Small Investors Beware

High frequency (or algorithmic) trading was one of the major investment innovations to emerge in the late 20th century. Given the effectiveness and profit potential of such methods, it comes as no surprise to learn that 46 percent of daily volume originates through high frequency strategies.

Powerful computers, some housed right next to the machines that drive marketplaces like the New York Stock Exchange, enable high frequency traders to transmit millions of orders at lightning speed and, their detractors contend, reap billions at everyone else’s expense… High-frequency specialists clearly have an edge over typical traders, let alone ordinary investors… Powerful algorithms — “algos,” in industry parlance — execute millions of orders a second and scan dozens of public and private marketplaces simultaneously. They can spot trends before other investors can blink, changing orders and strategies within milliseconds… These systems are so fast they can outsmart or outrun other investors, humans and computers alike. (Duhigg, “Stock Traders Find Speed Pays, in Milliseconds,” NYT, 23 Jul 2009)

Unfortunately, the methods of high frequency trading are neither available nor assessable to the average investor. My advice to most investors is to invest in public companies only with funds that you can afford to lose. My recommended investment strategy of choice for serious investors is to target companies in which you are an active owner, partner, or director in order to ensure you have full access to the fundamental information you need to monitor your investments wisely (which incidentally is the same strategy apparently used by Warren Buffet, George Soros, and Carl Icahn).

Institutional investors and other major players dominate modern day investing with sophisticated methods and technologies that the average investor cannot hope to match. Unless regulators can find a way to level the playing field, small investors should beware of the markets.

The “Limits of Arbitrage” Agenda

by Denis Gromb and Dimitri Vayanos © VoxEU.org

Why do financial market anomalies arise and persist? This column summarises a new thread in financial economics – the "limits of arbitrage" literature – explaining how financial institutions sometimes lack the capital needed to arbitrage away anomalies. This new approach has far-reaching implications for our understanding of how financial markets work and how they should be regulated.

Each financial crisis reminds us that governments are vital to the functioning of financial markets – with the current crisis being a particularly painful reminder (see for example Boone and Johnson 2010, Dewatripont et al, 2009).

Standard models, however, are ill-suited to analysing public policy. These models were developed to study the properties of asset prices; they typically ignore financial institutions and the financial constraints to which they are subject. Institutions, jointly labelled “arbitrageurs” in the theory, are instead assumed to have unfettered access to all the capital they need.*

Frustratingly optimistic theory

For economists with a public policy interest, what one might call the “unconstrained arbitrage” hypothesis delivers a frustratingly optimistic message. Financial markets are in a socially efficient equilibrium; consequently, public intervention is at best redistributive and at worst inefficient. This result, a special case of the so-called fundamental welfare theorems, captures the idea that in a free market economy, prices adjust so that profit-maximising agents end up making socially efficient choices.

Recent developments in financial economics may offer a more useful framework for policy analysis. To understand how these developments came about, we must take a step back and understand what unconstrained arbitrage really means for asset prices, the empirical challenges this hypothesis has met with, and the new theories emerging to deal with those challenges.

No free lunch on Wall Street

The main implication of the “unconstrained arbitrage” hypothesis is that there should be no arbitrage opportunities in equilibrium or, in plain English, no free lunch on Wall Street. This cornerstone of both the modern theory of asset pricing and its industry applications has itself two important corollaries.

• First, assets with similar payoffs should trade at similar prices (law of one price).
• Second, asset prices should change only in response to news about fundamentals, and news being by definition unpredictable, asset returns should also be unpredictable (efficient market hypothesis).

The main impetus to reconsider standard models was provided by what financial economists have affectionately dubbed market anomalies."

• For a start, some pairs of assets with very similar payoffs consistently trade at substantially different prices, in apparent violation of the law of one price. Newly issued “on-the-run” government bonds can trade at significantly higher prices than older “off-the-run” government bonds with nearly identical payoffs.
• Other anomalies concern the predictability of asset returns such as the “momentum effect”, whereby an asset's recent price performance tends to persist in the short run.

In both cases, the standard theory predicts that arbitrageurs would spot these proverbial free lunches, trade on them, and eliminate them in the process. For instance, arbitrageurs would buy the off-the-run bond and short the on-the-run bond to exploit their relative mispricing, but doing so they would narrow the price gap so that by the time mere mortals wake up, the free lunch has come and gone and all but crumbs are left.

