The Banking Business…Note Quote

retailandcommercialbanking

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….

 

Game Theory and Finite Strategies: Nash Equilibrium Takes Quantum Computations to Optimality.

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Finite games of strategy, within the framework of noncooperative quantum game theory, can be approached from finite chain categories, where, by finite chain category, it is understood a category C(n;N) that is generated by n objects and N morphic chains, called primitive chains, linking the objects in a specific order, such that there is a single labelling. C(n;N) is, thus, generated by N primitive chains of the form:

x0 →f1 x1 →f2 x1 → … xn-1 →fn xn —– (1)

A finite chain category is interpreted as a finite game category as follows: to each morphism in a chain xi-1 →fi xi, there corresponds a strategy played by a player that occupies the position i, in this way, a chain corresponds to a sequence of strategic choices available to the players. A quantum formal theory, for a finite game category C(n;N), is defined as a formal structure such that each morphic fundament fi of the morphic relation xi-1 →fi xis a tuple of the form:

fi := (Hi, Pi, Pˆfi) —– (2)

where Hi is the i-th player’s Hilbert space, Pi is a complete set of projectors onto a basis that spans the Hilbert space, and Pˆfi ∈ Pi. This structure is interpreted as follows: from the strategic Hilbert space Hi, given the pure strategies’ projectors Pi, the player chooses to play Pˆfi .

From the morphic fundament (2), an assumption has to be made on composition in the finite category, we assume the following tensor product composition operation:

fj ◦ fi = fji —– (3)

fji = (Hji = Hj ⊗ Hi, Pji = Pj ⊗ Pi, Pˆfji = Pˆfj ⊗ Pˆfi) —– (4)

From here, a morphism for a game choice path could be introduced as:

x0 →fn…21 xn —– (5)

fn…21 = (HG = ⊗i=n1 Hi, PG = ⊗i=n1 Pi, Pˆ fn…21 = ⊗i=n1fi) —– (6)

in this way, the choices along the chain of players are completely encoded in the tensor product projectors Pˆfn…21. There are N = ∏i=1n dim(Hi) such morphisms, a number that coincides with the number of primitive chains in the category C(n;N).

Each projector can be addressed as a strategic marker of a game path, and leads to the matrix form of an Arrow-Debreu security, therefore, we call it game Arrow-Debreu projector. While, in traditional financial economics, the Arrow-Debreu securities pay one unit of numeraire per state of nature, in the present game setting, they pay one unit of payoff per game path at the beginning of the game, however this analogy may be taken it must be addressed with some care, since these are not securities, rather, they represent, projectively, strategic choice chains in the game, so that the price of a projector Pˆfn…21 (the Arrow-Debreu price) is the price of a strategic choice and, therefore, the result of the strategic evaluation of the game by the different players.

Now, let |Ψ⟩ be a ket vector in the game’s Hilbert space HG, such that:

|Ψ⟩ = ∑fn…21 ψ(fn…21)|(fn…21⟩ —– (7)

where ψ(fn…21) is the Arrow-Debreu price amplitude, with the condition:

fn…21 |ψ(fn…21)|2 = D —– (8)

for D > 0, then, the |ψ(fn…21)|corresponds to the Arrow-Debreu prices for the game path fn…21 and D is the discount factor in riskless borrowing, defining an economic scale for temporal connections between one unit of payoff now and one unit of payoff at the end of the game, such that one unit of payoff now can be capitalized to the end of the game (when the decision takes place) through a multiplication by 1/D, while one unit of payoff at the end of the game can be discounted to the beginning of the game through multiplication by D.

In this case, the unit operator, 1ˆ = ∑fn…21 Pˆfn…21 has a similar profile as that of a bond in standard financial economics, with ⟨Ψ|1ˆ|Ψ⟩ = D, on the other hand, the general payoff system, for each player, can be addressed from an operator expansion:

πiˆ = ∑fn…21 πi (fn…21) Pˆfn…21 —– (9)

where each weight πi(fn…21) corresponds to quantities associated with each Arrow-Debreu projector that can be interpreted as similar to the quantities of each Arrow-Debreu security for a general asset. Multiplying each weight by the corresponding Arrow-Debreu price, one obtains the payoff value for each alternative such that the total payoff for the player at the end of the game is given by:

⟨Ψ|1ˆ|Ψ⟩ = ∑fn…21 πi(fn…21) |ψ(fn…21)|2/D —– (10)

We can discount the total payoff to the beginning of the game using the discount factor D, leading to the present value payoff for the player:

PVi = D ⟨Ψ|πiˆ|Ψ⟩ = D ∑fn…21 π (fn…21) |ψ(fn…21)|2/D —– (11)

, where π (fn…21) represents quantities, while the ratio |ψ(fn…21)|2/D represents the future value at the decision moment of the quantum Arrow- Debreu prices (capitalized quantum Arrow-Debreu prices). Introducing the ket

|Q⟩ ∈ HG, such that:

|Q⟩ = 1/√D |Ψ⟩ —– (12)

then, |Q⟩ is a normalized ket for which the price amplitudes are expressed in terms of their future value. Replacing in (11), we have:

PVi = D ⟨Q|πˆi|Q⟩ —– (13)

