee to be headed by Chief Minister/Minister in Charge of Ports with members from relevant Departments and agencies. The state level Committee will also take up matters on priority as decided in the National Sagarmala Apex Committee (NSAC)11. At the state level, the State Maritime Boards/State Port Departments shall service the State Sagarmala Committee and also be, inter alia, responsible for coordination and implementation of individual projects, including through Special Purpose Vehicles (SPVs) (as may be necessary) and oversight. The development of each Coastal economic zone shall be done through individual projects and supporting activities that will be undertaken by the State Government, Central line Ministries and SPVs to be formed by the State Governments at the state level or by Sagarmala Development Company (SDC) and ports, as may be necessary.

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Before getting into the financial ecosystem of Sagarmala, it is necessary to wrap this brief section on introducing Sagarmala with a list of the kinds of development projects envisaged in the initiative. This is also kind of summarizes the Blue Economy/ Revolution, Sagarmala Project and the hunger for infrastructural development by instances: (i) Port-led industrialization (ii) Port based urbanization (iii) Port based and coastal tourism and recreational activities (iv) Short-sea shipping coastal shipping and Inland Waterways Transportation (v) Ship building, ship repair and ship recycling (vi) Logistics parks, warehousing, maritime zones/services (vii) Integration with hinterland hubs (viii) Offshore storage, drilling platforms (ix) Specialization of ports in certain economic activities such as energy, containers, chemicals, coal, agro products, etc. (x) Offshore Renewable Energy Projects with base ports for installations (xi) Modernizing the existing ports and development of new ports. This strategy incorporates both aspects of port-led development viz. port-led direct development and port-led indirect development.

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Table12

What does the Government want to make us believe about this ambitious Project, i.e. raison d’être for undertaking this? The growth of India’s maritime sector13 is constrained due to many developmental, procedural and policy related challenges namely, involvement of multiple agencies in development of infrastructure to promote industrialization, trade, tourism and transportation; presence of a dual institutional structure that has led to development of major and non-major ports as separate, unconnected entities; lack of requisite infrastructure for evacuation from major and non- major ports leading to sub-optimal transport modal mix; limited hinterland linkages that increases the cost of transportation and cargo movement; limited development of centers for manufacturing and urban and economic activities in the hinterland; low penetration of coastal and inland shipping in India, limited mechanization and procedural bottlenecks and lack of scale, deep draft and other facilities at various ports in India.

The Financial Ecosystem of Blue Revolution/Economy and Sagarmala Project

Let us begin with a shocker. In the words of Nitin Gadkari14,

With bank credit drying up for large infrastructure projects, the National Democratic Alliance (NDA) government is exploring a plan to raise Rs 10 trillion from retirees and provident fund beneficiaries15.

The plan aims to raise money in tranches of Rs10,000 crore by selling 10-year bonds at a coupon of 7.25-7.75%. Each tranche will be meant for a specific project. India plans to invest as much as Rs 3.96 trillion in the current financial year to bankroll its new integrated infrastructure programme which involves building of roads, railways, waterways and airports.16 It needs to be recalled that in 2015, the Government set up the National Investment and Infrastructure fund (NIIF) to raise funds for the infrastructure sector with an initial targeted corpus of Rs 40,000 crore, of which Rs 20,000 crore was to be invested by the government. The remaining Rs 20,000 crore was to be raised from long-term international investors, including sovereign wealth funds, insurance and pension funds and endowments.

Using this option is a risky affair, not that it hasn’t been used elsewhere and for a time now, but it still retains the element of risk. The risk factor is cut if the interest rates are high, for then these retiree (pension17) and provident funds are used to buy bonds that would mature when there is a need to payout. But as interest rates fall, which is very much the case with India at the moment with fluctuations and a dip in growth post-demonetization and the banking system under stress due to NPAs, these funds are likely to face up with a dilemma. Staying heavily invested in bonds would force the Government to either set aside more cash upfront or to cut promises pertaining to retiree (pension) and provident funds. If this is the two-pronged strategy of the government on one hand, then on the other, these funds, if they are allowed to raise capital from the international markets (which, incidentally is cashing and catching up in India) radically change their investment strategies by embracing investments that produce higher returns, but are staring at more risks associated with the market. Though, this shift is in line with neoliberal market policies, this has replaced an explicit cost with a hidden one, in that the policy-makers would have to channel more capital into these funds, cut back benefits or both when the stock market crashes18 causing the asset value of these funds to decline.

A project of this magnitude is generally financed using the instrument of Project Finance. So, it becomes necessary to throw light on what exactly is entailed by the term. Project Finance is looked upon as the most viable form of financing that there is with highly mitigated levels of risks, at least, according to the financial worldlings! Although, there are difficulties and challenges/needs/necessities (in short applied/application), these need to be delineated. Additionally, a study on Project Finance leads inherently to a study on Public Private Partnerships (PPPs), another preferred mode in use in India at present. One of the fundamental trade-offs for PPPs designing is to strike a right balance between risks allocations between the public and private sector, risk allocation within the private sector and cost of funding for the PPP company. This again has potentials for points of conflict with specially designed Special Purpose Vehicles (SPVs) out there to bend inclinations due to lack of disclosure clauses that define Project Finance in the first place. The factorization of PPPs and SPVs is often channeled through what is currently gaining currency the world over: Financial Intermediaries (FIs). Let us tackle these one by one in order to know the financial ecosystem that would be helpful in tracking funds and investments flowing through into the Blue Revolution/Economy and Sagarmala Project.

A project is characterized by major productive capital investment. Now, there are some asymmetric downside risks associated with a project in addition to the usual symmetric and binary ones. These asymmetric risks are environmental risks and a possibility of creeping expropriation (due to the project). Demand, price; input/supply are symmetric risks in nature, while technological glitch and regulatory fluctuations are binary risks. All that a project is on the lookout for is a customized capital structure, and governance to minimize cash inflow/outflow volatility. Project finance aims to precisely do that. It involves a corporate sponsor investing through a non-recourse debt. It is characterized by cash flows, high debt leading to a need for additional support, bank guarantees, and letters of credit to cover greater risks during construction, implementation (commissioning as the context maybe), and at times sustainability. Now funding is routed through various sources, viz. export credits, development funds, specialized assets financing, conventional debt and equity finance. This is archetypal of how the corporate financial structure operates as far as managing risks is concerned from the point of view of future inflow of funds. It has a high concentration of equity and debt ownership, with up to three equity sponsors, syndicate of banks and financial institutions to provide for credit. Moreover, there is an extremely high level of debt with the balance of capital provided by the sponsors in the form of equity, while importantly, the debt is non- recourse to the sponsors.

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The attractiveness of project finance is the ability to fund projects off balance sheet with limited or no recourse to equity investors i.e. if a project fails, the project lenders recourse is to ownership of actual project and they are unable to pursue the equity investors for debt. For this reason lenders focus on the project cash-flow as this the main sources for repaying project debt. The shareholders will invest in the SPV with a focus to minimize their equity contributions, since equity commands a higher rate of return, and thus is a more risky affair compared with a conventional commercial bank debt. Whereas, the bank lenders will always seek a comfortable level of equity from shareholders of SPV to ensure that the project sponsors are seriously committed to the project and have a vested interest in seeing the project succeed.19

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The figure above delineates what Project Finance could do advantageously, but at the same time is a sneak peek into what the disadvantages are.

  1. Project Finance mandates greater disclosure of information on deals and contracts, which happen to be proprietary in nature.
  2. Extensive contracting restricts management decision-making, by looping it into complexities, where decisions making nodes are difficult to make.
  3. Project debt is more expensive.

To turn around the disadvantages of PPPs model, an SPV is introduced. SPV is generally taken as a concessionary authority, where the concession authority is the government itself, and grants a concession to the SPV, a license granting it exclusive ownership of a facility, which, once the term for the license is over is transferred back to the government, or any other public authority. The concession forms the contract between the government and SPV and goes under the name of project agreement. Things begin to get a bit murky here, for the readers be forewarned that this applicability is becoming a commonality in the manner in which infrastructure projects are funded nowadays. Let us try and extricate the knots here.

Consider a Rs. 100 crore collection of risky loan, obligations of borrowers who have promised to repay their loans at some point in future. Let us imagine them sitting on the balance sheet of some bank XYZ, but they equally well could be securities available on the market that the Bank’s traders want to purchase and repackage for a profit. No one knows whether the borrowers will repay, so a price is put on this uncertainty by the market, where thousands of investors mull over the choice of betting on these risky loans and the certainty of risk-free government bonds. To make them indifferent to the uncertainty these loans carry, potential investors require a bribe in the form of 20% discount at face value. If none of the loans default, investors stand a chance to earn a 25% return. A good deal for investors, but a bad one for the Bank, which does not want to sell the loans for a 20% discount and thereby report a loss.

Now imagine that instead of selling the loans at their market price of Rs. 80 crore, the Bank sells them to an Special Purpose Vehicle (SPV) that pays a face value of Rs. 100 crore. Their 20% loss just disappeared. Ain’t this a miracle? But, how? The SPV has to raise Rs. 100 crore in order to buy the loans from the Bank. Lenders in SPV will only want to put Rs. 80 crore against such risky collateral. The shortfall of Rs. 20 crore will have to be made up somehow. The Bank enters here under a different garb. It puts in Rs. 20 crore as an equity investment so that the SPV has enough money now to buy the Rs. 100 crore of loans. However, there is a catch here. Lenders no longer expect to receive Rs. 100 crore, or a 25% return in compensation for putting up the Rs. 80 crore. SPV’s payout structure guarantees that the Rs. 20 crore difference between face value and market value will be absorbed by the Bank, implying treating Rs. 80 crore investment as virtually risk-free. Even though the Bank has to plough Rs. 20 crore back into the SPV as a kind of hostage against the loans going bad, from Bank’s perspective, this might be better than selling the loans at an outright Rs. 20 crore loss. This deal reconciles two opposing views, the first one being the market suspicion that those Bank assets are somehow toxic, and secondly that the Bank’s faith that its loans will eventually pay something close to their face value. So, SPVs become a joint creation of equity owners and lenders, purely for the purpose of buying and owning assets, where the lenders advance cash to the SPV in return for bonds and IOUs, while equity holders are anointed managers to look after those assets. Assets, when parked safely within the SPV cannot be redeployed as collateral even in the midst of irresponsible buying spree.

Now, this technically might spell out the reasons for why an SPV is even required in the first place. But, enter caveat, for the architecture of an SPV is what lends complexity and a degree of murkiness to it. If one were to look at the architecture of SPV holdings, things get a bit muddled in that not only is the SPV a limited company registered under the Companies Act 2013, the promotion of SPV would lie chiefly with the state/union territory and elected Urban Local Body (ULB) on a 50:50 equity holding. The state/UT and ULB have full onus to call upon private players as part of the equity, but with the stringent condition that the share of state/UT and ULB would always remain equal and upon addition be in majority of 50%. So, with permutations and combinations, it is deduced that the maximum share a private player can have will be 48% with the state/UT and ULB having 26% each. Initially, to ensure a minimum capital base for the SPV, the paid up capital of the SPV should be such that the ULB’s share has an option to increase it to the full amount of the first installment provided by the Government of India. There is more than meets the eye here, since a major component is the equity shareholding, and from here on things begin to get complex. This is also the stage where SPV gets down to fulfilling its responsibilities and where the role of elected representatives of the people, either at the state/UT level or at the ULB level appears to get hazy. Why is this so? The Board of the SPV, despite having these elected representatives has in no certain ways any clarity on the decisions of those represented making a strong mark when the SPV gets to apply its responsibilities. SPVs, now armed with finances can take on board consultative expertise from the market, thus taking on the role befitting their installation in the first place, i.e. going along the privatization of services in tune with the market-oriented neoliberal policies. Such an arrangement is essentially dressing up the Economic Zones in new clothes sewn with tax exemptions, duties and stringent labour laws in bringing forth the most dangerous aspect of Blue Revolution/Economy/Sagarmala Project, viz. privatized governance. In short, this is how armed with finances, the doctrine of privatized governance could be realized, and SPV actually becomes the essence to attain it.