These empirical discoveries have prompted a very active debate among financial economists (as well as very active trading by hedge funds).

• Some try to reconcile the anomalies with more sophisticated versions of the standard theory that still retain the assumption of unconstrained arbitrage.
• Others reject the more fundamental assumption that traders are rational and instead explain the anomalies based on behavioural biases.
• Yet another group lies somewhere in between, believing that arbitrageurs are crucial for the workings of financial markets but thinking of them as having to do their job with one hand tied behind their backs.

The limits of arbitrage

In a recent paper (Gromb and Vayanos 2010) we review the achievements and promises of this third way, the “limits of arbitrage” literature. This research seeks to understand why perfect arbitrage does not always happen in practice, i.e. why anomalies arise and persist. Its focus is on the process of arbitrage, with an emphasis on financial institutions, the real-world incarnations of textbook arbitrageurs, and on the constraints they face.

The premise is that arbitrageurs face constraints in that they cannot always raise the capital they need even when they face good investment opportunities. As it turns out, this simple premise has far-reaching implications for finance, and financial economists are only beginning to understand their full range and scale.

Suppose for instance that some investors suddenly want to sell a large amount of a given asset. These investors may be individuals, day-traders, mutual funds, banks, it does not matter for our example, and neither does the reason why they so suddenly want to sell. In any event, this “supply shock” has the potential to cause a drop in the asset’s price, which would offer an attractive investment opportunity for arbitrageurs.

Without constraints, arbitrageurs as group would simply absorb that supply shock, i.e. buy the asset the investors want to sell. If they required additional capital to buy the asset, arbitrageurs would be able to find it. As a result, even a large shock would have only a limited price impact. But once we consider that arbitrageurs face financial constraints, the picture is totally different. Indeed, if arbitrageurs cannot raise additional capital easily, they may not be able to absorb the shock fully and selling pressure can have a substantial and lasting price impact. Overall, when arbitrageurs as a group are flush with money, financial markets’ behaviour should resemble that of the standard theory. But if and when their capital is low, strange things can happen.

This simple enough insight is proving very fruitful. Let’s consider two of the tastier pieces of fruit. Both come from an important remark. If arbitrageurs’ capital affects asset prices, the reverse is also true.

First, the new approach has helped explain how small shocks can have big effects, as tends to be the case in financial crises. Consider again our supply shock example. We have seen that arbitrageurs facing financial constraints may not be able to absorb this shock, allowing it to have a substantial price impact. Things may be even worse. Suppose that before the shock, the arbitrageurs hold substantial amounts of that asset. A shock might cause the asset’s price to drop, implying a capital loss for the arbitrageurs. Not only may arbitrageurs not be able to absorb the shock fully, they may even be forced to liquidate assets themselves, pushing prices further down. In that case, arbitrageurs’ effect on asset prices is neither stabilising nor neutral, it is destabilising.

Second, the limits of arbitrage can rationalise episodes of contagion across asset markets. Here’s how it works. Following a supply shock in one market, the capital of arbitrageurs may be depleted. But since arbitrageurs draw from the same pool of capital to absorb shocks in different markets, a drop in their capital may force them to liquidate positions in other markets, affecting asset prices in those markets. Overall, a shock in one market affects other markets.

Inefficient markets

Research on the limits of arbitrage might very well reshape our understanding of financial markets. The next and arguably most important question however is whether it can provide a useful framework for public policy. Despite its relevance, the welfare analysis of asset markets with limited arbitrage is still in its infancy. But we believe it has great potential.

This research emphasises the role of financial institutions in the functioning of asset markets. Accordingly, these institutions’ financial health affects the functioning of markets. As we have seen, the reverse is also true. Their financial health is itself affected by asset prices through the capital gains and losses arbitrageurs realise. Now the question is whether arbitrageurs take financial positions putting their capital at risk in a way that is desirable for them and society as a whole. In an earlier paper laying out a model of financially constrained arbitrage (Gromb and Vayanos, 2002), we explain why the answer is “No.” In technical jargon, the welfare theorems do not hold.

