naked capitalism

→ Rajiv Sethi

The erudite and very readable RT Leuchtkafer has posted yet another comment for the Securities and Exchange Commission to digest. This one was prompted by a paper by Andrei Kirilenko, Albert Kyle, Mehrdad Samadi and Tugkan Tuzun that provides a fascinating glimpse into the kinds of trading strategies that are common in asset markets today and the manner in which they interact to determine the dynamics of asset prices.

As I have argued on a couple of earlier occasions, the stability of a market depends on the composition of trading strategies, which in turn evolves over time under pressure of differential performance. Since performance itself depends on market stability, and destabilizing strategies prosper most when they are rare, this process can give rise to switching regimes: the market alternates between periods of stability and instability, giving rise to empirical patterns such as fat tails and clustered volatility in asset returns.

But the underlying strategies that are at the heart of this evolutionary process are generally unobservable. Since traders have no incentive to reveal successful strategies, these can only be inferred if individual orders can be traced to specific accounts.

This is what Kirilenko and his co-authors have been able to do, on the basis of “audit-trail, transaction-level data for all regular transactions in the June 2010 E-mini S&P 500 futures contract (E-mini) during May 3-6, 2010 between 8:30 a.m. CT and 3:15 p.m. CT.” While their primary concern is with the flash crash that materialized on the afternoon of the 6th, their analysis also sheds light on the composition and behavior of strategies over the period that led up to this event. Their analysis accordingly provides broader insight into the ecology of financial markets.

The authors classify accounts into six categories based on patterns exhibited in their trading behavior, such as horizon length, order size, and the willingness to accumulate significant net positions.  The categories are High Frequency Traders (HFTs), Intermediaries, Fundamental Buyers, Fundamental Sellers, Opportunistic Traders and Small Traders:

[Different] categories of traders occupy quite distinct, albeit overlapping, positions in the “ecosystem” of a liquid, fully electronic market. HFTs, while very small in number, account for a large share of total transactions and trading volume. Intermediaries leave a market footprint qualitatively similar, but smaller to that of HFTs. Opportunistic Traders at times act like Intermediaries (buying a selling around a given inventory target) and at other times act like Fundamental Traders (accumulating a directional position). Some Fundamental Traders accumulate directional positions by executing many small-size orders, while others execute a few larger-size orders. Fundamental Traders which accumulate net positions by executing just a few orders look like Small Traders, while Fundamental Traders who trade a lot resemble Opportunistic Traders. In fact, it is quite possible that in order not to be taken advantage of by the market, some Fundamental Traders deliberately pursue execution strategies that make them appear as though they are Small or Opportunistic Traders. In contrast, HFTs appear to play a very distinct role in the market and do not disguise their market activity.

Based on this taxonomy, the authors examine the manner in which the strategies vary with respect to trading volume, liquidity provision, directional exposure, and profitability. Although high-frequency traders constitute a minuscule proportion (about one-tenth of one percent) of total accounts, they are responsible for more than a third of aggregate trading volume in this market. They have extremely short trading horizons and maintain low levels of directional exposure. Under normal market conditions they are net providers of liquidity but their desire to avoid significant exposure means that they can become liquidity takers very quickly and on a large scale.

The extent to which different trading strategies provide liquidity to the market is assessed by the authors on the basis of a measure of order aggression. An order is said to be aggressive if it is marketable against a resting order in the limit order book (and is therefore executed immediately.) The resting order with which it is matched is said to be passive:

From a liquidity standpoint, a passive order (either to buy or to sell) has provided visible liquidity to the market and an aggressive order has taken liquidity from the market. Aggressiveness ratio is the ratio of aggressive trade executions to total trade executions… weighted either by the number of transactions or trading volume… HFTs and Intermediaries have aggressiveness ratios of 45.68% and 41.62%, respectively. In contrast, Fundamental Buyers and Sellers have aggressiveness ratios of 64.09% and 61.13%, respectively.