In the quantum game setting, the capitalized Arrow-Debreu price amplitudes ⟨fn…21|Q⟩ become quantum strategic configurations, resulting from the strategic cognition of the players with respect to the game. Given |Q⟩, each player’s strategic valuation of each pure strategy can be obtained by introducing the projector chains:

Cˆfi = ∑fn…i+1fi-1…1 Pˆfn…i+1 ⊗ Pˆfi ⊗ Pˆfi-1…1 —– (14)

with ∑fi Cˆfi = 1ˆ. For each alternative choice of the player i, the chain sums over all of the other choice paths for the rest of the players, such chains are called coarse-grained chains in the decoherent histories approach to quantum mechanics. Following this approach, one may introduce the pricing functional from the expression for the decoherence functional:

D (fi, gi : |Q⟩) = ⟨Q| Cˆfi Cgi|Q⟩  —– (15)

we, then have, for each player

D (fi, gi : |Q⟩) = 0, ∀ fi ≠ gi —– (16)

this is the usual quantum mechanics’ condition for an aditivity of measure (also known as decoherence condition), which means that the capitalized prices for two different alternative choices of player i are additive. Then, we can work with the pricing functional D(fi, fi :|Q⟩) as giving, for each player an Arrow-Debreu capitalized price associated with the pure strategy, expressed by fi. Given that (16) is satisfied, each player’s quantum Arrow-Debreu pricing matrix, defined analogously to the decoherence matrix from the decoherent histories approach, is a diagonal matrix and can be expanded as a linear combination of the projectors for each player’s pure strategies as follows:

Di (|Q⟩) = ∑fi D(fi, f: |Q⟩) Pˆfi —– (17)

which has the mathematical expression of a mixed strategy. Thus, each player chooses from all of the possible quantum computations, the one that maximizes the present value payoff function with all the other strategies held fixed, which is in agreement with Nash.

Ramping the Markets: Banking on Ponzi Finance. Thought of the Day 112.0

China Minsky

When funded pension schemes were first put forward at the beginning of the 1970s as a private sector alternative to state earnings-related pensions, the merchant (investment) banks and stockbroking firms that promoted them did not anticipate how successful they would become in that, by the following decades, pension schemes and allied forms of life assurance would come to own most of the stocks and shares quoted on the world’s stock markets. When pension funds held a minority of stocks, the funds could altogether put money into stock markets by buying stocks, or withdraw it by selling, without significantly affecting prices or the liquidity of the market as a whole. Now that pension funds and allied life assurance and mutual funds constitute the majority of the market, it is not possible for them to withdraw funds altogether without causing a fall in prices, or even a stock market crash.

Because of their success, pension funds have become the newest and possibly the most catastrophic example of Ponzi finance. The term Ponzi finance was invented by the American economist Hyman P. Minsky as part of his analysis of financial market inflation. It describes a form of finance in which new liabilities are issued to finance existing liabilities. According to Minsky, financial crises are caused by the collapse of ‘financial structures’ whose failure is precipitated by their increasing ‘financial fragility’. Financial structures are simply commitments to make payments in the future, against claims that result in incoming payments in the future. For Minsky, the characteristic feature of financial markets and financial speculation is that they afford opportunities for economic units to enter into future financial commitments, in the expectation of gain. In this respect, at least, they are similar to fixed capital investment. Success in securing gains persuades speculators to enter into further commitments, which become more ‘fragile’, i.e., more prone to collapse because future commitments become more speculative and less covered by assured financial inflows.

Minsky identifies three types of financial commitments, which are distinguished by the different degree of financial risk that they entail. In hedge finance, future commitments are exactly matched by cash inflows. A common example is the practice in banking of lending at variable or floating rates of interest. In this way, if a bank has to pay more interest to its depositors, it can recoup the increase by raising the interest rates that it charges to its borrowers (assuming that its depositors cannot withdraw their deposits before the term of the loan expires).

Speculative finance is characterized by certain commitments which have to be covered by cash inflows which may rise or fall, or uncertain commitments against a fixed cash inflow. If a bank lends money at a fixed rate of interest it is engaging in speculative finance, because it is running the risk that it may have to pay a higher rate of interest to depositors if interest rates rise. However, to set against this risk it has the possibility that the interest rates paid to depositors may fall, and it will thereby make additional gains from a wider margin between lending and borrowing rates.

Ponzi finance, in Minsky’s view, is a situation in which both commitments and cash inflows are uncertain, so that there is a possibility of an even more enhanced profit if commitments fall and the cash inflow rises. Against this has to be set the possibility that commitments and the cash inflow will move together so that only a minimal profit will be secured, or that commitments will rise and the cash inflow will fall, in which case a much more serious loss will be entered than would have occurred under speculative finance.