Chain externalities

Follow the logic. Supply shocks have the potential to cause movements in asset prices, which constitute profit opportunities for arbitrageurs. Each arbitrageur however needs capital to be able to grab those tasty snacks. That’s fine; he might simply set capital aside in good times to be used when the opportunity arises and cash is king. In fact, a number of prominent investors follow such a strategy of keeping dry powder ready for when the goings get tough. Of course, setting capital aside means foregoing some risky but profitable opportunities available to arbitrageurs. Yet each arbitrageur can compare the benefit of investing in those opportunities to the cost of being short of capital in case of a big shock, and then decide for himself on the right amount based on this cost-benefit analysis. So far, still no inefficiency. However, there is something each arbitrageur does not fully take into account when deciding how much dry powder to keep and how much capital to put at risk. Indeed the cost of being short of capital in case of a shock is less than the implied social cost. When an arbitrageur is short of capital, not only is he unable to exploit the price movement caused by the shock, but as we have seen, his inability to do so amplifies the price effect of the shock. In turn lower prices cause other arbitrageurs to incur bigger capital losses, forcing them to liquidate assets, further depressing prices. This chain reaction has the effect of depriving arbitrageurs of capital right at the time when it would be most socially useful.

Policy

While in the standard model the invisible hand and its competitive prices elves gently guide towards taking socially optimal decisions, here they don’t. Instead, they drive arbitrageurs to put too much of their capital at risk. Since the price system cannot do its job of guiding agents, it can be good if someone else, a regulator perhaps, can provide that guidance. Regulation incentivising or even forcing arbitrageurs to take less risk could make everyone better off, arbitrageurs included.

How might this be best achieved? Risk-based capital requirements? Taxes and subsidies? A lender of last resort policy? Asset purchase programs? This is pretty much where this research agenda is at. The answers to these fascinating questions are still pending and hotly debated by academics and practitioners (see, for example, Sarkar and Shrader 2010). Hopefully, they’ll be ready by the time the next crisis hits.

References:

Boone, Peter and Simon Johnson (2010), “The Doomsday Cycle,” VoxEU.org, 22 February.

Dewatripont, Mathias, Xavier Freixas, and Richard Portes (2009), “Macroeconomic Stability and Financial Regulation: Key Issues for the G20,” VoxEU.org, 2 March.

Gromb, Denis, and Dimitri Vayanos (2002), “Equilibrium and Welfare in Markets with Constrained Arbitrageurs,” Journal of Financial Economics.

Gromb, Denis, and Dimitri Vayanos (2010), “The Limits of Arbitrage: The State of the Theory,” Annual Review of Financial Economics, forthcoming.

Sarkar, Asani, and Jeffrey Shrader (2010), “Financial Amplification Mechanisms and the Federal Reserve’s Supply of Liquidity during the Crisis,” Federal Reserve Bank of New York Staff Reports, no. 431.

*Textbook arbitrageurs represent professional arbitrageurs such as hedge funds and proprietary trading desks, but also and more generally financial intermediaries such as dealers, banks or mutual funds.

Reproduced with permission of VoxEU.org

Why Policymakers Need to Take Note of High-Frequency Finance

by Richard Olsen © VoxEU.org

Why should high-frequency finance be of any interest to policymakers interested in long-term economic issues? This column argues that the discipline can revolutionise economics and finance by turning accepted assumptions on their head and offering novel solutions to today’s issues.

I believe high-frequency finance is turning aspects of economics and finance into a hard science. The discipline was officially inaugurated at a conference in Zurich in 1995 that was attended by over 200 of the world’s top researchers. Since then, there have been a large number of publications including a book with the title Introduction to High-frequency Finance. “High-frequency data” is a term used for tick-by-tick price information that is collected from financial markets. The tick data is valuable, because they represent transaction prices at which assets are bought and sold. The price changes are a footprint of the changing balance of buyers and sellers.

The term “high-frequency finance” has a deeper meaning and is a statement of intent indicating that research is data-driven and agnostic. There are no ex ante theories or hypotheses. We let the data speak for itself. In natural sciences this is how research is often conducted. The first step towards discovery is pure observation and coming up with a description of what has been observed – this may sound easy but is not at all the case. Only in a second step, when the facts are clearly established, do natural scientists start formulating hypotheses that are then verified with experiments.

In high-frequency finance:

• The first step involves the collecting and scrubbing of data.
• The second step is to analyse the data and identify its statistical properties.

Here one looks for stylised facts which are significant and not just spurious. Due to the masses of data points available for analysis (for many financial instruments one can collect more than 100,000 data points per day), identification of structures is straightforward, either there is a regularity or there is none.

• The third step is to formalise observations of specific patterns and seek tentative explanations, theories to explain them.