Turning our focus to what is often termed the glue in Project Finance, the instrument widely practiced today and what often joins PPPs and SPVs into a node of financing, Financial Intermediaries. Although, very much susceptible to abuse for the way it has been implemented, these are institutions that provide the market function of matching borrowers and lenders or traders. Financial intermediaries facilitate transactions between those with excess cash in relation to current requirements (suppliers of capital) and those with insufficient cash in relation to current requirements (users of capital) for mutual benefit. Now these take on astronomical importance considering that almost every other Non-Banking Financial Institution or an SPV could potentially be a financial intermediary. For example, insurance companies, credit unions, financial advisors, mutual funds and investment trusts are financial intermediaries. Financial intermediaries are able to transform the risk characteristics of assets because they can overcome a market failure and resolve an information asymmetry problem. Information asymmetry in credit markets arises because borrowers generally know more about their investment projects than lenders do. The information asymmetry can occur “ex ante” or “ex post”. An ex ante information asymmetry arises when lenders cannot differentiate between borrowers with different credit risks before providing loans and leads to an adverse selection problem. Adverse selection problems arise when an increase in interest rates leaves a more risky pool of borrowers in the market for funds. Financial intermediaries are then more likely to be lending to high-risk borrowers, because those who are willing to pay high interest rates will, on average, be worse risks. The information asymmetry problem occurs ex post when only borrowers, but not lenders, can observe actual returns after project completion. This leads to a moral hazard problem. Moral hazard arises when a borrower engages in activities that reduce the likelihood of a loan being repaid. An example of moral hazard is when firms’ owners “siphon off” funds (legally or illegally) to themselves or to associates, for example, through loss-making contracts signed with associated firms.

The problem with imperfect information is that information is a “public good”. If costly privately-produced information can subsequently be used at less cost by other agents, there will be inadequate motivation to invest in the publicly optimal quantity of information. The implication for financial intermediaries is as follows. Once banks obtain information they must be able to signal their information advantage to lenders without giving away their information advantage. One reason, financial intermediaries can obtain information at a lower cost than individual lenders is that financial intermediation avoids duplication of the production of information. Moreover, there are increasing returns to scale to financial intermediation. Financial intermediaries develop special skills in evaluating prospective borrowers and investment projects. They can also exploit cross- sectional (across customers) information and re-use information over time. Adverse selection increases the likelihood that loans will be made to bad credit risks, while moral hazard lowers the probability that a loan will be repaid. As a result, lenders may decide in some circumstances that they would rather not make a loan and credit rationing may occur. There are two forms of credit rationing: (i) some loan applicants may receive a smaller loan than they applied for at the given interest rate, or (ii) they may not receive a loan at all, even if they offered to pay a higher interest rate.

In other words, financial intermediaries play an important role in credit markets because they reduce the cost of channelling funds between relatively uninformed depositors to uses that are information-intensive and difficult to evaluate, leading to a more efficient allocation of resources. Intermediaries specialize in collecting information, evaluating projects, monitoring borrowers’ performance and risk sharing. Despite this specialization20, the existence of financial intermediaries does not replicate the credit market outcomes that would occur under a full information environment. The existence of imperfect, asymmetrically-held information causes frictions in the credit market. Changes to the information structure and to variables which may be used to overcome credit frictions (such as firm collateral and equity) will in turn cause the nature and degree of credit imperfections to alter. Banks and other intermediaries are “special” where they provide credit to borrowers on terms which those borrowers would not otherwise be able to obtain. Because of the existence of economies of scale21 in loan markets, small firms in particular may have difficulties obtaining funding from non-bank sources and so are more reliant on bank lending than are other firms. Adverse shocks to the information structure, or to these firms’ collateral or equity levels, or to banks’ ability to lend, may all impact on firms’ access to credit and hence to investment and output.

This section started with a shocker, and then weaved the plot generically in a deliberate manner to highlight the financial instruments in use for funding the massive Blue Revolution/Economy and Sagarmala Project. From the generic sense, it is time to move on to the specifics, where in the next section, one could easily fathom the generic nature of these instruments in use. this section was indeed technical, but a major collaborator to understanding the contours of Project Finance, and how is it that such instruments govern the polity and the policy of the ruling dispensation. Let us therefore, turn to what engineers the funding of this infrastructural giant.

Engineering Finance for Blue Revolution/Economy and Sagarmala Project

To reiterate, at the Central level, the Sagarmala Development Company (SDC) has been set up under the Companies Act, 2013 to assist the State level/zone level Special Purpose Vehicles (SPVS), as well as SPVs to be set up by the ports, with equity support for implementation of projects under Sagarmala to be undertaken by them. The formation of SDC was approved by the Cabinet on 20th July 2016 and was incorporated on 31st August 2016. It may be clarified that the implementation of the projects shall be done by the Central Line Ministries, State Governments/State Maritime Boards and SPVs and the SDC will provide a funding window and/or take up only those residual projects that cannot be funded by any other means/mode. The SDC will primarily provide equity support to the State-level or port-level SPV. All efforts would be made to implement these projects through the private sector and through the Public Private Participation (PPP) wherever feasible, strictly following the established guidelines and modality of appraisal and sanction of PPP projects. Projects in which SDC will take an equity stake, are expected to start giving returns only after 5-6 years. Therefore, SDC will be supported during initial 5-6 years through budgetary allocation of Ministry of Shipping. SDC will also be raising funds as debt/equity (as long term capital) from multi-lateral and bilateral funding agencies, as per the requirements, in consultation with Department of Economic Affairs. The SPVs in which SDCL will invest may start giving dividends once they become profitable and will constitute a revenue stream. The expenses incurred for project development will be treated as part of the equity contribution of SDC. In case SDCL is not taking any stake or the expenses incurred are more than the stake of SDC, then it will be defrayed by the SPV to SDC. SDC may, in future, want to divest its investment in any particular SPV to recoup its capital for future projects. At the State level, the State Maritime Boards/State Port Departments shall service the State Sagarmala Committees and also be, inter alia responsible for coordination and implementation of individual projects, including through SPVs and oversight. The State Governments/State Maritime  Boards (SMBs) shall implement such identified projects either from their own budgets or through SPVs wherein the SDC may provide equity support, as may be required and necessary. Funds will be sought for the implementation of residual projects from time to time in the budgets of the respective ministries/departments which will be implementing the projects. The Ministries/State Governments/Maritime Boards shall implement such identified either from their own budgets or through SPVs wherein the SDC may provide equity support, as may be required and necessary. Projects considered for funding (other than equity support) under Sagarmala’s budget shall be appraised and approved under the extant instructions and guidelines of the Ministry of Finance. Road and rail connectivity projects, already appraised and approved by the Ministry of Road Transport & Highways and Ministry of Railways respectively, will be considered as appraised projects. A representative of Ministry of Shipping could be a member of the project appraisal committee, set up by the relevant Ministries. Projects considered for equity support under Sagarmala and to be financed by SDC, will be independently appraised and approved by SDC as per its procedure. One can see that even if the title of the section is engineering, it is actually the architecture of who gets to fund what that is slowly building up the complexity of Sagarmala. Let us take a brief look at the numbers before getting back to engineering of funding.

As of March 2017, under Sagarmala, 415 projects, at an estimated investment of approximately Rs. 8 lakh crore, have already been identified across port modernization & new port development, port connectivity enhancement, port-linked industrialization and coastal community development for phase wise implementation over the period 2015 to 2035. As per the approved implementation plan of Sagarmala, these projects are to be taken up by the relevant Central Ministries/Agencies and State Governments preferably through private/PPP mode.

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Some of the key projects are:

  • Rs. 58.5 Crore released for capital dredging for Gogha-Dahej RO-Pax Ferry Services project
  • Rs. 50 Crore released for construction of RoB cum Flyover at Ranichak level crossing at Kolkata Port
  • Rs. 43.76 Crore released for RO-RO Services Project at Mandwa
  • Rs. 20 Crore released for setting up second rail line from Take-off Point A cabin atDurgachak (Haldia Dock Complex)
  • Rs. 20 Crore released for Vizag Port road connectivity to NH5
  • Rs. 10 Crore released for development of a full-fledged Truck Parking Terminal adjacent to NH7A (VOCPT)

As part of the Sagarmala Programme, 6 new port locations have been identified, namely – Vadhavan, Enayam, Sagar Island, Paradip Outer Harbour, Sirkazhi and Belekeri. The current status of each of the proposed new port locations is as follows:

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Increasing the share of coastal shipping and inland navigation in the transport modal mix is one of the key objectives of Sagarmala. In order to equip ports for movement of coastal cargo, the scope of coastal berth scheme has been expanded and merged with Sagarmala. Under the scheme, the financial assistance of 50% of project cost is provided to Major Ports/State Governments for construction of Coastal Berths, Breakwater, mechanization of coastal berths and capital dredging. Rs. 152 Cr has been released for 16 projects under this scheme. To augment transshipment capacity in the country, Vizhinjam (Kerala) and Enayam (Tamil Nadu) are being developed as transshipment ports. Vizhinjam is being developed as transshipment hub under PPP mode by Government of Kerala with Viability Gap Funding22 from Government of India.

Switching back to engineering, projects considered for funding under Sagarmala will either be provided equity support (SPV route) from SDC or funded (other than equity support) from the budget of Ministry of Shipping. Port projects will be primarily funded through the SPV route. Once the project is funded after due appraisal and approval, to the extent and limits prescribed under the guidelines, funds shall be released once all the clearances are in place. Most importantly, no other guarantees23 will be provided to the projects considered for funding.

The fund contribution from Sagarmala (from the budget of Ministry of Shipping) in any project will be limited to 50 per cent of project cost as per the Detailed Project Report (DPR) or tendered cost, whichever is lesser. 50 per cent is the cap of assistance from all sources/schemes of Government of India and will be provided in three tranches24 based on project milestones, In case of UTs, where no other sources of funding are available, the limit of 50 per cent could be relaxed. The fund released for a project being implemented in convergence mode with the schemes of other Central Line Ministries will not be higher than the approved ceiling of financial assistance under the concerned Central Sector Scheme (CSS). Projects having high social impact but with no return or low Internal Rate of Return (IRR)25 (e.g. fishing harbour projects, coastal community skill development projects, coastal tourism infrastructure projects etc.) may be provided funding, in convergence with the schemes of other Central Line Ministries, for implementation under Engineering, Procurement and Construction (EPC) mode. EPC mode is an interesting digression from PPP model as the former has a slight edge over the latter. In an EPC mode, the Government bears the entire financial burden and funds the project by raising capital through issuing bonds. In PPP, private entity would do cost-benefit analysis and would bid for project. Cheapest bid would be selected. So incentive is to reduce bid price. But as construction starts, there are local protests against land acquisition, and thus work halts. That means now costs would go up. Project faces market risk. Private entity will suffer loss and would refuse to work on pre-agreed bid. He would ask for more funding from Government. Government machinery, lethargic or may be skeptic of bidder’s intentions, would also make counter-arguments. And so there would be litigations. In EPC, it is government who is going to take up engineering. But, does government has engineering expertise? No, so government would call for bids for engineering knowledge. Thereafter, the government would give out calls for procurement of raw material and construction expertise. Under an EPC contract, the contractor designs the installation, procures the necessary materials and builds the project, either directly or by subcontracting part of the work. In some cases, the contractor carries the project risk for schedule as well as budget in return for a fixed price, called lump sum or LSTK26 depending on the agreed scope of work. So here, if project halts due to say local protests, government will deal with it. The private entity is saved from political questions. Anyway private entities are there merely for bottomline, whereas government is there for political tussle and governance. Thus EPC makes more sense and is an alternative for PPP. But, barring a few exceptions, the Government is still holding on to PPP as the preferred mode.