This is consistent with a view that HFTs and Intermediaries generally provide liquidity while Fundamental Traders generally take liquidity. The aggressiveness ratio of High Frequency Traders, however, is higher than what a conventional definition of passive liquidity provision would predict.

Moreover, the aggressiveness ratio of HFTs is not stable over time and can spike in times of market stress as they compete for liquidity with other market participants:

During the Flash Crash, the trading behavior of HFTs, appears to have exacerbated the downward move in prices. High Frequency Traders who initially bought contracts from Fundamental Sellers, proceeded to sell contracts and compete for liquidity with Fundamental Sellers. In addition, HFTs appeared to rapidly buy and [sell] contracts from one another many times, generating a “hot potato” effect before Opportunistic or Fundamental Buyers were attracted by the rapidly falling prices to step in and take these contracts off the market.

To my mind, the most revealing findings in the paper pertain to the profitability of the various strategies, and the ability of some traders to anticipate price movements over very short horizons (emphasis added):

High Frequency Traders effectively predict and react to price changes… [they] are consistently profitable although they never accumulate a large net position. This does not change on May 6 as they appear to have been even more successful despite the market volatility observed on that day… Intermediaries appear to be relatively less profitable than HFTs. During the Flash Crash, Intermediaries also appeared to have incurred significant losses… consistent with the notion that the relatively slower Intermediaries were unable to liquidate their position immediately, and were subsequently run over by the decrease in price…

HFTs appear to trade in the same direction as the contemporaneous price and prices of the past five seconds. In other words, they buy… if the immediate prices are rising. However, after about ten seconds, they appear to reverse the direction of their trading… possibly due to their speed advantage or superior ability to predict price changes, HFTs are able to buy right as the prices are about to increase… In marked contrast… Intermediaries buy when the prices are already falling and sell when the prices are already rising…

We consider Intermediaries and HFTs to be very short term investors. They do not hold positions over long periods of time and revert to their target inventory level quickly… HFTs very quickly reduce their inventories by submitting marketable orders. They also aggressively trade when prices are about to change. Over slightly longer time horizons, however, HFTs sometimes act as providers of liquidity. In contrast… unlike HFTs, Intermediaries provide liquidity over very short horizons and rebalance their portfolios over longer horizons.

What appears to have happened during the crash is that the fastest moving market makers with the most effective algorithms for short run price prediction were able to trade ahead of their slower and less effective brethren, imposing significant losses on the latter. In Leuchtkafer’s colorful language, this was a case of interdealer panic and marker maker fratricide.

But regardless of how the gains or losses were distributed in this instance, the fact remains that an overwhelming share of trading activity is based short-run price forecasts rather than fundamental research. Under these conditions, how can one expect prices to track changes in the fundamental values of the income streams to which the assets give title?

Markets have always been based on a shifting balance between information augmenting and information extracting strategies, but a computational arms race coupled with changes in institutions and regulation seem to have shifted the balance markedly towards the latter. Unless the structure of incentives is altered to favor longer holding periods, I suspect that we shall continue to see major market disruptions and spikes in volatility.

This is not just a matter of academic interest. To the extent that changes in the perceived volatility of stocks gives rise to changes in asset allocations by institutional and retail investors, there will be consequences for the extent and distribution of risk-bearing, and ultimately on rates of job creation and economic growth.

→ Rajiv Sethi

Back in 1980, Avraham Beja and Barry Goldman published a theoretical paper in the Journal of Finance that explored the manner in which the composition of trading strategies in an asset market affects the volatility of prices. Their main insight was that if the prevalence of momentum based strategies was too large relative to that of strategies based on fundamental analysis, then the dynamics of asset prices would be locally unstable: departures of prices from fundamentals would be amplified rather than corrected over time. More importantly, they argued that the relationship between the composition of strategies and market stability was discontinuous: there was a threshold (bifurcation) value of this population mixture that separated the stable from the unstable regime, and an imperceptible change in composition that took the market across the threshold could result in dramatic increases in volatility.

