Financial Fragility in the Margins. Thought of the Day 114.0

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If micro-economic crisis is caused by the draining of liquidity from an individual company (or household), macro-economic crisis or instability, in the sense of a reduction in the level of activity in the economy as a whole, is usually associated with an involuntary outflow of funds from companies (or households) as a whole. Macro-economic instability is a ‘real’ economic phenomenon, rather than a monetary contrivance, the sense in which it is used, for example, by the International Monetary Fund to mean price inflation in the non-financial economy. Neo-classical economics has a methodological predilection for attributing all changes in economic activity to relative price changes, specifically the price changes that undoubtedly accompany economic fluctuations. But there is sufficient evidence to indicate that falls in economic activity follow outflows of liquidity from the industrial and commercial company sector. Such outflows then lead to the deflation of economic activity that is the signal feature of economic recession and depression.

Let us start with a consideration of how vulnerable financial futures market themselves are to illiquidity, since this would indicate whether the firms operating in the market are ever likely to need to realize claims elsewhere in order to meet their liabilities to the market. Paradoxically, the very high level of intra-broker trading is a safety mechanism for the market, since it raises the velocity of circulation of whatever liquidity there is in the market: traders with liabilities outside the market are much more likely to have claims against other traders to set against those claims. This may be illustrated by considering the most extreme case of a futures market dominated by intra-broker trading, namely a market in which there are only two dealers who buy and sell financial futures contracts only between each other as rentiers, in other words for a profit which may include their premium or commission. On the expiry date of the contracts, conventionally set at three-monthly intervals in actual financial futures markets, some of these contracts will be profitable, some will be loss-making. Margin trading, however, requires all the profitable contracts to be fully paid up in order for their profit to be realized. The trader whose contracts are on balance profitable therefore cannot realize his profits until he has paid up his contracts with the other broker. The other broker will return the money in paying up his contracts, leaving only his losses to be raised by an inflow of money. Thus the only net inflow of money that is required is the amount of profit (or loss) made by the traders. However, an accommodating gross inflow is needed in the first instance in order to make the initial margin payments and settle contracts so that the net profit or loss may be realized.

The existence of more traders, and the system for avoiding counterparty risk commonly found in most futures market, whereby contracts are made with a central clearing house, introduce sequencing complications which may cause problems: having a central clearing house avoids the possibility that one trader’s default will cause other traders to default on their obligations. But it also denies traders the facility of giving each other credit, and thereby reduces the velocity of circulation of whatever liquidity is in the market. Having to pay all obligations in full to the central clearing house increases the money (or gross inflow) that broking firms and investors have to put into the market as margin payments or on settlement days. This increases the risk that a firm with large net liabilities in the financial futures market will be obliged to realize assets in other markets to meet those liabilities. In this way, the integrity of the market is protected by increasing the effective obligations of all traders, at the expense of potentially unsettling claims on other markets.

This risk is enhanced by the trading of rentiers, or banks and entrepreneurs operating as rentiers, hedging their futures contracts in other financial markets. However, while such incidents generate considerable excitement around the markets at the time of their occurrence, there is little evidence that they could cause involuntary outflows from the corporate sector on such a scale as to produce recession in the real economy. This is because financial futures are still used by few industrial and commercial companies, and their demand for financial derivatives instruments is limited by the relative expense of these instruments and their own exposure to changes in financial parameters (which may more easily be accommodated by holding appropriate stocks of liquid assets, i.e., liquidity preference). Therefore, the future of financial futures depends largely on the interest in them of the contemporary rentiers in pension, insurance and various other forms of investment funds. Their interest, in turn, depends on how those funds approach their ‘maturity’.

However, the decline of pension fund surpluses poses important problems for the main securities markets of the world where insurance and pension funds are now the dominant investors, as well as for more peripheral markets like emerging markets, venture capital and financial futures. A contraction in the net cash inflow of investment funds will be reflected in a reduction in the funds that they are investing, and a greater need to realize assets when a change in investment strategy is undertaken. In the main securities markets of the world, a reduction in the ‘new money’ that pension and insurance funds are putting into those securities markets will slow down the rate of growth of the prices in those markets. How such a fall in the institutions’ net cash inflow will affect the more marginal markets, such as emerging markets, venture capital and financial futures, depends on how institutional portfolios are managed in the period of declining net contributions inflows.