The equity contribution from SDC, in any project SPV, will be decided based on the project equity as per its DPR and will generally not exceed 49 per cent of the project equity. SDC can take equity contribution in existing or newly incorporated SPVs formed by State Governments/Maritime Boards/Ports etc. provided that these SPVs have projects which are ready for implementation. SDC can take equity in an existing or newly incorporated umbrella SPVs formed by State Governments/Maritime Boards/Ports etc. provided the same has been duly approved by the competent authority. SDC’s participation in the umbrella SPV would not restrain SDC from taking part in any other project SPV created by the same State Governments/Maritime Boards/Ports. SDC shall take only token equity to initiate/assist the process of project development in those SPVs which are scouting for projects or having projects under development stage only. As it would be difficult to ascertain the revenue flow from a particular project, a separate accounting for each project is an important clause in the contract document of the SPV. Continuing in line with equity-based funding, the question then arises as to what would be the recommended monitoring parameters for funded projects for equity and for those other than equity? Projects which are provided equity support (SPV route) by SDC will be monitored by the SPVs as well as SDC and Ministry of Shipping through an appropriate monitoring and evaluation mechanism. For projects which are provided funding (other than equity support), the fund recipients/project proponents will submit monthly progress report (physical and financial) of projects as per the electronic format/ MIS prescribed by SDC. SDC along with the fund recipients/project proponents will monitor the progress of projects based on the same. Additionally, the fund recipients/ project proponents will submit the utilization certificate for the fund released in the previous tranche for claiming release of subsequent installments/tranches. Wherever possible, the fund recipients/project proponents will submit a completion certificate, issued by an independent 3rd party agency, along with the final utilization certificate to claim the final tranche of fund. The 3rd party agency is to be appointed by the Ministry of Shipping from the approved panel maintained by the Indian Ports Association (IPA) for this purpose. The cost of appointing and functioning of the 3rd party agency will be borne by the Ministry of Shipping. The fund recipients/project proponents will maintain financial records, supporting documents, statistical records and all other records, to support performance of the project.

Although the monitoring mechanisms look neat on paper, there is absence of any transparency and accountability of whether these are in existence, or are these to be invoked at a stage when funds reach a point of questionability either in the sense of non- repayment, or by stressed assets, the consequent of which are the Non-Performing Assets. Still, what is not very clear is who are the funders involved apart from Government of India and State Governments. It is absolutely clear though, that both these entities would be taking recourse to National Financial Institutions (NFIs) and Non-Banking Financial Institutions (FIs) in addition to packing coffers in the budgets (both at the central and at the state levels) towards the massive investments in point. But, what of the International Financial Institutions (IFIs), or bi-lateral development institutions? This question is slightly jumping the gun, and would be understood in the perspective of what has now come to be called Blue Growth Initiative (BGI). Let us park this for a section and turn to looking at Sagarmala with some of its humungous initiatives that would give an idea behind numbers and figures.

Multi-headed Hydra (Projects envisaged under Sagarmala)

That the Government is going full steam on infrastructure cannot be fathomed by looking at Sagarmala in isolation. This has to be looked in tandem with industrial corridors, coastal economic zones, inland waterways, and tourism among a host of infrastructural stressed-upon points. Though, much of that is largely outside the scope of this chapter, it nevertheless is crucial to hover the compass of analysis in a loci around these allied infrastructures.

Starting with port modernization and new port development, Sagarmala is a gamut of 189 projects tipped at a whopping 1.42 lakh crore. Of these, the masterplans have already been finalized for 12 major ports; 142 projects at a cost of Rs. 91, 434 crore identified for implementation till 2035; and 42 projects worth Rs. 23, 263 crore are already under implementation.

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Port modernization along with new port development and major port operational efficiency improvement is to be integrated into what is now referred to as promotion of cruise tourism, foe which a task force has already been constituted and where now foreign flag vessels with passengers on board would be allowed to call at Indian ports without obtaining a license from director General of Shipping. Well, isn’t this what globalization is all about? Yes, largely, and more concertedly if the operating procedures effectuating these get standardized, and this is what has precisely happened for promoting cruise tourism in consultation with Bureau of Immigration, Ministry of Home Affairs, Central Board of Excise and Customs, Central Industrial Security Force and Port Authorities. The collaborative efforts of these authorities have led to the constitution of port-level committees to address manpower, coordination and logistical support. It is under the aegis of Sagarmala that cruise terminals are under development at Chennai and Mormugao in Goa.

Under the umbrella of port connectivity enhancements, 170 projects are either approved or in the pipeline at a cost of Rs. 2.3 lakh crore comprising of rail connectivity projects, road connectivity projects, multi-modal logistics parks and coastal shipping. Rail and road connectivity are precisely the components of freight corridors also launched under the name of industrial or economic corridors. Coastal shipping on the other hand is closely amalgamated with inland waterways. On a project-wise scale, there are plans to implement road projects under Sagarmala including 10 freight friendly expressways (E.g. Expressway from Ahmedabad to JNPT). Other proposals include awarding implementation of Heavy Haul Rail Corridor project between Talcher & Paradip in coordination with Ministry of Railways, proposing Cabotage relaxation for 2 years subject to level playing field for Indian flag ships, and bringing out a modal shift incentive scheme for Inland Water Transport sector by developing 37 prioritized National Waterways.

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On the port-led industrialization front, 33 projects are either approved, or in the pipeline at a cost of Rs. 4.2 lakh crore with perspective plans prepared for 14 coastal economic zones (CEZs). Moreover, 29 potential port-linked industrial clusters identified across Energy, Materials, Discrete Manufacturing and Maritime sectors are identified. The futuristic plans for port-led industrialization involves developing master plans for the 14 coastal economic zones in a phased manner with the first phase covering the states of Gujarat, Maharashtra, Andhra Pradesh and Tamil Nadu. The proposals also include developing detailed project reports (DPRs) for maritime clusters in Gujarat and Tamil Nadu. Crucially, the integration of smart cities with Sagarmala would be the implementation of Kandla & Paradip Smart Port Industrial Cities. What the Government has achieved through the New Shipbuilding Policy is granting shipyards infrastructure status, thus helping avail cheap working capital. The policy also has provided exemptions on taxes and duties, and made recommended arrangement for financial assistance to the tune of Rs. 20 crore of the initial cost flowing in from the center.

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The Coastal Community Development could be looked at under two broad categories, viz. skill development and fisheries development. Under the category of skill development, Rs. 30 crore have been sanctioned, of which Rs. 10 crore are already released towards safety training for workers in Alang-Sisoya shipyard. For the Coastal Districts Skill Training Project under Deen Dayal Upadhyay Grameen Kaushal Yojana (DDU-GKY), Rs. 13.77 crore have been sanctioned & Rs. 6.9 crore are already released. The Ministry of Shipping is undertaking skill gap analysis in 21 coastal districts, and the action plan for 6 districts in Gujarat, Maharashtra & AP are already prepared with projects from the same to be implemented under DDU-GKY. Under fisheries development, the Ministry of Shipping is part-funding select fishing harbour projects under Sagarmala in convergence with Department of Animal Husbandry Dairying & Fisheries (Ministry of Agriculture). On a more specific project-wise data, Rs. 52.17 crore is sanctioned for modernization & upgrading of Sassoon Dock. Upgradation of Kulai, Veraval and Mangrol fishing harbours are in the pipeline. For the Ministry of Shipping, this would support development of deep sea fishing vessels and fish processing centers in convergence with Department of Animal Husbandry Dairying & Fisheries.

The scale is massive and thus makes the moot question of who is financing Sagarmala all the more pressing. The disclaimer is: not much is known as there is a tremendous lack of transparency and accountability as regards this. But, information from discrete sources that are in the public domain at least gives a fair enough idea of who could be behind this gigantic infrastructure? At the same time, one needs to look at the intricate knot between the financiers and Blue Growth Initiative to draw out a clear message, which shouldn’t be shocking anymore, and that being the policies and funds are internationally-oriented. It is this section, the penultimate one in the chapter that we now turn to.

Who possibly could be driving the impetus for Blue Revolution/Economy and Sagarmala?

Are there International Financial Institutions involved? Hopefully by the end of this section, there would be some clarity to the muddied waters. Shipping ministry is roping in global multilateral agencies to extend a helping hand to entrepreneurs looking to explore Rs 3.5 lakh-crore of investment opportunities under the Sagarmala project, which was aimed at port-led development along 7,500-km coastline. Devendra Kumar Rai, Director at the Ministry of Shipping, said Sagarmala Development Company, with an equity base of Rs 1,000 crore, would chip in for investments and viability gap funding to help entrepreneurs achieve viable returns on their investments. He also said that the company was also seeking the support of Asian Development Bank (ADB) and other multilateral agencies for program loans to various initiatives under the Sagarmala project.27 Admitting that not all the projects being contemplated under the Sagarmala project would offer attractive returns on investments, Rai said, “The shipping ministry views that unless there is at least 13 per cent IRR, no private investor would come forward to invest.” To address the issue of not so attractive returns, the ministry would encourage some projects under the public-private partnership model and even extend the viability gap funding (VGF) up to 40 per cent of the project cost to turn the projects viable, Rai said. MT Krishna Babu, chairman of Visakhapatnam Port Trust, said various development projects will be thrown open for private participation. “The five greenfield ports alone would involve investments of around Rs 25,000 crore each in phases.” Babu said it will mobilise funds from government and global agencies like ADB and Japan International Cooperation Agency (JICA) and decide on the nature of funding to the unviable projects – whether equity or VGF.28

Asian Development Bank (ADB) has been giving boosters to India’s infrastructure development program from time to time, and injected yet another towards the end of June this year, when it promised the country its commitment to investing $10 billion over the next five years. Half the sum, or $5 billion were to be used for developing the 2,500 km East Coast Economic Corridor, which will ultimately extend from Kolkata to Tuticorin in Tamil Nadu. ADB had last year approved $631 million to develop the 800-km industrial corridor between Visakhapatnam and Chennai.29 The East Coast Economic Corridor also aligns with port-led industrialization under Sagarmala initiative and Act East Policy by linking domestic companies with vibrant global production networks of east and southeast Asia.

The World Bank, on the other hand, might not seem to have a direct hand in the funding of Sagarmala, but thinking it thus would be like missing the woods for the trees. Though the Bank is heavily investing in industrial corridors with a significant share in Amritsar-Kolkata Industrial Corridor, and seed capital along with creating conditions ripe for Viability Gap funding along the Delhi-Mumbai Industrial corridor, its involvement in Sagarmala is like an advisory to the Government of India. The World Bank, which has been advising the shipping ministry on the development of the inland waterways and the Clean Ganga mission, has approved an assistance of Rs 4,200 crore for the development of the existing five national waterways. Incidentally, India has jumped 19 places in the latest World Bank ranking in the global logistics performance. The World Bank in its latest once-in-two-year Logistics Performance Index (LPI) said India is now ranked 35th as against the 54th spot it occupied in the previous 2014 report. Such rise in position on the global logistics performance is the policy shot in the arm for India, thus making it conducive for investments to flow in.