Ponzi finance lies behind the view that is no less erroneous for being widely repeated, that a higher return reflects the ‘greater risk’ of an enterprise. This is exemplified in the practice of banks charging higher rates of interest for what they perceive as greater risks. Behind this view lies the Austrian tradition, from Böhm-Bawerk onwards, of regarding economic outcomes as analogous to the gains and lotteries obtainable from repeated routine games, such as the tossing of a dice. The certain pay-off (or ‘certainty-equivalent’) is held to be lower than some possible pay-off. The association of the greater payoff with its lower probability then leads to a presumption that the latter ‘causes’ the former. However, the profits of companies and financial institutions are not the proceeds of gaming, however much enterprise in an unstable market system may appear similar to gambling. In fact, these profits are the outcomes of financial flows that ebb and progress through the economy, propelled by actual expenditure and financing decisions. The systems of financial claims and liabilities arising from those decisions become more fragile, as first speculative and then Ponzi financing structures come to predominate, and larger gains and larger losses may then be made. But the possibility of extraordinary profits or losses in Ponzi financing structures in no way means that realization of such profits is caused or justified by the possibility of the losses. Ponzi finance arises out of objective contractual obligations. The ‘greater risk’, which is held to justify a higher cost of finance, may be entirely subjective or a cover for monopoly profits in finance.

The simplest example of Ponzi finance is borrowing money to pay interest on a loan. In this case, the financial liability is increased, with no reduction in the original loan. Pyramid bank deposit schemes were the schemes after which this phenomenon is named, and they are perhaps the most extreme example of such financial structures. In a pyramid deposit scheme, the financier might take, say, Rs. 100 from a depositor, and promise to double this money after a month if the depositor introduces two new depositors at the end of that month. The two new depositors get the same terms and when they pay in their Rs. 100 respectively, Rs. 100 goes to double the money of the first depositor, and the other Rs. 100 is the financier’s profit. The two new depositors get their profit at the end of the next month from the new deposits paid in by the four new depositors that they introduce to the scheme, and so on. Initially, such schemes promise and deliver good profits. But their flaw lies in the fact that they require deposits to rise exponentially in order to pay the promised rewards to depositors. In the example that is described above, deposits have to double each month so that after one year, the scheme requires Rs. 409,600 in deposits just to keep solvent. After the thirteenth month, Rs. 819,200 would need to be deposited to keep up promised payments to depositors. Such schemes therefore usually collapse when they run out of gullible people to deposit their savings in them. While their life can be briefly extended by persuading depositors not to withdraw their profits, this cannot work for more than one or two payment periods, because such schemes are so dependent on increasing amounts of additional money being paid into them in each successive period.

Ponzi schemes are named after Charles Ponzi, an Italian immigrant who swindled Boston investors in 1919 and 1920 with a pyramid banking scheme. Minsky noted that Ponzi’s scheme ‘swept through the working classes and even affected “respectable” folk’. Because they prey on the poor and the ignorant, Ponzi schemes in banking are usually banned, although this does not prevent them from occurring in countries where it is difficult to regulate them. In Minsky’s view, financial collapses occur because booms in financial markets result in sufficient profits for speculative and Ponzi finance to induce a shift from hedge finance to speculative and Ponzi finance.

Ponzi finance in securities markets is much more common than in banking. Indeed, it is arguable that such finance is essential for the liquidity of markets in long-term securities: if a security is bought, it may have an assured ‘residual liquidity’ if it is a bond in that, when it matures, the money will then be returned to the investor. If, however, the security is a share which is not repaid by the issuer except on liquidation of the company, then it has no assured residual liquidity. Its liquidity depends on some other investor wishing to buy it at a reasonable price. If the share is to be sold at a profit, then the condition for this to happen is that demand for it has risen since it was bought. In this respect, liquidity and capital gains in the markets for long-term securities depend on Ponzi finance.

Ponzi finance was present at the very inception of modern stock markets. The South Sea Company and the Mississippi Company, whose stocks featured in the first stock market collapse of 1720, both ended up issuing stocks to raise finance in order to buy stocks to keep the market in their stocks liquid and stable. In modern times, this is a common feature of merger and takeover activity in capital markets. Corporate takeovers are frequently financed by issuing securities. The proceeds of the new issue are used to buy in the target company’s stock ‘at a premium’, i.e., at a price that is greater than the pre-takeover market price. The money raised by issuing the new stocks is used to pay the higher return to the stock-holders of the company being taken over. In this case, issuing new stock is exactly equivalent to the pyramid banking practice of taking in new deposits in order to pay an enhanced return to older depositors, which is the premium on the target company’s stock. The main difference between the two types of operation is that, during such takeover activity, the stock market as a whole functions as a pyramid banking scheme. However, precisely because it is the market as a whole which is operating in this Ponzi way, the pyramid nature of the venture is less obvious, and the gains are greater, because more and wealthier contributors are involved. Since this is an outcome of the normal functioning of the market, which may hitherto have been acting in a perfectly proper and respectable fashion, raising finance for industry and providing for widows and orphans, it is not possible to ‘finger’ the perpetrator of the pyramid scheme.