In general, investment managers in their own firms, or as employees of merchant or investment banks, compete to manage institutions’ funds. Such competition is likely to increase as investment funds approach ‘maturity’, i.e., as their cash outflows to investors, pensioners or insurance policyholders, rises faster than their cash inflow from contributions and premiums, so that there are less additional funds to be managed. In principle, this should not affect financial futures markets, in the first instance, since, as argued above, the short-term nature of their instruments and the large proportion in their business of intra-market trade makes them much less dependent on institutional cash inflows. However, this does not mean that they would be unaffected by changes in the portfolio preferences of investment funds in response to lower returns from the main securities markets. Such lower returns make financial investments like financial futures, venture capital and emerging markets, which are more marginal because they are so hazardous, more attractive to normally conservative fund managers. Investment funds typically put out sections of portfolios to specialist fund managers who are awarded contracts to manage a section according to the soundness of their reputation and the returns that they have made hitherto in portfolios under their management. A specialist fund manager reporting high, but not abnormal, profits in a fund devoted to financial futures, is likely to attract correspondingly more funds to manage when returns are lower in the main markets’ securities, even if other investors in financial futures experienced large losses. In this way, the maturing of investment funds could cause an increased inflow of rentier funds into financial futures markets.

An inflow of funds into a financial market entails an increase in liabilities to the rentiers outside the market supplying those funds. Even if profits made in the market as a whole also increase, so too will losses. While brokers commonly seek to hedge their positions within the futures market, rentiers have much greater possibilities of hedging their contracts in another market, where they have assets. An inflow into futures markets means that on any settlement day there will therefore be larger net outstanding claims against individual banks or investment funds in respect of their financial derivatives contracts. With margin trading, much larger gross financial inflows into financial futures markets will be required to settle maturing contracts. Some proportion of this will require the sale of securities in other markets. But if liquidity in integrated cash markets for securities is reduced by declining net inflows into pension funds, a failure to meet settlement obligations in futures markets is the alternative to forced liquidation of other assets. In this way futures markets will become more fragile.

Moreover, because of the hazardous nature of financial futures, high returns for an individual firm are difficult to sustain. Disappointment is more likely to be followed by the transfer of funds to management in some other peripheral market that shows a temporary high profit. While this should not affect capacity utilization in the futures market, because of intra-market trade, it is likely to cause much more volatile trading, and an increase in the pace at which new instruments are introduced (to attract investors) and fall into disuse. Pension funds whose returns fall below those required to meet future liabilities because of such instability would normally be required to obtain additional contributions from employers and employees. The resulting drain on the liquidity of the companies affected would cause a reduction in their fixed capital investment. This would be a plausible mechanism for transmitting fragility in the financial system into full-scale decline in the real economy.

The proliferation of financial futures markets has only had been marginally successful in substituting futures contracts for Keynesian liquidity preference as a means of accommodating uncertainty. A closer look at the agents in those markets and their market mechanisms indicates that the price system in them is flawed and trading hazardous risks in them adds to uncertainty rather than reducing it. The hedging of financial futures contracts in other financial markets means that the resulting forced liquidations elsewhere in the financial system are a real source of financial instability that is likely to worsen as slower growth in stock markets makes speculative financial investments appear more attractive. Capital-adequacy regulations are unlikely to reduce such instability, and may even increase it by increasing the capital committed to trading in financial futures. Such regulations can also create an atmosphere of financial security around these markets that may increase unstable speculative flows of liquidity into the markets. For the economy as a whole, the real problems are posed by the involvement of non-financial companies in financial futures markets. With the exception of a few spectacular scandals, non-financial companies have been wary of using financial futures, and it is important that they should continue to limit their interest in financial futures markets. Industrial and commercial companies, which generate their own liquidity through trade and production and hence have more limited financial assets to realize in order to meet financial futures liabilities in times of distress, are more vulnerable to unexpected outflows of liquidity in proportion to their increased exposure to financial markets. The liquidity which they need to set aside to meet such unexpected liabilities inevitably means a reduced commitment to investment in fixed capital and new technology.