The abundance of data in high-frequency finance has profound implications for the statistical relevance of its results. Unlike in other fields of economics and finance, where there is not sufficient data to back up the inferences, this is not an issue in high-frequency finance. The results are unambiguous and turn economics and finance into a hard science, just as is the case for natural sciences. This is not a bad thing.

High-frequency data as an answer to singularity of macro events

Today we are all grappling with the global financial crisis and have to make hard decisions. In living memory, we have not seen a crisis of a similar scale, so policymakers are in a vacuum and do not have any comparable historical precedents to validate their policy decisions.

If the global economy had been in existence for 100,000 years, this would be a different matter. We would have had many crises of a similar scale, and we could use these previous events as a benchmark to evaluate the current crisis. The modern economy with financial markets linked together through high speed communication networks trading trillions of dollars on a daily basis is a new phenomenon that did not exist even 20 years ago. People refer to the events of 1929 and subsequent years, but while these events can be used as one possible point of reference, they are not meaningful in the statistical sense. On a macro level, we can make observations but no inferences because we do not have the historical data. There is a void that researchers and policymakers need to acknowledge.

Fractals: Understanding macro structure from micro data

High-frequency finance can fill the void with its huge amounts of data – if we embrace fractal theory that explains how phenomena are the same even if they occur at different scales. Fractal theory suggests that we can search for explanations of the big crisis by moving to another time scale, the short term.

At a second-by-second level, there are an abundance of crises and systemic shocks; just imagine the occurrence of the many price jumps due to unexpected news releases and political events or large market orders. Albeit on a short-term time scale, we study how regime shifts occur and how human beings react. The large number of occurrences allows for meaningful analysis. We study all facets of a crisis, how traders behave prior to the crisis, how they react to the first onslaught, how they panic, when the going gets hard and finally, how their frame of reference which previously was a kind of anchor and gave them a degree of security breaks down and how later, when the shock has passed, the excitement dies down, there is the aftershock depression and then eventually how gradual recovery to a new state of normality begins.

The everyday events sum up and shape the tomorrow

High-frequency finance has another big selling point, one which policymakers should take note of: the study of market events on a tick-by-tick basis brings to the surface the detailed flows of buying and selling that occur in the market. From this information, it is possible to build maps of how market participants build up positions and how asset bubbles develop over time. By tracking price action on a tick-by-tick basis, it is possible to infer the composition of those bubbles similar to the work of geologists studying rock formations. Researchers can identify, who has been buying and selling, on what time horizons they trade, how resilient they are to price shocks, what makes them turn their position and become net sellers as buyers. Based on this information we can make inferences of the likely collapse of those bubbles.

High-frequency finance opens the way to develop "economic weather maps". Just as in meteorology, where the large scale models rely on the most detailed information of precipitation, air pressure and wind, the same is true for the economic weather map. We have to start collecting data on a tick-by-tick level and then iteratively build large scale models. Today, the development of such a global economic weather map has barely started. The "scale of market quake" (a free Internet service) is a first instalment, but the start of an exciting development.

High-frequency finance holds out the hope of turning aspects economics and finance into a hard science by the sheer volume of data and its ability to set events into their appropriate context by mapping rare events into a short-term time scale with a near infinity of events, albeit at a shorter-term time scale. Second, the tracking of events on a tick-by-tick basis opens the door to identify underlying flows and develop economic weather maps. Surely that’s not a bad thing?

References

Bisig T, Dupuis, A, Impagliazzo, V, and Olsen, R (2009), “The scale of market quakes”, working paper, September.

Gençay, R, Dacorogna, M, Müller, U, Olsen, R, and Pictet, O (2001), An Introduction to High Frequency Finance, Academic Press.

Mandelbrot, B (1997), Fractals and Scaling in Finance, Springer.

Mandelbrot, B, Hudson, R (2004), The (Mis)behavior of Markets, Basic Books.

Republished with permission of VoxEU.org

Controlling vs Collateralizing Risk

Regulatory reforms that focus on improving risk controls rather than increasing capital reserves are the better path for the future of banking, according to Katsunori Nagayasu, president of the Bank of Tokyo-Mitsubishi UFJ and chairman of the Japanese Bankers Association (“How Japan Restored Its Financial System,” WSJ, Aug 6, 2009).

Regulatory authorities around the world are currently discussing ways to prevent another financial crisis. One idea is to mandate higher levels of capital reserves. Japan’s banking reform shows that a comprehensive solution would work better.