The Beja/Goldman analysis can be taken a step further: not only does market stability depend on the composition of trading strategies, but the profitability of different trading strategies, and hence changes in their relative population shares over time, depend very much on whether one is in a stable or an unstable regime. In a stable regime prices track fundamentals reasonably well, which makes it possible for technical strategies to extract information from incoming market data without going through the trouble and expense of fundamental research. Such strategies can therefore prosper and proliferate, provide that they remain sufficiently rare. But if they become too common, markets are destabilized, asset price bubbles can form, and the value of fundamental information rises. When a major correction arrives, it is the fundamental strategies that prosper, the composition of trading strategies is shifted accordingly, and market stability is restored for a time. This process of endogenous regime switching provides one possible interpretation of the empirical phenomenon known as volatility clustering.

From this perspective, it is critically important that technical trading strategies to be allowed to suffer losses when market instability arises. The cancellation of trades in almost 300 securities after the flash crash of May 6 did exactly the opposite, by providing an implicit subsidy to destabilizing strategies. The excuse that this was done to protect retail investors whose stop orders were executed as prices fell to insane levels is unconvincing. According to the SEC’s own report on the crash, most trades against stub quotes of five cents or less were short sales, and there was also considerable upward instability, with prices rising well beyond the reach of ordinary retail investors. (Shares in Sotheby’s, for instance, changed hands at ten million dollars per round lot.) The cancellation of trades was therefore a bailout of some funds (heavily reliant on algorithmic trading) at the expense of others, and this prevented a stabilizing shift in the market composition of trading strategies.

A similar argument could be made about the effects of the Troubled Asset Relief Program. It has recently been claimed, for instance by Alan Blinder and Mark Zandi, that TARP has been a “substantial success” because it averted a second Great Depression at a cost to taxpayers that is turning out to be much lower than originally feared:

The Troubled Asset Relief Program was controversial from its inception. Both the program’s $700 billion headline price tag and its goal of “bailing out” financial institutions—including some of the same institutions that triggered the panic in the first place—were hard for citizens and legislators to swallow. To this day, many believe the TARP was a costly failure. In fact, TARP has been a substantial success, helping to restore stability to the financial system and to end the freefall in  housing and auto markets. Its ultimate cost to taxpayers will be a small fraction of the headline $700 billion figure: A number below $100 billion seems more likely to us, with the bank bailout component probably turning a profit.

Yves Smith is unpersuaded by such figures, which she attributes to “back door, less visible bailouts, super cheap interest rates, [and] regulatory forbearance.” But even if one were to take at face value the Blinder-Zandi estimates of the revenue consequences of TARP, there remain potentially harmful effects on the size composition of firms and the distribution of financial practices. The institutions that were bailed out made directional bets that either failed directly, or were with counterparties that would have failed in the absence of government support. Smaller institutions making such mistakes were allowed to go under, while larger ones were bailed out. Quite apart from the unfairness of this, the policy could be severely damaging to the stability of the system over the medium run.

This point was made a couple of months ago in a speech by Richard Fisher of the Dallas Fed (and expanded upon by Tyler Durden and Ashwin Parameswaran shortly thereafter):

Big banks that took on high risks and generated unsustainable losses received a public benefit… As a result, more conservative banks were denied the market share that would have been theirs if mismanaged big banks had been allowed to go out of business. In essence, conservative banks faced publicly backed competition…

The system has become slanted not only toward bigness but also high risk… Clearly, if the central bank and regulators view any losses to big bank creditors as systemically disruptive, big bank debt will effectively reign on high in the capital structure. Big banks would love leverage even more, making regulatory attempts to mandate lower leverage in boom times all the more difficult. In this manner, high risk taking by big banks has been rewarded, and conservatism at smaller institutions has been penalized…

It is not difficult to see where this dynamic leads—to more pronounced financial cycles and repeated crises.