But, to understand the policy initiatives behind Blue Revolution/Economy and Sagarmala, one needs to keep a tab on what is known as the Blue Growth Initiative, which happens to be the climate initiatives platform of the United Nations Programme on Environment. The Food and Agriculture Organization of the United Nations (FAO) Blue Growth Initiative (BGI) aims at building resilience of coastal communities and restoring the productive potential of fisheries and aquaculture, in order to support food security, poverty alleviation and sustainable management of living aquatic resources. Promoting international coordination is crucial to strengthen responsible management regimes and practices that can reconcile economic growth and food security with the restoration of the eco-systems they sustain. The initiative works towards two major goals, viz.

  1. Enabling environment (capacity building, knowledge platform, and improved governance) established within 2 to 3 years in 10 target countries.

  2. 10% reduction of carbon emissions in the 10 target countries in 5 years and 25% in 10 years.
  3. 3. Reduction of overfishing by 20% in the target countries in 5 years and 50% in 10 years.
  4. 4. “Blue communities” established in 5 target countries and resource stewardship ownership with 30% improved livelihoods.
  5. 5. Ecosystem degradation reversed in the target countries and 10% ecosystems restored in 4 target countries within 5 years.

These goals are to be attained by improving the evaluations of ecosystem services in Large Marine Ecosystems including coastal zones and Lakes for local and regional integrated and spatial planning. Also, strengthened fisheries and aquaculture governance and institutional frameworks would augment clear objectives and development paths for the sector; by increasing contributions from the small-scale fisheries and aquaculture sectors – through improved fisheries management, aquaculture development and improved post-harvest practices and market access. The initiative plans to attain increased resilience to climate change, extreme events and other drivers of change through improved knowledge of vulnerability and adaptation and disaster risk management options specific to fisheries, aquaculture and dependent communities who are at the front line of change and thereby providing technical and financial support to transitioning the sector to low-impact and fuel/energy efficiency and Blue Carbon enhancements30. for the medium and long term, the BGI is being promoted as an important vehicle for mobilizing resources and advocacy in international fora. In the global arena, the Initiative is enabling FAO to align with major global initiatives such as the Green Economy in a Blue World (UNEP/IMO/FAO/UNDESA/IUCN/World Fish), the Global Partnership for Oceans GPO (World Bank), the Coral Triangle Initiative, the Oceans SDG, Fishing for the Future (World Fish/FAO), the World Ocean Council and GEF6, as well as commitments stemming from the Rio+20 Conference. The oceans with a current estimated asset value of USD 24 trillion and an annual value addition of US$2.5 trillion, would continue to offer significant economic benefits both in the traditional areas of fisheries, transport, tourism and hydrocarbons as well as in the new fields of deep-sea mining, renewable energy, ocean biotechnology and many more, only if integrated with sustainable practices and business models. With land-based resources depleting fast, there are renewed attempts to further expand economic exploitation of the world’s oceans. However, if not managed sustainably, growing economic engagement with the oceans could risk further aggravation of their already strained health with serious impact on their natural role as the single most important CO2 sink and replenisher of oxygen. This in turn could accelerate global warming with catastrophic effects on fish stocks, climatic stabilization, water cycle and essential biodiversity. With arguments like these, UNFAO sure needs voices from the communities who would be severely impacted by these policies slapped on them.

So, even if India is not officially a participating nation in the Blue Growth Initiative (BGI), it could easily be drawn that the whole rationale for the Blue Revolution has sprung up from UNFAO. So, what is the viability of Blue Revolution/Economy and Sagarmala? It is to this section we turn now by way of conclusion.

Conclusion

Even while this chapter was being written, another mega infrastructure project in the form of country’s first bullet train was inaugurated to be laid between the Financial Capital Mumbai and Ahmedabad. As Prime Minister Narendra Modi and Japanese Prime Minister Shinzo Abe lay the foundation stone for the Mumbai-Ahmedabad bullet train project in Gujarat, tribals in Maharashtra and Gujarat will meet tehsildars of tribal areas that will be impacted by the project and submit letters of protest.31 Criticisms of the kind are expected and are already taking shape across the coastline.32 But, since this chapter is geared towards understanding the financial and economic ecosystem of Blue Revolution/ Economy and Sagarmala, let us divert our attention to financial critiques of mega infrastructural development projects.

The corporates and multinational Companies have joined together with the intention to grab the ocean and the coast. State and Central governments have become agents of the corporates and are permitting the foreign trawlers in the India deep seas, constructing atomic thermal power stations, disastrous chemical industries, hotels and big resorts and coastal industrial corridors at the coasts. These projects have several illegalities in formulation, approval, sanction and implementation. The livelihood of the fishworkers has not been considered by the elected government.33 The corporates have freedom to pour toxic affluent in the coastal area and sea soar. As a result fishes died and disappeared. So far more than twenty commercial species of fish have been disappeared from the sea at Kachchh, Gujarat. Similar is the case in other sea areas. Mangroves have been destroyed and land are being used by corporates. Solid toxic wastes and inflatable toxics dumped in forest and other empty threatens human and animal lives in the coastal area.34 This is not a mirror into the post-apocalyptic, run over by hungry capitalism scenario, but a reality that is very much brewing across the coastline of the country from Gujarat in the west to West Bengal in the east.

Economically and financially, there are three major challenges that are encountered in infrastructural mega-projects. In one influential study, Bent Flyvbjerg35, an expert in project management at Oxford’s business school, estimated that nine out of ten go over budget. Second challenge is the time overrun, which directly leads to cost escalation. Finally, the premise that projects need to work on two levels – in the short term for recovering financial outlays and the longer term for creating social impact – often becomes a barrier to taking action. Even projects that are needed do not get executed, especially in places where revenues from a project are unlikely to cover its cost. the enormity of Sagarmala leaves it vulnerable to unviability. In addition to three reasons cited above as to what could challenge a mega-project, these three reasons are most likely to be attributable to why Sagarmala could remain an unfinished dream, and in a dire attempt to realizing it could have enormous adverse effects of the socio-environmental and economical life of the communities coming under its fire.

  1. Overoptimism and overcomplexity. In order to justify a project, sometimes costs and timelines are systematically underestimated and benefits systematically overestimated. Flyvbjerg argues that project managers competing for funding massage the data until they come under the limit of what is deemed affordable; stating the real cost, he writes, would make a project unpalatable. From the outset, such projects are on a fast track to failure. One useful reality check is to compare the project under consideration to similar projects that have already been completed. Known as “reference-class forecasting,” this process addresses confirmation bias by forcing decision makers to consider cases that don’t necessarily justify the preferred course of action. But, sadly, India does not have any avenues to a “reference-class forecasting”, for the country has hitherto not known anything on this scale.

  2. Poor execution. Having delivered an unrealistically low project budget, the temptation is to cut corners to maintain cost assumptions and protect the (typically slim) profit margins for the engineering and construction firms that have been contracted to deliver the project. Project execution, from design and planning through construction, is riddled with problems such as incomplete design, lack of clear scope, ill-advised shortcuts, and even mathematical errors in scheduling and risk assessment. In part, execution is poor because many projects are so complex that what might seem like routine issues can become major headaches. For example, if steel does not arrive at the job site on time, the delay can stall the entire project. Ditto if one of the specialty trades has a problem. Higher productivity will not compensate for these shortfalls because such delays tend to ripple through the entire project system. Another challenge is low productivity. While the manufacturing sector in India is languishing, raw materials for such mega-projects would always be hindered through supply lines, thus leading to either stalling of the projects, or an exponential cost escalation.
  3. Weakness in organizational design and capabilities. Many entities involved inbuilding megaprojects have an organizational setup in which the project director sits four or five levels down from the top leadership. The following structure is common:

    Layer 1: Subcontractor to contractor

    Layer 2: Contractors to construction manager or managing contractor

    Layer 3: Construction manager to owner’s representative

    Layer 4: Owner’s representative to project sponsor

    Layer 5: Project sponsor to business executive

This is a problem because each layer will have a view on how time and costs can be compressed. For example, the first three layers are looking for more work and more money, while the later ones are looking to deliver on time and budget. Also, the authority to make final decisions is often remote from the action. Capabilities, or lack thereof, are another issue. Large projects are typically either sponsored by the government or by an entrepreneur with bold aspirations, where completion times are most often than not compromised.


Notes:

1 Micro Units Development & Refinance Agency Ltd. (MUDRA) is an institution set up by Government of India to provide funding to the non-corporate, non-farm sector income generating activities of micro and small enterprises whose credit needs are below ₹10 Lakh. Under the aegis of Pradhan Mantri MUDRA Yojana (PMMY), MUDRA has created three products i.e. ‘Shishu’, ‘Kishore’ and ‘Tarun’ as per the stage of growth and funding needs of the beneficiary micro unit. These schemes cover loan amounts as below:

  1. a  Shishu: covering loans up to ₹50,000
  2. b  Kishore: covering loans above ₹50,000 and up to ₹5,00,000
  3. c  Tarun: covering loans above ₹5,00,000 and up to ₹10,00,000

All Non-Corporate Small Business Segment (NCSBS) comprising of proprietorship or partnership firms running as small manufacturing units, service sector units, shopkeepers, fruits/vegetable vendors, truck operators, food-service units, repair shops, machine operators, small industries, food processors and others in rural and urban areas, are eligible for assistance under Mudra. Bank branches would facilitate loans under Mudra scheme as per customer requirements. Loans under this scheme are collateral free loans.

2 Press Information Bureau, Government of India, Ministry of Agriculture. Mission Fingerling with a total expenditure of about Rs. 52000 lakh to achieve Blue Revolution. 11 Mar 2017 <http://pib.nic.in/ newsite/PrintRelease.aspx?relid=159159>

3 Government of India, Ministry of Agriculture and Farmers Welfare Department of Animal Husbandry, Dairying & Fisheries. Central Sector Scheme on Blue Revolution: Integrated Development and Management of Fisheries. Jun 2016 <http://dahd.nic.in/sites/default/files/ Guidelines.BR-30616.Fisheries.pdf>

4 Press Information Bureau, Government of India, Cabinet Committee on Economic Affairs (CCEA). Integrated Development and Management of Fisheries – a Central Sector Scheme on Blue Revolution. 22 Dec 2015

5 The term “ocean grabbing” has been used to describe actions, policies or initiatives that deprive small- scale fishers of resources, dispossess vulnerable populations of coastal lands, and/or undermine historical access to areas of the sea. Rights and access to marine resources and spaces are frequently reallocated through government or private sector initiatives to achieve conservation, management or development objectives with a variety of outcomes for different sectors of society. For a reallocation to be considered ocean grabbing, it must: (1) occur by means of inadequate governance, and (2) be implemented using actions that undermine human security and livelihoods, or (3) produce impacts that reduce social- ecological well-being.

6 The Sagarmala initiative would also strive to ensure sustainable development of the population living in the Coastal Economic Zone (CEZ). This would be done by synergising and coordinating with State Governments and line Ministries of Central Government through their existing schemes and programmes such as those related to community and rural development, tribal development and employment generation, fisheries, skill development, tourism promotion etc. In order to provide funding for such projects and activities that may be covered by departmental schemes a separate fund by the name ‘Community Development Fund’ would be created.