Requiring banks to increase capital reserves is itself, “risky.” For one thing, banks may not be able to raise sufficient capital in the equity markets to meet the revised capital requirements. Moreover, raising capital requirements tends to disadvantage banks that focus on traditional borrowing and lending transactions, and advantage banks that trade and take risks with their own accounts.

A new regulatory framework must also distinguish between banks whose main business is deposit taking and lending—the vast majority of banks worldwide—and banks that trade for their own account. The recent financial crisis demonstrated that balance sheet structure matters. Trusted banks with a large retail deposit base continued to provide funds to customers even in the depths of the crisis, whereas many banks that relied heavily on market funding or largely trading for their own account effectively failed. Investment banks with higher risk businesses by nature should be charged a higher level of capital requirement—otherwise, sound banking will not be rewarded.

That the government has undertaken to save only the largest banks under the “too big to fail” presumption is of concern to the public for a variety of reasons, not the least of which is that such an approach may actually reward the banks that are taking the biggest risks, while closing those that have played by the rules. Additionally, requiring banks to maintain excessive capital reserves may sound good, but high reserves brings reduced capital efficiency, particularly at a time when money is scarce.

Regulators would be wise to consider the capital efficiency of the reforms they intend to invoke, or the current recession could extend well into the future. The US should take a lesson from the Japanese banking experience and focus on new ways to control risk, rather than simply collateralizing it.

The Minds Behind the Meltdown

In an effort to assign responsibility for the Wall Street financial meltdown that began in 2007, Scott Peterson (2010) makes the case that "quants" were the demons that somehow misled investment managers and the public:

On Wall Street, they were all known as "quants," traders and financial engineers who used brain-twisting math and superpowered computers to pluck billions in fleeting dollars out of the market. Instead of looking at individual companies and their performance, management and competitors, they use math formulas to make bets on which stocks were going up or down. By the early 2000s, such tech-savvy investors had come to dominate Wall Street, helped by theoretical breakthroughs in the application of mathematics to financial markets, advances that had earned their discoverers several shelves of Nobel Prizes.

Regarding the management teams that supervised these "quants," Peterson is mute. I tend to take the alternative view that management is responsbile for everything that happens or fails to happen within their domains. My question for the investment managers and policy makers is simply, "How could you be so dumb as to let your trading staffs run over you?" I tend to point the finger directly at the investment policies and greed of the investment managers and other senior executives rather than the traders. After all, the public can hardly hold staff traders responsibile for the devastating loses that ensued. The executive management teams of these investment institutions have a lot to answer for from my perspective. The minds behind the meltdown sit amongst the policy makers - not the traders.

Reference: Peterson, S (2010, January 23), The Minds Behind the Meltdown, Wall Street Journal Online.

Silver Hits New Highs

Silver is currently trading at above $40 per ounce -- the global debt crisis appears to be on the minds of at least some investors...

Risk Management in Demand

As the global financial disaster continues unabated, research is beginning to percolate findings about some of the causes of the storm, as well as the precautionary measures that might avert future crises of this nature. In a recent survey of over 500 key financial executives conducted by MPI Europe (April 2009), several important views prevailed. One of the survey's strongest findings was the perceived need to develop a “risk management culture” in today’s financial institutions, including bolstering the relative power of risk management functions vis-à-vis its trading counterparts. Now, as good as that sounds, I am skeptical as to whether our financial services industry has it within itself to embed a new risk-aware culture without demonstrable intermediate measures to lead the way (after all, our world is inspired by capitalism). The good news is that several other findings were more specific and actionable. Over 75 percent of the respondents saw a shortage of sufficiently and appropriately trained personnel as having a “high impact” on creating the crisis. Additionally, a significant majority of respondents wanted to see an improvement in their “risk management applications,” to include a shift from predominantly quantitative measures toward qualitative methodologies (e.g., internal controls). Both of these latter measures are fully actionable through increased investment in risk management technologies and training. Moreover, implementing stronger spreadsheet control regimes, coupled with stricter guidelines for spreadsheet checking and auditing, are another immediate requirement. Finally, I would argue that by funding and initiating improved risk management technologies and training, executives will be taking the first vital steps toward creating the risk management culture that they seek. The recognized need for effective risk management is gaining traction in today’s financial services industry. The real question remains whether the industry’s leaders will have the courage to recognize the deficiencies of their existing risk management structures, and respond by investing in the technologies and training that can address these shortcomings.