Fisher goes on to argue for strict limits on the size of individual financial institutions relative to that of the industry. So does Nouriel Roubini:

Greed has to be controlled by fear of loss, which derives from knowledge that the reckless institutions and agents will not be bailed out. The systematic bailouts of the latest crisis – however necessary to avoid a global meltdown – worsened this moral-hazard problem. Not only were “too big to fail” financial institutions bailed out, but the distortion has become worse as these institutions have become – via financial-sector consolidation – even bigger. If an institution is too big to fail, it is too big and should be broken up.

But were the bailouts really necessary to avoid a global meltdown? Blinder and Zandi argue that the alternative would have been completely catastrophic:

The financial policy responses were especially important. In the scenario without them, but including the fiscal stimulus, the recession would only now be winding down, a full year after the downturn’s actual end… The differences between the baseline and the scenario based on no financial policy responses… represent our estimates of the combined effects of the various policy efforts to stabilize the financial system — and they are very large. By 2011, real GDP is almost $800 billion (6%) higher because of the policies, and the unemployment rate is almost 3 percentage points lower. By the second quarter of 2011 — when the difference between the baseline and this scenario is at its largest — the financial-rescue policies are credited with saving almost 5 million jobs.

Here the baseline is the set of policies actually pursued (including fiscal and financial policies) and it is being compared to the case of “no financial policy responses.” However, as Yves Smith and Barry Ritholtz have pointed out, this is an absurd counterfactual. Barry argues that  the proper point of comparison ought to be what should have been done, which in his view is the following:

One by one, we should have put each insolvent bank into receivership, cleaned up the balance [sheet], sold off the bad debts for 15-50 cents on the dollar, fired the management, wiped out the shareholders, and spun out the proceeds, with the bondholders taking the haircut, and the taxpayers on the hook for precisely zero dollars. Citi, Bank of America, Wamu, Wachovia, Countrywide, Lehman, Merrill, Morgan, etc. all of them should have been handled this way.

The net result of this would have been more turmoil, lower stock prices, and a sharper, but much shorter economic contraction. It would have been painful and disruptive — like emergency surgery is — but its better than an exploded appendix.

And today, we would have a much healthier economy.

Whether or not one agrees with this assessment, Yves and Barry are surely correct in arguing that counterfactuals other than the hands-off policy ought to be considered before one accepts the emerging conventional wisdom that the authorities handled the crisis well.

What the broken trades trades of May 6 and the bailouts of 2008 have in common is that they were both impulsive decisions, designed to deal with immediate concerns, and executed with little regard for their long term consequences. As I said in an earlier post, these decisions were made under enormous pressure with little time for reflection, and mistakes made in such circumstances would ordinarily be forgivable. But to insist that the best available course of action was taken, and that any alternative would have had devastating economic costs, is neither credible nor wise.

→ Rajiv Sethi

In a recent post on his (consistently interesting) blog, David Murphy questions the value of equilibrium analysis in economics and finance, and points to two earlier posts of his in which the same point is made. Here he is in July 2007:

An interesting post on the Street Light Blog, on currency misalignments, suggests an interesting question: is economics an equilibrium discipline? The very idea of a misaligned FX rate suggests that the natural state is an aligned one: perhaps the fundamentals move faster than the markets adjust, so FX is never in equilibrium. Perhaps (in the language of statistical mechanics) the relaxation time is much longer than the average time between forcings.

And here, in August 2008:

My own view is that finance is not an equilibrium discipline, mostly, so while classical economics might work well in explaining the price of coffee… it does rather less well in asset allocation or explaining the return distribution of financial assets. Rather new news arrives faster than the market can restore equilibrium after the last perturbation, meaning that most of the time equilibrium is not a useful concept.

In a 1975 paper that remains worth reading to this day, James Tobin was explicit about the limitations of equilibrium analysis in understanding large scale economic fluctuations:

Keynes’s General Theory attempted to prove the existence of equilibrium with involuntary unemployment, and this pretension touched off a long theoretical controversy. A. C. Pigou, in particular, argued effectively that there could not be a long-run equilibrium with excess supply of labor. The predominant verdict of history is that, as a matter of pure theory, Keynes failed to prove his case.