A more obviously controversial kind of Ponzi finance is the practice known as ‘ramping’ the market. A financier discreetly buys up a particular stock over a period of time, and then announces with great fanfare that he or she is buying in the stock. There are few markets in which emulatory competition is as strong as financial markets, where being conservative in practice and faddish in innovation are preconditions for a ‘sound’ reputation. The ‘sounder’ that reputation, the more likely it is other investors will imitate the buying strategy. Indeed, there is an element of compulsion about it, depending on the reputation of the investor. Those investors without reputation must follow for whatever reasons the investment direction signalled by investors with reputation, or else languish among lower-growth stocks. As the price of the stock rises due to the increased demand for it, such reputable financiers quietly sell out at a profit to their imitators, thereby confirming their reputation for financial ‘soundness’. Obviously, the better the reputation of the financier, the greater the gain from such an operation. To support such a reputation and legitimize the profits from trading on it, financiers will obviously attribute the gains from this practice to their own financial acumen, rather than confessing to having ramped the market.

The almost instantaneous dissemination of relevant information on which modern financial markets pride themselves (and which many financial economists believe makes them near perfect), also facilitates this kind of market manipulation. In securities markets, the investors emulating the financier are the equivalent of the new depositors. They too may make money, if they too can persuade subsequent new investors to buy at higher prices. As with the pyramid banking case, ramping markets depends on increasing interest by additional investors. Because in practice it is indistinguishable from normal trading (unlike pyramid banking, which is rather more obvious), and because any losers usually have other wealth to fall back on, such practices are frowned upon in securities markets, but cannot be eliminated. However, in the case of pension funds, the eventual losers will be ordinary working people, who will only have a minimal state pension in the future to fall back on. This makes it all the more important to understand how a reputable system for financing pensions has become a Ponzi finance scheme which will in future collapse.

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.

A Monetary Drain due to Excess Liquidity. Why is the RBI Playing Along

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And so we thought demonetization was not a success. Let me begin with the Socratic irony to assume that it was indeed a success, albeit not in arresting black money for sure. Yes, the tax net has widened and the cruelty of smashing down the informal sector to smithereens to be replaceable with a formal economy, more in the manner of sucking the former into the latter has been achieved. As far as terror funding is concerned, it is anybody’s guess and so let them be with their imaginations. What none can deny is the surge in deposits and liquidity in the wake of demonetization. But, what one has been consciously, or through an ideological-driven standpoint denying is the fact that demonetization clubbed with the governmental red carpet for foreign direct investment has been an utter failure to attract money into the country. And the reason attributed for the same has been a dip in the economy as a result of the idiosyncratic decision of November 8 added with the conjuring acts of mathematics and statistics in tweaking base years to let go off the reality behind a depleting GDP and project the country as the fastest growing emerging economy in the world. The irony I started off with is defeated here, for none of the claims that the government propaganda machine churns out on the assembly line are in fact anywhere near the truth. But, thats what a propaganda is supposed to doing, else why even call it that, or even call for a successful governance and so on and on (sorry for the Žižekian interjections here).

Assuming the irony still has traces and isn’t vanquished, it is time to move on and look into the effects of what calls for a financial reality-check. Abruptly going vertically through the tiers here, it is recently been talked about in the corridors of financial power that the Reserve Bank of India (RBI) is all set to drain close to 1.5 lakh crore in excess liquidity from the financial system as surging foreign investments forces the central bank to absorb the dollar inflows and sell rupees to cap gains in the local currency. This is really interesting, for the narrative or the discourse is again symptomatic of what the government wants us to believe, and so believe we shall, or shall we? After this brief stopover, chugging off again…Foreign investments into debt and shares have reached a net $31 billion this year, compared with $2.7 billion in sales last year, due to factors including India’s low inflation and improving economic growth. This is not merely a leap, but a leap of faith, in this case numerically. Yes, India is suffering from low inflation, but it ain’t deflation, but rather disinflation. There is a method to this maddening reason, if one needs to counter what gets prime time economic news in the media or passes on as Chinese Whispers amongst activists hell-bent on proving the futility of the governmental narrative. There is nothing wrong in the procedure as long as this hell-bent-ness is cooked in proper proportions of reason. But, why call it disinflation and not deflation? A sharp drop in inflation below the Reserve Bank of India’s (RBI’s) 4% target has been driven by only two items – pulses and vegetables. the consumer price index (CPI), excluding pulses and vegetables, rose at the rate of 3.8% in July, much higher than the official headline figure of 2.4% inflation for the month. The re-calculated CPI is based on adjusted weights after excluding pulses and vegetables from the basket of goods and services. The two farm items – pulses and vegetables – have a combined weight of only 8.4% in the consumer price index (CPI) basket. However, they have wielded disproportionate influence over the headline inflation number for more than a year now owing to the sharp volatility in their prices. So, how does it all add up? Prices of pulses and vegetables have fallen significantly this year owing to increased supply amid a normal monsoon last year, as noted by the Economic Survey. The high prices of pulses in the year before and the government’s promises of more effective procurement may have encouraged farmers to produce more last year, resulting in a glut. Demonetisation may have added to farmers’ woes by turning farm markets into buyers’ markets. Thus, there does not seem to be any imminent threat of deflation in India. A more apt characterization of the recent trends in prices may be ‘disinflation’ (a fall in the inflation rate) rather than deflation (falling prices) given that overall inflation, excluding pulses and vegetables, is close to the RBI target of 4%. On the topicality of improving economic growth in the country, this is the bone of contention either weakening or otherwise depending on how the marrow is key up.