7 In the words of T Peter, General Secretary, National Fish Workers Forum (NFF), “Governments support corporates and no one is concerned about the livelihood of the people. Now, the Union government is bent on supporting Adani group on the pretext of developing ports across the country. The Sagarmala project, which proposes setting up industrial corridors, 52 new ports and petrochemical region will deplete the vulnerable coastline and will leave the survival of the fishermen community at stake. It will only support real estate majors and industrialists.” Fishermen oppose Sagarmala project. Times of India. 23 Dec 2016 <http://timesofindia.indiatimes.com/city/thiruvananthapuram/fishermen-oppose-sagarmala- project/articleshow/56140852.cms>

8 Kohli, K. & Menon, M. Of a frictionless development : Ports have the potential to endanger the environment. Daily News and Analysis (DNA), 21 May 2017 <http://www.dnaindia.com/analysis/ column-of-a-frictionless-development-2445597>

9 India has around 7,500-km long coastline, but the country transports only 6 per cent of its cargo through the waterways compared with around 55 per cent on roadways and 35 per cent by the railways. As a result, India’s logistics costs as percentage of its GDP is as high as 19 per cent compared with 12.5 per cent in China. According to the echoes in New Delhi, India’s exports would go up by one and a half times if the country was able to reduce its logistics costs to 12 per cent. India’s cargo traffic growth is expected to increase to 2,500 MT in 2024-25 from 1,072 MT in 2015-16. In India, share of coastal and inland water transport is 2-3 per cent compared to China’s 25 per cent.

10 Sagarmala Coordination and Steering Committee (SCSC) is constituted under the chairmanship of the Cabinet Secretary with Secretaries of the Ministries of Shipping, Road Transport and Highways, Tourism, Defence, Home Affairs, Environment, Forest & Climate Change, Departments of Revenue, Expenditure, Industrial Policy and Promotion, Chairman, Railway Board and CEO, NITI Aayog as members. This Committee will provide coordination between various ministries, state governments and agencies connected with implementation and review the progress of implementation of the National Perspective Plan, Detailed Master Plans and projects. It will, inter alia, consider issues relating to funding of projects and their implementation. This Committee will also examine financing options available for the funding of projects, the possibility of public-private partnership in project financing/construction/ operation.

11 A National Sagarmala Apex Committee (NSAC) is geared for overall policy guidance and high level coordination, and to review various aspects of planning and implementation of the plan and projects. The NSAC shall be chaired by the Minister in-charge of Shipping, with Cabinet Ministers from stakeholder Ministries and Chief Ministers/Ministers in-charge of ports of maritime states as members. This committee, while providing policy direction and guidance for the initiative’s implementation, shall approve the overall National Perspective Plan (NPP) and review the progress of implementation of these plans.

12 Ray, S. S. Infrastructure: How Sagarmala project can be a shot in the arm for the economy. Financial Express. 21 Nov 2016 <http://www.financialexpress.com/economy/infrastructure-how-sagarmala-project- can-be-a-shot-in-the-arm-for-the-economy/450802/>

13 Indian ports handle more than 90 percent of India’s total EXIM trade volume. However, the current proportion of merchandize trade in Gross Domestic Product (GDP) of India is only 42 percent, whereas for some developed countries and regions in the world such as Germany and European Union, it is 75 percent and 70 percent respectively. Therefore, there is a great scope to increase the share of merchandising trade in India’s GDP. With the Union Government’s “Make in India” initiative, the share of merchandise trade in India’s GDP is expected to increase and approach levels achieved in developed countries. India lags far behind in ports and logistics infrastructure. Against a share of 9 percent of railways and 6 percent of roads in the GDP the share of ports is only 1 percent. In addition high logistics costs make Indian exports uncompetitive. This is another of the reasons from the economic front that the government wants to drill into the populace as a justification for the launch of the initiative.

14 Sood, J. Govt mulls Rs10 trillion public financing for infrastructure projects. LiveMint. 12 Sep 2017 <http://www.livemint.com/Home-Page/G63KRD11vfvag0lOWJtBIN/Govt-mulls-Rs10-trillion-public- financing-for-infrastructure.html>

15 As there are limited options available to raise funds for infrastructure finance, the plan to raise money from retirees and provident fund beneficiaries comes as Indian banks, loaded with bad debt, have turned averse to funding infrastructure projects. With many large conglomerates and infrastructure companies weighed down by debt, the onus of creating infrastructure has fallen on the government.

16 India needs funds for its ambitious plans such as Sagarmala (ports) and Bharatmala (roads) to improve its transport infrastructure. While the total investment for the Bharatmala plan is estimated at Rs10 trillion – the largest ever outlay for a government road construction scheme – the country has envisaged Rs8 trillion of investment until 2035 under the Sagarmala programme.

17 We should have more to talk about Financial Intermediaries in a while, but is is important to note that pension funds belong to this category. Pension funds may be defined as forms of institutional investor, which collect pool and invest funds contributed by sponsors and beneficiaries to provide for the future pension entitlements of beneficiaries. They thus provide means for individuals to accumulate saving over their working life so as to finance their consumption needs in retirement, either by means of a lump sum or by provision of an annuity, while also supplying funds to end-users such as corporations, other households (via securitized loans) or governments for investment or consumption.

18 The volatility of stock returns is why pension funds invested in bonds in the first place. The theory with alternatives is that they earn a premium return in exchange for the difficulty of investing in them. Small investors lack easy access to these asset classes, and the investments are often illiquid. As a result, by investing in alternatives, pension funds should be able to get either returns similar to those of equities at a lower risk, or higher returns at a similar level of risk. That’s the theory. The evidence on alternative investments is considerably more mixed. Hedge funds are supposed to pursue equity-like returns with lower levels of risk. Hedge funds don’t have to report their performance to public databases, and are more likely to do so when returns are good. They often engage in strategies that produce modest regular returns at the expense of rare catastrophic losses, which may make their track records look misleadingly strong.

19 This could be acted upon, with a specific case study that underlines the knowledge-base requisite for any understanding of financials involved in the project. Knowledge-base could encompass: issues for the host government/legislative provisions, public/private infrastructure partnerships, public/private financial structures, credit requirement of lenders, and analytical techniques to measure the feasibility of the project. In case of Project Finance, the financier principally looks to the assets and revenue of the project in order to secure and service the loan. In contrast to an ordinary borrowing situation, in a project financing the financier usually has little or no recourse to the non-project assets of the borrower or the sponsors of the project. In this situation, the credit risk associated with the borrower is not as important as in an ordinary loan transaction; what is most important is the identification, analysis, allocation and management of every risk associated with the project.

20 This might sound like an argument from a Devil’s Advocate, for financial intermediaries are nowadays increasingly used as markets for firms’ assets. Financial intermediaries appear to have a key role in the restructuring and liquidation of firms in distress. In particular, financial intermediaries play an active role in the reallocation of displaced capital, meant both as the piece-meal reallocation of assets and, more broadly, as the sale of entire bankrupt corporations to healthy ones. A key part of reorganization under main bank supervision or management is the implementation of a plan of asset sales with proceeds typically used to recover bank loans. Knowing possible synergies among firms, banks can suggest solutions for the efficient reallocation of assets and of corporate control and that in several countries there is widespread anecdotal evidence, though not quantitative one, on this role of banks. Healthy firms search around for the displaced capital of bankrupt firms but matching is imperfect and firms can end up with machines unsuitable for them. Financial intermediaries arise as internal, centralized markets where information on machines and buyers is readily available, allowing displaced capital to migrate towards its most productive uses. Financial intermediaries can perform this role by aggregating the information on firms collected in the credit market. The function of intermediaries as matchmakers between savers and firms in the credit market can support their function as internal markets for assets. Intuitively, by increasing the number of highly productive matches in the credit market, intermediaries increase the share of highly productive second hand users in the decentralized resale market. This improvement in the quality of the decentralized secondary market reduces the incentive of firms to address financial intermediaries for their ability as re-deployers. However, by increasing the number of highly productive matches in the credit market, intermediaries create also wealthy buyers without assets and contribute to decrease the thickness of the decentralized resale market. This makes the decentralized market less appealing and increases the incentive of firms to use intermediaries as resale markets. When the quality improvement in the decentralized market is not too big and the second effect prevails, better matchmaking in the credit market supports the function of intermediaries as internal markets for assets.

21 Economies of scale is an economics term that describes a competitive advantage that large entities have over smaller entities. It means that the larger the business, non-profit or government, the lower its costs. For example, the cost of producing one unit is less when many units are produced at once. This is confusingly used with economies of scope. It is worthwhile to differentiate the two here. Economies of scope occur when a company branches out into multiple product lines. When companies broaden their scope, they benefit by combining complementary business functions, product lines or manufacturing processes. For example, most newspapers diversified into similar product lines, such as magazines and online news, to diversify their revenue from declining newspaper sales. They achieved some economies of scope by taking advantage of their advertising sales teams, who could sell advertising in all three product lines.

22 Viability Gap Funding (VGF) is a special facility to support the financial viability of those infrastructure projects, which are economically justifiable but not viable commercially in the immediate future. It involves upfront grant assistance of up to 20% of the project cost for state or central PPP projects implemented by the private sector developer who is selected through competitive bidding. An Empowered Committee has been set up for quick processing of cases. Sectors shortlisted for availing Viability Gap Funding Assistance include Roads and bridges, railways, seaports, airports, inland waterways, Power, Urban transport, water supply, sewerage, solid waste management and other physical infrastructure in urban areas. Infrastructure projects in Special Economic Zones and International convention centers and other tourism infrastructure projects.

23 The use of guarantees offers extra security to the lender or business who is providing the finance. For an SME with only one director in the company, the lender relies heavily on their ability to pay it back. But with another person or company to back up the loan agreement, there is extra security that the lender will be able to recover their funds. In particular, for those companies or directors with an adverse credit record, they may rely on the use of a guarantor in order to secure the funds they need. Every extra guarantee added gives the lender more confidence, especially when guaranteed by individuals or firms with strong credit records and reputations. Guarantees are primarily of two types: (1) A pure guarantee ensures that the third party meets their financial obligations. They are legally obliged and responsible to be a guarantor. (2) A conditional payment guarantee means that they are liable to pay any amounts outstanding.

24 Tranches are pieces, portions or slices of debt or structured financing. Each portion, or tranche, is one of several related securities offered at the same time but with different risks, rewards and maturities. For example, a collateralized mortgage obligation CMO offering a partitioned mortgage-backed securities MBS portfolio might have mortgage tranches with one-year, two-year, five-year and 20-year maturities, all with varying degrees of risk and returns. A tranche is a common financial structure for debt securities such as mortgage-backed securities. These types of securities are made up of multiple mortgage pools that have a wide variety of mortgages, from safe loans with lower interest rates to risky loans with higher rates. Each specific mortgage pool also has its own time to maturity, which factors into the risk and reward benefits. Therefore, tranches are made to divide up the different mortgage profiles into slices that have financial terms suitable for specific investors.

25 Internal rate of return (IRR) is the interest rate at which the net present value of all the cash flows (both positive and negative) from a project or investment equal zero. Internal rate of return is used to evaluate the attractiveness of a project or investment. If the IRR of a new project exceeds a company’s required rate of return, that project is desirable. If IRR falls below the required rate of return, the project should be rejected. IRR does not measure the absolute size of the investment or the return. This means that IRR can favor investments with high rates of return even if the dollar amount of the return is very small. For example, a $1 investment returning $3 will have a higher IRR than a $1 million investment returning $2 million. Another short-coming is that IRR can’t be used if the investment generates interim cash flows. Finally, IRR does not consider cost of capital and can’t compare projects with different durations. IRR is best-suited for analyzing venture capital and private equity investments, which typically entail multiple cash investments over the life of the business, and a single cash outflow at the end via IPO or sale.