Very likely Keynes chose the wrong battleground. Equilibrium analysis and comparative statics were the tools to which he naturally turned to express his ideas, but they were probably not the best tools for his purpose… The real issue is not the existence of a long-run static equilibrium with unemployment, but the possibility of protracted unemployment which the natural adjustments of a market economy remedy very slowly if at all. So what if, within the recherché rules of the contest, Keynes failed to establish an “underemployment equilibrium”? The phenomena he described are better regarded as disequilibrium dynamics.

Tobin then goes on to develop a dynamic disequilibrium model of the macroeconomy (discussed at length here) which has a unique equilibrium characterized by full employment, steady inflation, and correct expectations. He shows that even if this equilibrium is locally stable, so that small perturbations are self-correcting, it need not be globally stable: sufficiently large shocks to the economy can result in cumulative divergence away from equilibrium unless arrested by a significant policy response. This seems to describe what we have experienced over the past couple of years better than any equilibrium model of which I am aware.

Note that Tobin’s model is deterministic. The problem here is not that the economy is being buffeted by frequent shocks that arrive before a transition to equilibrium can occur, it is that the internal dynamics of adjustment simply do not approach the equilibrium from certain (large) sets of initial states even in the absence of shocks. The idea that the instability of steady growth with respect to disequilibrium dynamics is an important feature of modern market economies, and cannot be neglected in a comprehensive theory of economic fluctuations was forcefully advanced by Richard Goodwin as far back as 1951, and Paul Samuelson had explored the possibility even earlier. As Willem Buiter has recently lamented, this line of research in macroeconomics simply dried up about a generation ago.

Another area in which equilibrium analysis is likely to be inadequate is in the study of asset markets with significant speculative activity. Price and volume dynamics in such markets depend not just on changes in fundamentals but also on the distribution of trading strategies, and this in turn adjusts under pressure of differential performance. The idea of an equilibrium composition of trading strategies is a contradiction in terms: if there were any such thing there would be a new strategy that could enter to exploit the resulting regularity. It is the complexity of this disequilibrium process that allows information arbitrage efficiency to be approximately satisfied, while allowing for significant departures from fundamental valuation efficiency (the distinction, naturally, is also due to Tobin.)

Finally consider Hyman Minsky’s financial instability hypothesis, built on the paradoxical idea that stability itself can be destabilizing. In Minsky’s framework stable expansions give rise to increasingly aggressive financial practices as those firms having the greatest maturity mismatch between assets and liabilities profit relative to their closest competitors. The resulting erosion in margins of safety increases financial fragility, interpreted as the likelihood that a major default will trigger a crisis of liquidity. Such a crisis eventually materializes, devastating precisely those firms whose actions gave rise to greater fragility. The balance of financial practices is then shifted in favor of increased prudence, and the stage is set for another period of stability. Trying to give this analysis an equilibrium interpretation is a futile exercise; expectations of financial market tranquility are self-falsifying, and no fixed distribution of financial practices can be stable.

Given the potential of disequilibrium dynamic models to illuminate our understanding of the economy, why are they generally neglected in contemporary economics? In part it is because the quality of a disequilibrium model is hard to evaluate and the dynamics are necessarily arbitrary to a degree. There is a professional consensus on how equilibrium analysis should be done, but none (so far) when it comes to disequilibrium analysis. Furthermore, equilibrium models can be enormously insightful, even in applications to macroeconomics and finance. The work of John Geanakoplos on the leverage cycle is a case in point, and Abreu and Brunnermeier’s paper on bubbles and crashes is another. I have used equilibrium methods frequently and will continue to do so. But it seems that there ought to be greater space in the profession for serious work on the dynamics of disequilibrium.