Moving on…The strong inflows have sent the rupee up nearly 7 per cent against the dollar and forced the RBI to buy more than $10 billion in spot market and $10 billion in forwards this year – which has meant an equivalent infusion in rupees. Those rupee sales have added liquidity into a financial system already flush with cash after a ban on higher-denomination currency in November sparked a surge in bank deposits. Average daily liquidity has risen to around Rs 3 lakh crore, well above the RBI’s goal of around Rs 1 lakh crore, according to traders. That will force the RBI to step up debt sales to remove liquidity and avoid any inflationary impact. Traders estimate the RBI will need to drain Rs 1 lakh crore to Rs 1.4 lakh crore ($15.7 billion to $22 billion) after taking into account factors such as festival-related consumer spending that naturally reduce cash in the system. How the RBI drains the cash will thus become an impact factor for bond traders, who have benefitted from a rally in debt markets. The RBI has already drained about Rs 1 lakh crore via one-year bills under a special market stabilisation scheme (MSS), as well as Rs 30,000 crore in longer debt through open market sales. MSS (Market Stabilisation Scheme) securities are issued with the objective of providing the RBI with a stock of securities with which it can intervene in the market for managing liquidity. These securities are issued not to meet the government’s expenditure. The MSS scheme was launched in April 2004 to strengthen the RBI’s ability to conduct exchange rate and monetary management. The bills/bonds issued under MSS have all the attributes of the existing treasury bills and dated securities. These securities will be issued by way of auctions to be conducted by the RBI. The timing of issuance, amount and tenure of such securities will be decided by the RBI. The securities issued under the MSS scheme are matched by an equivalent cash balance held by the government with the RBI. As a result, their issuance will have a negligible impact on the fiscal deficit of the government. It is hoped that the procedure would continue, noting staggered sales in bills, combined with daily reverse repo operations and some long-end sales, would be easily absorbable in markets. The most disruptive fashion would be stepping up open market sales, which tend to focus on longer-ended debt. That may send yields higher and blunt the impact of the central bank’s 25 basis point rate cut in August. The RBI does not provide a timetable of its special debt sales for the year. and if the RBI drains the cash largely through MSS bonds then markets wont get too much impacted. This brings us to close in proving the success story of demonetization as a false beacon, in that with a surge in liquidity, the impact on the market would be negligible if MSS are resorted to culminating in establishing the fact that demonetization clubbed with red-carpeted FDI has had absolutely no nexus in the influx of dollars and thus any propaganda of this resulting as a success story of demonetization is to be seen as purely rhetoric. QED.

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

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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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Momentum of Accelerated Capital. Note Quote.

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Distinct types of high frequency trading firms include independent proprietary firms, which use private funds and specific strategies which remain secretive, and may act as market makers generating automatic buy and sell orders continuously throughout the day. Broker-dealer proprietary desks are part of traditional broker-dealer firms but are not related to their client business, and are operated by the largest investment banks. Thirdly hedge funds focus on complex statistical arbitrage, taking advantage of pricing inefficiencies between asset classes and securities.

Today strategies using algorithmic trading and High Frequency Trading play a central role on financial exchanges, alternative markets, and banks‘ internalized (over-the-counter) dealings:

High frequency traders typically act in a proprietary capacity, making use of a number of strategies and generating a very large number of trades every single day. They leverage technology and algorithms from end-to-end of the investment chain – from market data analysis and the operation of a specific trading strategy to the generation, routing, and execution of orders and trades. What differentiates HFT from algorithmic trading is the high frequency turnover of positions as well as its implicit reliance on ultra-low latency connection and speed of the system.

The use of algorithms in computerised exchange trading has experienced a long evolution with the increasing digitalisation of exchanges:

Over time, algorithms have continuously evolved: while initial first-generation algorithms – fairly simple in their goals and logic – were pure trade execution algos, second-generation algorithms – strategy implementation algos – have become much more sophisticated and are typically used to produce own trading signals which are then executed by trade execution algos. Third-generation algorithms include intelligent logic that learns from market activity and adjusts the trading strategy of the order based on what the algorithm perceives is happening in the market. HFT is not a strategy per se, but rather a technologically more advanced method of implementing particular trading strategies. The objective of HFT strategies is to seek to benefit from market liquidity imbalances or other short-term pricing inefficiencies.

While algorithms are employed by most traders in contemporary markets, the intense focus on speed and the momentary holding periods are the unique practices of the high frequency traders. As the defence of high frequency trading is built around the principles that it increases liquidity, narrows spreads, and improves market efficiency, the high number of trades made by HFT traders results in greater liquidity in the market. Algorithmic trading has resulted in the prices of securities being updated more quickly with more competitive bid-ask prices, and narrowing spreads. Finally HFT enables prices to reflect information more quickly and accurately, ensuring accurate pricing at smaller time intervals. But there are critical differences between high frequency traders and traditional market makers:

  1. HFT do not have an affirmative market making obligation, that is they are not obliged to provide liquidity by constantly displaying two sides quotes, which may translate into a lack of liquidity during volatile conditions.
  2. HFT contribute little market depth due to the marginal size of their quotes, which may result in larger orders having to transact with many small orders, and this may impact on overall transaction costs.
  3. HFT quotes are barely accessible due to the extremely short duration for which the liquidity is available when orders are cancelled within milliseconds.