26 LSTK stands for Lump Sum Turn Key. This is a contractual agreement in which a fixed price is agreed for the execution of a project or part of a project. Once the final development is completed a finished functioning asset is handed over to the client, hence the term “Turn Key” which effectively means ready to operate.

27 The Economic Times. Government roping in multilateral agencies for Rs 3.5 lakh crore Sagarmala project. 12 Feb 2016 <http://economictimes.indiatimes.com/news/economy/infrastructure/government- roping-in-multilateral-agencies-for-rs-3-5-lakh-crore-sagarmala-project/articleshow/50956475.cms>

28 ibid.

29 Bhaskar, U. ADB to invest $10 billion over five years in Indian infrastructure. LiveMint. 30 Jun 2017 <http://www.livemint.com/Politics/CTTTI4B5VWYVdkh6qOnV4J/ADB-to-invest-10-billion-over-five- years-in-Indian-infrastr.html>

30 The idea of blue economy was argued during the Rio+20 preparatory meetings, where several Small Islands Developing States (SIDS) observed that ‘Green Economy’ had limited relevance for them; instead, ‘Green Economy in a Blue World’ was a good concept and most suitable for the sustainable development and management of ocean resources.

31 Phadke, M. Tribals, farmers in Gujarat and Maharahshtra who will lose land protest bullet train project. The Hindustan Times. 14 Sep 2017 <http://www.hindustantimes.com/mumbai-news/tribals-and- farmers-in-gujarat-and-maharahshtra-who-will-lose-land-to-the-bullet-train-project-protest-against-it/ story-AWbl8Z6VR3EOdOTP73twtI.html>

32 Thozhilalar koodam. Sea is for the fishing communities – Coastal Yatra by NFF in Tamil Nadu. 15 Jul 2017 <http://tnlabour.in/fish-workers/5654&gt;

33 The proposed Enayam International Container Transhipment Terminal (EICTT), a port to be developed at Enayam, Kanyakumari, has witnessed a lot of opposition from the fishing community over the last year. The port, which was earlier proposed to be established at Colachel, was shifted 10 km away to Enayam last year. There has also been criticism over the proximity of the proposed Enayam port to the upcoming Vizhinjam port in Trivandrum and Vallarpadam port at Cochin. The National Fishworkers’ Forum (NFF) has also raised concern about Idinthakarai, which has been the epicentre for protests against the Kudankulam Nuclear Power Plant (KNPP), since 2012. Fishermen and environmentalists have been opposing the nuclear power plant as it could have drastic impact on the livelihood of the fishing community.

34 Sagarmala project: Serious concerns being raised about environmental effects on coastal areas. counterview.org. 21 Nov 2016 <https://counterview.org/2016/11/21/sagarmala-project-serious-concerns- being-raised-about-environmental-effects-on-coastal-areas/>

35 Flyvbjerg, B. What You Should Know About Megaprojects, and Why: An Overview. 2014 <https:// arxiv.org/pdf/1409.0003.pdf>

Accelerating the Synthetic Credit. Thought of the Day 96.0

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The structural change in the structured credit universe continues to accelerate. While the market for synthetic structures is already pretty well established, many real money accounts remain outsiders owing to regulatory hurdles and technical limitations, e.g., to participate in the correlation market. Therefore, banks are continuously establishing new products to provide real money accounts with access to the structured market, with Constant proportion debt obligation (CPDOs) recently having been popular. Against this background, three vehicles which offer easy access to structured products for these investors have gained in importance: CDPCs (Credit Derivatives Product Company), PCVs (permanent capital vehicle), and SIVs (structured investment vehicles).

A CDPC is a rated company which buys credit risk via all types of credit derivative instruments, primarily super senior tranches, and sells this risk to investors via preferred shares (equity) or subordinated notes (debt). Hence, the vehicle uses super senior risk to create equity risk. The investment strategy is a buy-and-hold approach, while the aim is to offer high returns to investors and keep default risk limited. Investors are primarily exposed to rating migration risk, to mark-to-market risk, and, finally, to the capability of the external manager. The rating agencies assign, in general, an AAA-rating on the business model of the CDPC, which is a bankruptcy remote vehicle (special purpose vehicle [SPV]). The business models of specific CDPCs are different from each other in terms of investments and thresholds given to the manager. The preferred asset classes CDPC invested in are predominantly single-name CDS (credit default swaps), bespoke synthetic tranches, ABS (asset-backed security), and all kinds of CDOs (collateralized debt obligations). So far, CDPCs main investments are allocated to corporate credits, but CDPCs are extending their universe to ABS (Asset Backed Securities) and CDO products, which provide further opportunities in an overall tight spread environment. The implemented leverage is given through the vehicle and can be in the range of 15–60x. On average, the return target was typically around a 15% return on equity, paid in the form of dividends to the shareholders.

In contrast to CDPCs, PCVs do not invest in the top of the capital structure, but in equity pieces (mostly CDO equity pieces). The leverage is not implemented in the vehicle itself as it is directly related to the underlying instruments. PCVs are also set up as SPVs (special purpose vehicles) and listed on a stock exchange. They use the equity they receive from investors to purchase the assets, while the return on their investment is allocated to the shareholders via dividends. The target return amounts, in general, to around 10%. The portfolio is managed by an external manager and is marked-to-market. The share price of the company depends on the NAV (net asset value) of the portfolio and on the expected dividend payments.

In general, an SIV invests in the top of the capital structure of structured credits and ABS in line with CDPCs. In addition, SIVs also buy subordinated debt of financial institutions, and the portfolio is marked-to-market. SIVs are leveraged credit investment companies and bankruptcy remote. The vehicle issues typically investment-grade rated commercial paper, MTNs (medium term notes), and capital notes to its investors. The leverage depends on the character of the issued note and the underlying assets, ranging from 3 to 5 (bank loans) up to 14 (structured credits).

Malignant Acceleration in Tech-Finance. Some Further Rumination on Regulations. Thought of the Day 72.1

these-stunning-charts-show-some-of-the-wild-trading-activity-that-came-from-a-dark-pool-this-morning

Regardless of the positive effects of HFT that offers, such as reduced spreads, higher liquidity, and faster price discovery, its negative side is mostly what has caught people’s attention. Several notorious market failures and accidents in recent years all seem to be related to HFT practices. They showed how much risk HFT can involve and how huge the damage can be.

HFT heavily depends on the reliability of the trading algorithms that generate, route, and execute orders. High-frequency traders thus must ensure that these algorithms have been tested completely and thoroughly before they are deployed into the live systems of the financial markets. Any improperly-tested, or prematurely-released algorithms may cause losses to both investors and the exchanges. Several examples demonstrate the extent of the ever-present vulnerabilities.

In August 2012, the Knight Capital Group implemented a new liquidity testing software routine into its trading system, which was running live on the NYSE. The system started making bizarre trading decisions, quadrupling the price of one company, Wizzard Software, as well as bidding-up the price of much larger entities, such as General Electric. Within 45 minutes, the company lost USD 440 million. After this event and the weakening of Knight Capital’s capital base, it agreed to merge with another algorithmic trading firm, Getco, which is the biggest HFT firm in the U.S. today. This example emphasizes the importance of implementing precautions to ensure their algorithms are not mistakenly used.

Another example is Everbright Securities in China. In 2013, state-owned brokerage firm, Everbright Securities Co., sent more than 26,000 mistaken buy orders to the Shanghai Stock Exchange (SSE of RMB 23.4 billion (USD 3.82 billion), pushing its benchmark index up 6 % in two minutes. This resulted in a trading loss of approximately RMB 194 million (USD 31.7 million). In a follow-up evaluative study, the China Securities Regulatory Commission (CSRC) found that there were significant flaws in Everbright’s information and risk management systems.

The damage caused by HFT errors is not limited to specific trading firms themselves, but also may involve stock exchanges and the stability of the related financial market. On Friday, May 18, 2012, the social network giant, Facebook’s stock was issued on the NASDAQ exchange. This was the most anticipated initial public offering (IPO) in its history. However, technology problems with the opening made a mess of the IPO. It attracted HFT traders, and very large order flows were expected, and before the IPO, NASDAQ was confident in its ability to deal with the high volume of orders.

But when the deluge of orders to buy, sell and cancel trades came, NASDAQ’s trading software began to fail under the strain. This resulted in a 30-minute delay on NASDAQ’s side, and a 17-second blackout for all stock trading at the exchange, causing further panic. Scrutiny of the problems immediately led to fines for the exchange and accusations that HFT traders bore some responsibility too. Problems persisted after opening, with many customer orders from institutional and retail buyers unfilled for hours or never filled at all, while others ended up buying more shares than they had intended. This incredible gaffe, which some estimates say cost traders USD 100 million, eclipsed NASDAQ’s achievement in getting Facebook’s initial IPO, the third largest IPO in U.S. history. This incident has been estimated to have cost investors USD 100 million.

Another instance occurred on May 6, 2010, when U.S. financial markets were surprised by what has been referred to ever since as the “Flash Crash” Within less than 30 minutes, the main U.S. stock markets experienced the single largest price declines within a day, with a decline of more than 5 % for many U.S.-based equity products. In addition, the Dow Jones Industrial Average (DJIA), at its lowest point that day, fell by nearly 1,000 points, although it was followed by a rapid rebound. This brief period of extreme intraday volatility demonstrated the weakness of the structure and stability of U.S. financial markets, as well as the opportunities for volatility-focused HFT traders. Although a subsequent investigation by the SEC cleared high-frequency traders of directly having caused the Flash Crash, they were still blamed for exaggerating market volatility, withdrawing liquidity for many U.S.-based equities (FLASH BOYS).

Since the mid-2000s, the average trade size in the U.S. stock market had plummeted, the markets had fragmented, and the gap in time between the public view of the markets and the view of high-frequency traders had widened. The rise of high-frequency trading had been accompanied also by a rise in stock market volatility – over and above the turmoil caused by the 2008 financial crisis. The price volatility within each trading day in the U.S. stock market between 2010 and 2013 was nearly 40 percent higher than the volatility between 2004 and 2006, for instance. There were days in 2011 in which volatility was higher than in the most volatile days of the dot-com bubble. Although these different incidents have different causes, the effects were similar and some common conclusions can be drawn. The presence of algorithmic trading and HFT in the financial markets exacerbates the adverse impacts of trading-related mistakes. It may lead to extremely higher market volatility and surprises about suddenly-diminished liquidity. This raises concerns about the stability and health of the financial markets for regulators. With the continuous and fast development of HFT, larger and larger shares of equity trades were created in the U.S. financial markets. Also, there was mounting evidence of disturbed market stability and caused significant financial losses due to HFT-related errors. This led the regulators to increase their attention and effort to provide the exchanges and traders with guidance on HFT practices They also expressed concerns about high-frequency traders extracting profit at the costs of traditional investors and even manipulating the market. For instance, high-frequency traders can generate a large amount of orders within microseconds to exacerbate a trend. Other types of misconduct include: ping orders, which is using some orders to detect other hidden orders; and quote stuffing, which is issuing a large number of orders to create uncertainty in the market. HFT creates room for these kinds of market abuses, and its blazing speed and huge trade volumes make their detection difficult for regulators. Regulators have taken steps to increase their regulatory authority over HFT activities. Some of the problems that arose in the mid-2000s led to regulatory hearings in the United States Senate on dark pools, flash orders and HFT practices. Another example occurred after the Facebook IPO problem. This led the SEC to call for a limit up-limit down mechanism at the exchanges to prevent trades in individual securities from occurring outside of a specified price range so that market volatility will be under better control. These regulatory actions put stricter requirements on HFT practices, aiming to minimize the market disturbance when many fast trading orders occur within a day.