Complexity Wrapped Uncertainty in the Bazaar

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One could conceive a financial market as a set of N agents each of them taking a binary decision every time step. This is an extremely crude representation, but capture the essential feature that decision could be coded by binary symbols (buy = 0, sell = 1, for example). Although the extreme simplification, the above setup allow a “stylized” definition of price.

Let Nt0, Nt1 be the number of agents taking the decision 0, 1 respectively at the time t. Obviously, N = Nt0 + Nt1 for every t . Then, with the above definition of the binary code the price can be defined as:

pt = f(Nt0/Nt1)

where f is an increasing and convex function which also hold that:

a) f(0)=0

b) limx→∞ f(x) = ∞

c) limx→∞ f'(x) = 0

The above definition perfectly agree with the common believe about how offer and demand work. If Nt0 is small and Nt1 large, then there are few agents willing to buy and a lot of agents willing to sale, hence the price should be low. If on the contrary, Nt0 is large and Nt1 is small, then there are a lot of agents willing to buy and just few agents willing to sale, hence the price should be high. Notice that the winning choice is related with the minority choice. We exploit the above analogy to construct a binary time-series associated to each real time-series of financial markets. Let {pt}t∈N be the original real time-series. Then we construct a binary time-series {at}t∈N by the rule:

at = {1 pt > pt-1

at = {0 pt < pt-1

Physical complexity is defined as the number of binary digits that are explainable (or meaningful) with respect to the environment in a string η. In reference to our problem the only physical record one gets is the binary string built up from the original real time series and we consider it as the environment ε . We study the physical complexity of substrings of ε . The comprehension of their complex features has high practical importance. The amount of data agents take into account in order to elaborate their choice is finite and of short range. For every time step t, the binary digits at-l, at-l+1,…, at-1 carry some information about the behavior of agents. Hence, the complexity of these finite strings is a measure of how complex information agents face. The Kolmogorov – Chaitin complexity is defined as the length of the shortest program π producing the sequence η when run on universal Turing machine T:

K(η) = min {|π|: η = T(π)}

where π represent the length of π in bits, T(π) the result of running π on Turing machine T and K(η) the Kolmogorov-Chaitin complexity of sequence π. In the framework of this theory, a string is said to be regular if K(η) < η . It means that η can be described by a program π with length smaller than η length. The interpretation of a string should be done in the framework of an environment. Hence, let imagine a Turing machine that takes the string ε as input. We can define the conditional complexity K(η / ε) as the length of the smallest program that computes η in a Turing machine having ε as input:

K(η / ε) = min {|π|: η = CT(π, ε)}

We want to stress that K(η / ε) represents those bits in η that are random with respect to ε. Finally, the physical complexity can be defined as the number of bits that are meaningful in η with respect to ε :

K(η : ε) = |η| – K(η / ε)

η also represent the unconditional complexity of string η i.e., the value of complexity if the input would be ε = ∅ . Of course, the measure K (η : ε ) as defined in the above equation has few practical applications, mainly because it is impossible to know the way in which information about ε is encoded in η . However, if a statistical ensemble of strings is available to us, then the determination of complexity becomes an exercise in information theory. It can be proved that the average values C(η) of the physical complexity K(η : ε) taken over an ensemble Σ of strings of length η can be approximated by:

C|(η)| = 〈K(η : ε) ≅  |η| – K(η : ε), where

K(η : ε) = -∑η∈∑p(η / ε) log2p(η / ε)

and the sum is taking over all the strings η in the ensemble Σ. In a population of N strings in environment ε, the quantity n(η)/N, where n(s) denotes the number of strings equal to η in ∑, approximates p(η / ε) as N → ∞.

Let ε = {at}t∈N and l be a positive integer l ≥ 2. Let Σl be the ensemble of sequences of length l built up by a moving window of length l i.e., if η ∈ Σl then η = aiai+1ai+l−1 for some value of i. The selection of strings ε is related to periods before crashes and in contrast, period with low uncertainty in the market…..

Speculations

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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.