→ Rajiv Sethi

In a recent paper on financial innovation and fragility, Gennaioli, Shleifer and Vishny argue that investors (and often also financial intermediaries) are hobbled by certain systematic cognitive biases that cause them to neglect unlikely events when assessing asset values. They argue that such “local thinking” results in the creation and excessive issuance of engineered securities that are widely believed to be close substitutes for more traditional safe assets, but turn out to be much riskier than initially anticipated. This psychological regularity, they believe, accounts for a number of historical episodes of financial instability:

Many recent episodes of financial innovation share a common narrative. It begins with a strong demand from investors for a particular, often safe, pattern of cash flows. Some traditional securities available in the market offer this pattern, but investors demand more (so prices are high). In response to demand, financial intermediaries create new securities offering the sought after pattern of cash flows, usually by carving them out of existing projects or other securities that are more risky. By virtue of diversification, tranching, insurance, and other forms of financial engineering, the new securities are believed by the investors, and often by the intermediaries themselves, to be good substitutes for the traditional ones, and are consequently issued and bought in great volumes. At some point, news reveals that new securities are vulnerable to some unattended risks, and in particular are not good substitutes for the traditional securities. Both investors and intermediaries are surprised by the news, and investors sell these “false substitutes,” moving back to the traditional securities with the cash flows they seek. As investors fly for safety, financial institutions are stuck holding the supply of the new securities (or worse yet, having to dump them as well in a fire sale because they are leveraged). The prices of traditional securities rise while those of the new ones fall sharply.

The authors claim that this sequence of events describes not only the recent experience with collateralized debt obligations and money market funds, but also earlier episodes of financial innovation, including prepayment tranching of collateralized mortgage obligations in the 1980s.

In order to explore precisely the implications of local thinking in the context of financial innovation, the authors construct a model based on a number of stark, simplifying assumptions. There are two assets: a traditional safe security and a risky asset that has three possible terminal payoffs. The worst case outcome for the risky asset is also the least likely to occur (this is a crucial assumption). Investors are homogeneous and highly risk averse. Financial innovation takes the form of separating the cash flows from the risky asset into two components: a “safe” security that earns the the worst case payoff regardless of the actual outcome, and a risky residual claim. Under rational expectations this innovation is welfare improving, and the quantity of the substitute issued is precisely such that all such claims would be covered even if the worst case loss were to materialize. That is, the substitute security really is safe.

Under local thinking, the least likely event (which is also the worst case outcome) is simply neglected, and beliefs about the other two outcomes are correspondingly inflated. The intermediate outcome is now (mistakenly) perceived to be the worst, and a greater quantity of the substitute security is issued than could be honored if the actual worst case outcome were to be realized. Now suppose that some bad news arrives, conditional on which the objective probabilities of the three outcomes are altered in such a manner as to make the intermediate outcome the least likely. Local thinking then causes investors to become excessively pessimistic: the worst case outcome not only becomes suddenly salient, but the less disastrous intermediate outcome is neglected and the decline in the price of the asset previously thought to be safe is greater than it would be under rational expectations.

The development of a theoretical framework within which common elements of various historical episodes can be examined is clearly a worthwhile exercise. But what troubles me about this paper (and much of the behavioral finance literature) is that the rational expectations hypothesis of identical, accurate forecasts is replaced by an equally implausible hypothesis of identical, inaccurate forecasts. The underlying assumption is that financial market participants operating under competitive conditions will reliably express cognitive biases identified in controlled laboratory environments. And the implication is that financial instability could be avoided if only we were less cognitively constrained, or constrained in different ways — endowed with a propensity to overestimate rather than discount the likelihood of unlikely events for example.

This narrowly psychological approach to financial fragility neglects two of the most analytically interesting aspects of market dynamics: belief heterogeneity and evolutionary selection. Even behavioral propensities that are psychologically rare in the general population can become widespread in financial markets if they result in the adoption of successful strategies. As a result, asset prices disproportionately reflect the beliefs of investors who have been most successful in the recent past. There is no reason why these beliefs should consistently conform to those in the general population.

I have argued previously for the further development of this ecological perspective on financial instability, and similar themes have been explored elsewhere; see especially Macroeconomic Resilience and David Murphy. As I said in an earlier post, a bit too much is being asked of behavioral economics at this time, more than it has the capacity to deliver.