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High Frequency Traders: A Case in Point.

Events on 6th May 2010:

At 2:32 p.m., against [a] backdrop of unusually high volatility and thinning liquidity, a large fundamental trader (a mutual fund complex) initiated a sell program to sell a total of 75,000 E-Mini [S&P 500 futures] contracts (valued at approximately $4.1 billion) as a hedge to an existing equity position. […] This large fundamental trader chose to execute this sell program via an automated execution algorithm (“Sell Algorithm”) that was programmed to feed orders into the June 2010 E-Mini market to target an execution rate set to 9% of the trading volume calculated over the previous minute, but without regard to price or time. The execution of this sell program resulted in the largest net change in daily position of any trader in the E-Mini since the beginning of the year (from January 1, 2010 through May 6, 2010). [. . . ] This sell pressure was initially absorbed by: high frequency traders (“HFTs”) and other intermediaries in the futures market; fundamental buyers in the futures market; and cross-market arbitrageurs who transferred this sell pressure to the equities markets by opportunistically buying E-Mini contracts and simultaneously selling products like SPY [(S&P 500 exchange-traded fund (“ETF”))], or selling individual equities in the S&P 500 Index. […] Between 2:32 p.m. and 2:45 p.m., as prices of the E-Mini rapidly declined, the Sell Algorithm sold about 35,000 E-Mini contracts (valued at approximately $1.9 billion) of the 75,000 intended. [. . . ] By 2:45:28 there were less than 1,050 contracts of buy-side resting orders in the E-Mini, representing less than 1% of buy-side market depth observed at the beginning of the day. [. . . ] At 2:45:28 p.m., trading on the E-Mini was paused for five seconds when the Chicago Mercantile Exchange (“CME”) Stop Logic Functionality was triggered in order to prevent a cascade of further price declines. […] When trading resumed at 2:45:33 p.m., prices stabilized and shortly thereafter, the E-Mini began to recover, followed by the SPY. [. . . ] Even though after 2:45 p.m. prices in the E-Mini and SPY were recovering from their severe declines, sell orders placed for some individual securities and Exchange Traded Funds (ETFs) (including many retail stop-loss orders, triggered by declines in prices of those securities) found reduced buying interest, which led to further price declines in those securities. […] [B]etween 2:40 p.m. and 3:00 p.m., over 20,000 trades (many based on retail-customer orders) across more than 300 separate securities, including many ETFs, were executed at prices 60% or more away from their 2:40 p.m. prices. [. . . ] By 3:08 p.m., [. . . ] the E-Mini prices [were] back to nearly their pre-drop level [. . . and] most securities had reverted back to trading at prices reflecting true consensus values.

In the ordinary course of business, HFTs use their technological advantage to profit from aggressively removing the last few contracts at the best bid and ask levels and then establishing new best bids and asks at adjacent price levels ahead of an immediacy-demanding customer. As an illustration of this “immediacy absorption” activity, consider the following stylized example, presented in Figure and described below.

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Suppose that we observe the central limit order book for a stock index futures contract. The notional value of one stock index futures contract is $50. The market is very liquid – on average there are hundreds of resting limit orders to buy or sell multiple contracts at either the best bid or the best offer. At some point during the day, due to temporary selling pressure, there is a total of just 100 contracts left at the best bid price of 1000.00. Recognizing that the queue at the best bid is about to be depleted, HFTs submit executable limit orders to aggressively sell a total of 100 contracts, thus completely depleting the queue at the best bid, and very quickly submit sequences of new limit orders to buy a total of 100 contracts at the new best bid price of 999.75, as well as to sell 100 contracts at the new best offer of 1000.00. If the selling pressure continues, then HFTs are able to buy 100 contracts at 999.75 and make a profit of $1,250 dollars among them. If, however, the selling pressure stops and the new best offer price of 1000.00 attracts buyers, then HFTs would very quickly sell 100 contracts (which are at the very front of the new best offer queue), “scratching” the trade at the same price as they bought, and getting rid of the risky inventory in a few milliseconds.

This type of trading activity reduces, albeit for only a few milliseconds, the latency of a price move. Under normal market conditions, this trading activity somewhat accelerates price changes and adds to the trading volume, but does not result in a significant directional price move. In effect, this activity imparts a small “immediacy absorption” cost on all traders, including the market makers, who are not fast enough to cancel the last remaining orders before an imminent price move.

This activity, however, makes it both costlier and riskier for the slower market makers to maintain continuous market presence. In response to the additional cost and risk, market makers lower their acceptable inventory bounds to levels that are too small to offset temporary liquidity imbalances of any significant size. When the diminished liquidity buffer of the market makers is pierced by a sudden order flow imbalance, they begin to demand a progressively greater compensation for maintaining continuous market presence, and prices start to move directionally. Just as the prices are moving directionally and volatility is elevated, immediacy absorption activity of HFTs can exacerbate a directional price move and amplify volatility. Higher volatility further increases the speed at which the best bid and offer queues are being depleted, inducing HFT algorithms to demand immediacy even more, fueling a spike in trading volume, and making it more costly for the market makers to maintain continuous market presence. This forces more risk averse market makers to withdraw from the market, which results in a full-blown market crash.

Empirically, immediacy absorption activity of the HFTs should manifest itself in the data very differently from the liquidity provision activity of the Market Makers. To establish the presence of these differences in the data, we test the following hypotheses:

Hypothesis H1: HFTs are more likely than Market Makers to aggressively execute the last 100 contracts before a price move in the direction of the trade. Market Makers are more likely than HFTs to have the last 100 resting contracts against which aggressive orders are executed.

Hypothesis H2: HFTs trade aggressively in the direction of the price move. Market Makers get run over by a price move.

Hypothesis H3: Both HFTs and Market Makers scratch trades, but HFTs scratch more.

To statistically test our “immediacy absorption” hypotheses against the “liquidity provision” hypotheses, we divide all of the trades during the 405 minute trading day into two subsets: Aggressive Buy trades and Aggressive Sell trades. Within each subset, we further aggregate multiple aggressive buy or sell transactions resulting from the execution of the same order into Aggressive Buy or Aggressive Sell sequences. The intuition is as follows. Often a specific trade is not a stand alone event, but a part of a sequence of transactions associated with the execution of the same order. For example, an order to aggressively sell 10 contracts may result in four Aggressive Sell transactions: for 2 contracts, 1 contract, 4 contracts, and 3 contracts, respectively, due to the specific sequence of resting bids against which this aggressive sell order was be executed. Using the order ID number, we are able to aggregate these four transactions into one Aggressive Sell sequence for 10 contracts.

Testing Hypothesis H1. Aggressive removal of the last 100 contracts by HFTs; passive provision of the last 100 resting contracts by the Market Makers. Using the Aggressive Buy sequences, we label as a “price increase event” all occurrences of trading sequences in which at least 100 contracts consecutively executed at the same price are followed by some number of contracts at a higher price. To examine indications of low latency, we focus on the the last 100 contracts traded before the price increase and the first 100 contracts at the next higher price (or fewer if the price changes again before 100 contracts are executed). Although we do not look directly at the limit order book data, price increase events are defined to capture occasions where traders use executable buy orders to lift the last remaining offers in the limit order book. Using Aggressive sell trades, we define “price decrease events” symmetrically as occurrences of sequences of trades in which 100 contracts executed at the same price are followed by executions at lower prices. These events are intended to capture occasions where traders use executable sell orders to hit the last few best bids in the limit order book. The results are presented in Table below

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For price increase and price decrease events, we calculate each of the six trader categories’ shares of Aggressive and Passive trading volume for the last 100 contracts traded at the “old” price level before the price increase or decrease and the first 100 contracts traded at the “new” price level (or fewer if the number of contracts is less than 100) after the price increase or decrease event.

Table above presents, for the six trader categories, volume shares for the last 100 contracts at the old price and the first 100 contracts at the new price. For comparison, the unconditional shares of aggressive and passive trading volume of each trader category are also reported. Table has four panels covering (A) price increase events on May 3-5, (B) price decrease events on May 3-5, (C) price increase events on May 6, and (D) price decrease events on May 6. In each panel there are six rows of data, one row for each trader category. Relative to panels A and C, the rows for Fundamental Buyers (BUYER) and Fundamental Sellers (SELLER) are reversed in panels B and D to emphasize the symmetry between buying during price increase events and selling during price decrease events. The first two columns report the shares of Aggressive and Passive contract volume for the last 100 contracts before the price change; the next two columns report the shares of Aggressive and Passive volume for up to the next 100 contracts after the price change; and the last two columns report the “unconditional” market shares of Aggressive and Passive sides of all Aggressive buy volume or sell volume. For May 3-5, the data are based on volume pooled across the three days.

Consider panel A, which describes price increase events associated with Aggressive buy trades on May 3-5, 2010. High Frequency Traders participated on the Aggressive side of 34.04% of all aggressive buy volume. Strongly consistent with immediacy absorption hypothesis, the participation rate rises to 57.70% of the Aggressive side of trades on the last 100 contracts of Aggressive buy volume before price increase events and falls to 14.84% of the Aggressive side of trades on the first 100 contracts of Aggressive buy volume after price increase events.

High Frequency Traders participated on the Passive side of 34.33% of all aggressive buy volume. Consistent with hypothesis, the participation rate on the Passive side of Aggressive buy volume falls to 28.72% of the last 100 contracts before a price increase event. It rises to 37.93% of the first 100 contracts after a price increase event.

These results are inconsistent with the idea that high frequency traders behave like textbook market makers, suffering adverse selection losses associated with being picked off by informed traders. Instead, when the price is about to move to a new level, high frequency traders tend to avoid being run over and take the price to the new level with Aggressive trades of their own.

Market Makers follow a noticeably more passive trading strategy than High Frequency Traders. According to panel A, Market Makers are 13.48% of the Passive side of all Aggressive trades, but they are only 7.27% of the Aggressive side of all Aggressive trades. On the last 100 contracts at the old price, Market Makers’ share of volume increases only modestly, from 7.27% to 8.78% of trades. Their share of Passive volume at the old price increases, from 13.48% to 15.80%. These facts are consistent with the interpretation that Market Makers, unlike High Frequency Traders, do engage in a strategy similar to traditional passive market making, buying at the bid price, selling at the offer price, and suffering losses when the price moves against them. These facts are also consistent with the hypothesis that High Frequency Traders have lower latency than Market Makers.

Intuition might suggest that Fundamental Buyers would tend to place the Aggressive trades which move prices up from one tick level to the next. This intuition does not seem to be corroborated by the data. According to panel A, Fundamental Buyers are 21.53% of all Aggressive trades but only 11.61% of the last 100 Aggressive contracts traded at the old price. Instead, Fundamental Buyers increase their share of Aggressive buy volume to 26.17% of the first 100 contracts at the new price.

Taking into account symmetry between buying and selling, panel B shows the results for Aggressive sell trades during May 3-5, 2010, are almost the same as the results for Aggressive buy trades. High Frequency Traders are 34.17% of all Aggressive sell volume, increase their share to 55.20% of the last 100 Aggressive sell contracts at the old price, and decrease their share to 15.04% of the last 100 Aggressive sell contracts at the new price. Market Makers are 7.45% of all Aggressive sell contracts, increase their share to only 8.57% of the last 100 Aggressive sell trades at the old price, and decrease their share to 6.58% of the last 100 Aggressive sell contracts at the new price. Fundamental Sellers’ shares of Aggressive sell trades behave similarly to Fundamental Buyers’ shares of Aggressive Buy trades. Fundamental Sellers are 20.91% of all Aggressive sell contracts, decrease their share to 11.96% of the last 100 Aggressive sell contracts at the old price, and increase their share to 24.87% of the first 100 Aggressive sell contracts at the new price.