Besides the shallowness of the HFT contribution to liquidity, are the real fears of how HFT can compound and magnify risk by the rapidity of its actions:

There is evidence that high-frequency algorithmic trading also has some positive benefits for investors by narrowing spreads – the difference between the price at which a buyer is willing to purchase a financial instrument and the price at which a seller is willing to sell it – and by increasing liquidity at each decimal point. However, a major issue for regulators and policymakers is the extent to which high-frequency trading, unfiltered sponsored access, and co-location amplify risks, including systemic risk, by increasing the speed at which trading errors or fraudulent trades can occur.

Although there have always been occasional trading errors and episodic volatility spikes in markets, the speed, automation and interconnectedness of today‘s markets create a different scale of risk. These risks demand that exchanges and market participants employ effective quality management systems and sophisticated risk mitigation controls adapted to these new dynamics in order to protect against potential threats to market stability arising from technology malfunctions or episodic illiquidity. However, there are more deliberate aspects of HFT strategies which may present serious problems for market structure and functioning, and where conduct may be illegal, for example in order anticipation seeks to ascertain the existence of large buyers or sellers in the marketplace and then to trade ahead of those buyers and sellers in anticipation that their large orders will move market prices. A momentum strategy involves initiating a series of orders and trades in an attempt to ignite a rapid price move. HFT strategies can resemble traditional forms of market manipulation that violate the Exchange Act:

  1. Spoofing and layering occurs when traders create a false appearance of market activity by entering multiple non-bona fide orders on one side of the market at increasing or decreasing prices in order to induce others to buy or sell the stock at a price altered by the bogus orders.
  2. Painting the tape involves placing successive small amount of buy orders at increasing prices in order to stimulate increased demand.

  3. Quote Stuffing and price fade are additional HFT dubious practices: quote stuffing is a practice that floods the market with huge numbers of orders and cancellations in rapid succession which may generate buying or selling interest, or compromise the trading position of other market participants. Order or price fade involves the rapid cancellation of orders in response to other trades.

The World Federation of Exchanges insists: ― Exchanges are committed to protecting market stability and promoting orderly markets, and understand that a robust and resilient risk control framework adapted to today‘s high speed markets, is a cornerstone of enhancing investor confidence. However this robust and resilient risk control framework‘ seems lacking, including in the dark pools now established for trading that were initially proposed as safer than the open market.

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.

Speculations

swing-trading

Any system that uses only single asset price (and possibly prices of multiple assets, but this case is not completely clear) as input. The price is actually secondary and typically fluctuates few percent a day in contrast with liquidity flow, that fluctuates in orders of magnitude. This also allows to estimate maximal workable time scale: the scale on which execution flow fluctuates at least in an order of magnitude (in 10 times).

Any system that has a built-in fixed time scale (e.g. moving average type of system). The market has no specific time scale.

Any “symmetric” system with just two signals “buy” and “sell” cannot make money. Minimal number of signals is four: “buy”, “sell position”, “sell short”, “cover short”. The system where e.g. “buy” and “cover short” is the same signal will eventually catastrophically lose money on an event when market go against position held. Short covering is buying back borrowed securities in order to close an open short position. Short covering refers to the purchase of the exact same security that was initially sold short, since the short-sale process involved borrowing the security and selling it in the market. For example, assume you sold short 100 shares of XYZ at $20 per share, based on your view that the shares were headed lower. When XYZ declines to $15, you buy back 100 shares of XYZ in the market to cover your short position (and pocket a gross profit of $500 from your short trade).

Any system entering the position (does not matter long or short) during liquidity excess (e.g. I > IIH) cannot make money. During liquidity excess price movement is typically large and “reverse to the moving average” type of system often use such event as position entering signal. The market after liquidity excess event bounces a little, then typically goes to the same direction. This give a risk of on what to bet: “little bounce” or “follow the market”. What one should do during liquidity excess event is to CLOSE existing position. This is very fundamental – if you have a position during market uncertainty – eventually you will lose money, you must have ZERO position during liquidity excess. This is very important element of the P&L trading strategy.

Any system not entering the position during liquidity deficit event (e.g. I < IIL) typically lose money. Liquidity deficit periods are characterized by small price movements and difficult to identify by price-based trading systems. Liquidity deficit actually means that at current price buyers and sellers do not match well, and substantial price movement is expected. This is very well known by most traders: before large market movement volatility (and e.g. standard deviation as its crude measure) becomes very low. The direction (whether one should go long or short) during liquidity deficit event can, to some extent, be determined by the balance of supply–demand generalization.

An important issue is to discuss is: what would happen to the markets when this strategy (enter on liquidity deficit, exit on liquidity excess) is applied on mass scale by market participants. In contrast with other trading strategies, which reduce liquidity at current price when applied (when price is moved to the uncharted territory, the liquidity drains out because supply or demand drains ), this strategy actually increases market liquidity at current price. This insensitivity to price value is expected to lead not to the strategy stopping to work when applied on mass scale by market participants, but starting to work better and better and to markets’ destabilization in the end.