Panels C and D report results for Aggressive Buy trades and Aggressive Sell trades for May 6, 2010. Taking into account symmetry between buying and selling, the results for Aggressive buy trades in panel C are very similar to the results for Aggressive sell trades in panel D. For example, Aggressive sell trades by Fundamental Sellers were 17.55% of Aggressive sell volume on May 6, while Aggressive buy trades by Fundamental Buyers were 20.12% of Aggressive buy volume on May 6. In comparison with the share of Fundamental Buyers and in comparison with May 3-5, the Flash Crash of May 6 is associated with a slightly lower – not higher – share of Aggressive sell trades by Fundamental Sellers.

The number of price increase and price decrease events increased dramatically on May 6, consistent with the increased volatility of the market on that day. On May 3-5, there were 4100 price increase events and 4062 price decrease events. On May 6 alone, there were 4101 price increase events and 4377 price decrease events. There were therefore approximately three times as many price increase events per day on May 6 as on the three preceding days.

A comparison of May 6 with May 3-5 reveals significant changes in the trading patterns of High Frequency Traders. Compared with May 3-5 in panels A and B, the share of Aggressive trades by High Frequency Traders drops from 34.04% of Aggressive buys and 34.17% of Aggressive sells on May 3-5 to 26.98% of Aggressive buy trades and 26.29% of Aggressive sell trades on May 6. The share of Aggressive trades for the last 100 contracts at the old price declines by even more. High Frequency Traders’ participation rate on the Aggressive side of Aggressive buy trades drops from 57.70% on May 3-5 to only 38.86% on May 6. Similarly, the participation rate on the Aggressive side of Aggressive sell trades drops from and 55.20% to 38.67%. These declines are largely offset by increases in the participation rate by Opportunistic Traders on the Aggressive side of trades. For example, Opportunistic Traders’ share of the Aggressive side of the last 100 contracts traded at the old price rises from 19.21% to 34.26% for Aggressive buys and from 20.99% to 33.86% for Aggressive sells. These results suggest that some Opportunistic Traders follow trading strategies for which low latency is important, such as index arbitrage, cross-market arbitrage, or opportunistic strategies mimicking market making.

Testing Hypothesis H2. HFTs trade aggressively in the direction of the price move; Market Makers get run over by a price move. To examine this hypothesis, we analyze whether High Frequency Traders use Aggressive trades to trade in the direction of contemporaneous price changes, while Market Makers use Passive trades to trade in the opposite direction from price changes. To this end, we estimate the regression equation

Δyt = α + Φ . Δyt-1 + δ . yt-1 + Σi=120i . Δpt-1 /0.25] + εt

(where yt and Δyt denote inventories and change in inventories of High Frequency Traders for each second of a trading day; t = 0 corresponds to the opening of stock trading on the NYSE at 8:30:00 a.m. CT (9:30:00 ET) and t = 24, 300 denotes the close of Globex at 15:15:00 CT (4:15 p.m. ET); Δpt denotes the price change in index point units between the high-low midpoint of second t-1 and the high-low midpoint of second t. Regressing second-by-second changes in inventory levels of High Frequency Traders on the level of their inventories the previous second, the change in their inventory levels the previous second, the change in prices during the current second, and lagged price changes for each of the previous 20 previous seconds.)

for Passive and Aggressive inventory changes separately.

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Table above presents the regression results of the two components of change in holdings on lagged inventory, lagged change in holdings and lagged price changes over one second intervals. Panel A and Panel B report the results for May 3-5 and May 6, respectively. Each panel has four columns, reporting estimated coefficients where the dependent variables are net Aggressive volume (Aggressive buys minus Aggressive sells) by High Frequency Traders (∆AHFT), net Passive volume by High Frequency Traders (∆PHFT), net Aggressive volume by Market Makers (∆AMM), and net Passive volume by Market Makers (∆PMM).

We observe that for lagged inventories (NPHFTt−1), the estimated coefficients for Aggressive and Passive trades by High Frequency Traders are δAHFT = −0.005 (t = −9.55) and δPHFT = −0.001 (t = −3.13), respectively. These coefficient estimates have the interpretation that High Frequency Traders use Aggressive trades to liquidate inventories more intensively than passive trades. In contrast, the results for Market Makers are very different. For lagged inventories (NPMMt−1), the estimated coefficients for Aggressive and Passive volume by Market Makers are δAMM = −0.002 (t = −6.73) and δPMM = −0.002 (t = −5.26), respectively. The similarity of these coefficients estimates has the interpretation that Market Makers favor neither Aggressive trades nor Passive trades when liquidating inventories.

For contemporaneous price changes (in the current second) (∆Pt−1), the estimated coefficient Aggressive and Passive volume by High Frequency Traders are β0 = 57.78 (t = 31.94) and β0 = −25.69 (t = −28.61), respectively. For Market Makers, the estimated coefficients for Aggressive and Passive trades are β0 = 6.38 (t = 18.51) and β0 = −19.92 (t = −37.68). These estimated coefficients have the interpretation that in seconds in which prices move up one tick, High Frequency traders are net buyers of about 58 contracts with Aggressive trades and net sellers of about 26 contracts with Passive trades in that same second, while Market Makers are net buyers of about 6 contracts with Aggressive trades and net sellers of about 20 contracts with Passive trades. High Frequency Traders and Market Makers are similar in that they both use Aggressive trades to trade in the direction of price changes, and both use Passive trades to trade against the direction of price changes. High Frequency Traders and Market Makers are different in that Aggressive net purchases by High Frequency Traders are greater in magnitude than the Passive net purchases, while the reverse is true for Market Makers.

For lagged price changes, coefficient estimates for Aggressive trades by High Frequency Traders and Market Makers are positive and statistically significant at lags 1-4 and lags 1-10, respectively. These results have the interpretation that both High Frequency Traders’ and Market Makers’ trade on recent price momentum, but the trading is compressed into a shorter time frame for High Frequency Traders than for Market Makers.

For lagged price changes, coefficient estimates for Passive volume by High Frequency Traders and Market Makers are negative and statistically significant at lags 1 and lags 1-3, respectively. Panel B of Table presents results for May 6. Similar to May 3-5, High Frequency Traders tend to use Aggressive trades more intensely than Passive trades to liquidate inventories, while Market Makers do not show this pattern. Also similar to May 3-5, High Frequency Trades and Market makers use Aggressive trades to trade in the contemporaneous direction of price changes and use Passive trades to trade in the direction opposite price changes, with Aggressive trading greater than Passive trading for High Frequency Traders and the reverse for Market Makers. In comparison with May 3-5, the coefficients are smaller in magnitude on May 6, indicating reduced liquidity at each tick. For lagged price changes, the coefficients associated with Aggressive trading by High Frequency Traders change from positive to negative at lags 1-4, and the positive coefficients associated with Aggressive trading by Market Makers change from being positive and statistically significant at lags lags 1-10 to being positive and statistically significant only at lags 1-3. These results illustrate accelerated trading velocity in the volatile market conditions of May 6.

We further examine how high frequency trading activity is related to market prices. Figure below illustrates how prices change after HFT trading activity in a given second. The upper-left panel presents results for buy trades for May 3-5, the upper right panel presents results for buy trades on May 6, and the lower-left and lower-right present corresponding results for sell trades. For an “event” second in which High Frequency Traders are net buyers, net Aggressive Buyers, and net Passive Buyers value-weighted average prices paid by the High Frequency Traders in that second are subtracted from the value-weighted average prices for all trades in the same second and each of the following 20 seconds. The results are averaged across event seconds, weighted by the magnitude of High Frequency Traders’ net position change in the event second. The upper-left panel presents results for May 3-5, the upper-right panel presents results for May 6, and the lower two panels present results for sell trades calculated analogously. Price differences on the vertical axis are scaled so that one unit equals one tick ($12.50 per contract).

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When High Frequency Traders are net buyers on May 3-5, prices rise by 17% of a tick in the next second. When HFTs execute Aggressively or Passively, prices rise by 20% and 2% of a tick in the next second, respectively. In subsequent seconds, prices in all cases trend downward by about 5% of a tick over the subsequent 19 seconds. For May 3-5, the results are almost symmetric for selling.

When High Frequency Traders are buying on May 6, prices increase by 7% of a tick in the next second. When they are aggressive buyers or passive buyers, prices increase by increase 25% of a tick or decrease by 5% of a tick in the next second, respectively. In subsequent seconds, prices generally tend to drift downwards. The downward drift is especially pronounced after Passive buying, consistent with the interpretation that High Frequency Traders were “run over” when their resting limit buy orders were “run over” in the down phase of the Flash Crash. When High Frequency Traders are net sellers, the results after one second are analogous to buying. After aggressive selling, prices continue to drift down for 20 seconds, consistent with the interpretation that High Frequency Traders made profits from Aggressive sales during the down phase of the Flash Crash.

Testing Hypothesis H3. Both HFTs and Market Makers scratch trades; HFTs scratch more. A textbook market maker will try to buy at the bid price, sell at the offer price, and capture the bid-ask spread as a profit. Sometimes, after buying at the bid price, market prices begin to fall before the market maker can make a one tick profit by selling his inventory at the best offer price. To avoid taking losses in this situation, one component of a traditional market making strategy is to “scratch trades in the presence of changing market conditions by quickly liquidating a position at the same price at which it was acquired. These scratched trades represent inventory management trades designed to lower the cost of adverse selection. Since many competing market makers may try to scratch trades at the same time, traders with the lowest latency will tend to be more successful in their attempts to scratch trades and thus more successful in their ability to avoid losses when market conditions change.

To examine whether and to what extent traders engage in trade scratching, we sequence each trader’s trades for the day using audit trail sequence numbers which not only sort trades by second but also sort trades chronologically within each second. We define an “immediately scratched trade” as a trade with the properties that the next trade in the sorted sequence (1) occurred in the same second, (2) was executed at the same price, (3) was in the opposite direction, i.e., buy followed by sell or sell followed by buy. For each of the trading accounts in our sample, we calculate the number of immediately scratched trades, then compare the number of scratched trades across the six trader categories.

The results of this analysis are presented in the table below. Panel A provides results for May 3-5 and panel B for May 6. In each panel, there are five rows of data, one for each trader category. The first three columns report the total number of trades, the total number of immediately scratched trades, and the percentage of trades that are immediately scratched by traders in five categories. For May 3-6, the reported numbers are from the pooled data.

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This table presents statistics for immediate trade scratching which measures how many times a trader changes his/her direction of trading in a second aggregated over a day. We define a trade direction change as a buy trade right after a sell trade or vice versa at the same price level in the same second.

This table shows that High Frequency Traders scratched 2.84 % of trades on May 3-5 and 4.26 % on May 6; Market Makers scratched 2.49 % of trades on May 3-5 and 5.53 % of trades on May 6. While the percentages of immediately scratched trades by Market Makers is slightly higher than that for High Frequency Traders on May 6, the percentages for both groups are very similar. The fourth, fifth, and sixth columns of the Table report the mean, standard deviation, and median of the number of scratched trades for the traders in each category.

Although the percentages of scratched trades are similar, the mean number of immediately scratched trades by High Frequency Traders is much greater than for Market Makers: 540.56 per day on May 3-5 and 1610.75 on May 6 for High Frequency Traders versus 13.35 and 72.92 for Market Makers. The differences between High Frequency Traders and Market Makers reflect differences in volume traded. The Table shows that High Frequency Traders and Market Makers scratch a significantly larger percentage of their trades than other trader categories.