 

Fiscal Responsibility and Budget Management (FRBM) Act

The Government appointed a five-member Committee in May 2016, to review the Fiscal Responsibility and Budget Management (FRBM) Act and to examine a changed format including flexible FRBM targets. The Committee formation was announced during the 2016-17 budget by FM Arun Jaitely. The Panel was headed by the former MP and former Revenue and Expenditure Secretary NK Singh and included four other members, CEA Arvind Subramanian, former Finance Secretary Sumit Bose, the then Deputy Governor and present governor of the RBI Urjit Patel and Nathin Roy. There was a difference of opinion about the need for adopting a fixed FRBM target like fiscal deficit, and the divisive opinion lay precisely in not following through such a fixity in times when the government had to spend high to fight recession and support economic growth. The other side of the camp argued it being necessary to inculcate a feeling of fiscal discipline. During Budget speech in 2016, Mr Jaitley expressed this debate:

There is now a school of thought which believes that instead of fixed numbers as fiscal deficit targets, it may be better to have a fiscal deficit range as the target, which would give necessary policy space to the government to deal with dynamic situations. There is also a suggestion that fiscal expansion or contraction should be aligned with credit contraction or expansion, respectively, in the economy.

The need for a flexible FRBM target that allowed higher fiscal deficit during difficult/recessionary years and low targets during comfortable years, gives the government a breathing space to borrow more during tight years. In it report submitted in late January this year, the committee did advocate for a range rather than a fixed fiscal deficit target. Especially, fiscal management becomes all the more important post-demonetisation and the resultant slump in consumption expenditure. The view is that the government could be tempted to increase public spending to boost consumption. but, here is the catch: while ratings agencies do look at the fiscal discipline of a country when considering them for a ratings upgrade, they also look at the context and the growth rate of the economy, so the decision will not be a myopic one based only on the fiscal and revenue deficits.

Fiscal responsibility is an economic concept that has various definitions, depending on the economic theory held by the person or organization offering the definition. Some say being fiscally responsible is just a matter of cutting debt, while others say it’s about completely eliminating debt. Still others might argue that it’s a matter of controlling the level of debt without completely reducing it. Perhaps the most basic definition of fiscal responsibility is the act of creating, optimizing and maintaining a balanced budget.

“Fiscal” refers to money and can include personal finances, though it most often is used in reference to public money or government spending. This can involve income from taxes, revenue, investments or treasuries. In a governmental context, a pledge of fiscal responsibility is a government’s assurance that it will judiciously spend, earn and generate funds without placing undue hardship on its citizens. Fiscal responsibility includes a moral contract to maintain a financially sound government for future generations, because a First World society is difficult to maintain without a financially secure government.

But, what exactly is fiscal responsibility, fiscal management and FRBM. So, here is an attempt to demystify these.

Fiscal responsibility often starts with a balanced budget, which is one with no deficits and no surpluses. The expectations of what might be spent and what is actually spent are equal. Many forms of government have different views and expectations for maintaining a balanced budget, with some preferring to have a budget deficit during certain economic times and a budget surplus during others. Other types of government view a budget deficit as being fiscally irresponsible at any time. Fiscal irresponsibility refers to a lack of effective financial planning by a person, business or government. This can include decreasing taxes in one crucial area while drastically increasing spending in another. This type of situation can cause a budget deficit in which the outgoing expenditures exceed the cash coming in. A government is a business in its own right, and no business — or private citizen — can thrive eternally while operating with a deficit.

When a government is fiscally irresponsible, its ability to function effectively is severely limited. Emergent situations arise unexpectedly, and a government needs to have quick access to reserve funds. A fiscally irresponsible government isn’t able to sustain programs designed to provide fast relief to its citizens.

A government, business or person can take steps to become more fiscally responsible. One useful method for government is to provide some financial transparency, which can reduce waste, expose fraud and highlight areas of financial inefficiency. Not all aspects of government budgets and spending can be brought into full public view because of various risks to security, but offering an inside look at government spending can offer a nation’s citizens a sense of well-being and keep leaders honest. Similarly, a private citizen who is honest with himself about where he is spending his money is better able to determine where he might be able to make cuts that would allow him to live within his means.

Fiscal Responsibility and Budget Management (FRBM) became an Act in 2003. The objective of the Act is to ensure inter-generational equity in fiscal management, long run macroeconomic stability, better coordination between fiscal and monetary policy, and transparency in fiscal operation of the Government.

The Government notified FRBM rules in July 2004 to specify the annual reduction targets for fiscal indicators. The FRBM rule specifies reduction of fiscal deficit to 3% of the GDP by 2008-09 with annual reduction target of 0.3% of GDP per year by the Central government. Similarly, revenue deficit has to be reduced by 0.5% of the GDP per year with complete elimination to be achieved by 2008-09. It is the responsibility of the government to adhere to these targets. The Finance Minister has to explain the reasons and suggest corrective actions to be taken, in case of breach.

FRBM Act provides a legal institutional framework for fiscal consolidation. It is now mandatory for the Central government to take measures to reduce fiscal deficit, to eliminate revenue deficit and to generate revenue surplus in the subsequent years. The Act binds not only the present government but also the future Government to adhere to the path of fiscal consolidation. The Government can move away from the path of fiscal consolidation only in case of natural calamity, national security and other exceptional grounds which Central Government may specify.

Further, the Act prohibits borrowing by the government from the Reserve Bank of India, thereby, making monetary policy independent of fiscal policy. The Act bans the purchase of primary issues of the Central Government securities by the RBI after 2006, preventing monetization of government deficit. The Act also requires the government to lay before the parliament three policy statements in each financial year namely Medium Term Fiscal Policy Statement; Fiscal Policy Strategy Statement and Macroeconomic Framework Policy Statement.

To impart fiscal discipline at the state level, the Twelfth Finance Commission gave incentives to states through conditional debt restructuring and interest rate relief for introducing Fiscal Responsibility Legislations (FRLs). All the states have implemented their own FRLs.

Indian economy faced with the problem of large fiscal deficit and its monetization spilled over to external sector in the late 1980s and early 1990s. The large borrowings of the government led to such a precarious situation that government was unable to pay even for two weeks of imports resulting in economic crisis of 1991. Consequently, Economic reforms were introduced in 1991 and fiscal consolidation emerged as one of the key areas of reforms. After a good start in the early nineties, the fiscal consolidation faltered after 1997-98. The fiscal deficit started rising after 1997-98. The Government introduced FRBM Act, 2003 to check the deteriorating fiscal situation.

The implementation of FRBM Act/FRLs improved the fiscal performance of both centre and states.

The States have achieved the targets much ahead the prescribed timeline. Government of India was on the path of achieving this objective right in time. However, due to the global financial crisis, this was suspended and the fiscal consolidation as mandated in the FRBM Act was put on hold in 2007- 08.The crisis period called for increase in expenditure by the government to boost demand in the economy. As a result of fiscal stimulus, the government has moved away from the path of fiscal consolidation. However, it should be noted that strict adherence to the path of fiscal consolidation during pre crisis period created enough fiscal space for pursuing counter cyclical fiscal policy.the main provisions of the Act are:

  1. The government has to take appropriate measures to reduce the fiscal deficit and revenue deficit so as to eliminate revenue deficit by 2008-09 and thereafter, sizable revenue surplus has to be created.
  2. Setting annual targets for reduction of fiscal deficit and revenue deficit, contingent liabilities and total liabilities.
  3. The government shall end its borrowing from the RBI except for temporary advances.
  4. The RBI not to subscribe to the primary issues of the central government securities after 2006.
  5. The revenue deficit and fiscal deficit may exceed the targets specified in the rules only on grounds of national security, calamity etc.

Though the Act aims to achieve deficit reductions prima facie, an important objective is to achieve inter-generational equity in fiscal management. This is because when there are high borrowings today, it should be repaid by the future generation. But the benefit from high expenditure and debt today goes to the present generation. Achieving FRBM targets thus ensures inter-generation equity by reducing the debt burden of the future generation. Other objectives include: long run macroeconomic stability, better coordination between fiscal and monetary policy, and transparency in fiscal operation of the Government.

The Act had said that the fiscal deficit should be brought down to 3% of the gross domestic product (GDP) and revenue deficit should drop down to nil, both by March 2009. Fiscal deficit is the excess of government’s total expenditure over its total income. The government incurs revenue and capital expenses and receives income on the revenue and capital account. Further, the excess of revenue expenses over revenue income leads to a revenue deficit. The FRBM Act wants the revenue deficit to be nil as the revenue expenditure is day-to-day expenses and does not create a capital asset. Usually, the liabilities should not be carried forward, else the government ends up borrowing to repay its current liabilities.

However, these targets were not achieved because the global credit crisis hit the markets in 2008. The government had to roll out a fiscal stimulus to revive the economy and this increased the deficits.

In the 2011 budget, the finance minister said that the FRBM Act would be modified and new targets would be fixed and flexibility will be built in to have a cushion for unforeseen circumstances. According to the 13th Finance Commission, fiscal deficit will be brought down to 3.5% in 2013-14. Likewise, revenue deficit is expected to be cut to 2.1% in 2013-14.

In the 2012 Budget speech, the finance minister announced an amendment to the FRBM Act. He also announced that instead of the FRBM targeting the revenue deficit, the government will now target the effective revenue deficit. His budget speech defines effective revenue deficit as the difference between revenue deficit and grants for creation of capital assets. In other words, capital expenditure will now be removed from the revenue deficit and whatever remains (effective revenue deficit) will now be the new goalpost of the fiscal consolidation. Here’s what effective revenue deficit means.

Every year the government incurs expenditure and simultaneously earns income. Some expenses are planned (that it includes in its five-year plans) and other are non-planned. However, both planned and non-planned expenditure consists of capital and revenue expenditure. For instance, if the government sets up a power plant as part of its non-planned expenditure, then costs incurred towards maintaining it will now not be called revenue deficit because it is towards maintaining a “capital asset”. Experts say that revenue deficit could become a little distorted because by reclassifying revenue deficit, it is simplifying its target.

 

access to reserve funds. A fiscally irresponsible government isn’t able to sustain programs designed to provide fast relief to its citizens.