Predatory Trading and Crowded Exits

New thinking on market volatility

James Clunie

• Publisher: Harriman House
• Year: 2010
• ISBN:
  9780857191519
• Edition: 1
• Topic: Business
• Subtopic: Investments & Securities
• Index: Business

In this book, James Clunie looks at a series of market phenomena that involve security prices moving temporarily away from their 'fair value', creating opportunities for traders to profit (and the risk of losses for the unaware).These phenomena have only recently begun to be well understood and key among them are those known as 'predatory trading' and 'crowded exits'. The author examines these on three levels. Firstly, he describes the basic principles and theory behind each phenomenon, to build a solid framework for the way a trader should think about these situations. Secondly, he examines the accumulated empirical evidence of these situations. This gives an idea of what generally happens in these situations, and what the profit opportunity and the risks might be like. Finally, the author considers a number of individual cases to illustrate what can happen to traders in practice. Often, these will be special situations or extreme events from history, but always cases from which the trader can learn.By understanding these phenomena thoroughly in this way, a trader can gain an edge over others in the market. In the first instance by avoiding becoming the victim of the phenomena and secondly by using detailed knowledge of these situations to (legally and ethically) profit from the events.This book is for traders looking to gain an edge through a superior understanding of how markets work, both in theory and in practice. It will also be of interest to longer-horizon investors who are seeking to avoid timing errors, and to risk managers wanting to understand better the subtleties of risk beyond traditional risk statistics.

Chapter 1. The Ecology of Markets

Fair value

What is the right price for an asset?

A common way of thinking about this problem for a security, such as a stock or a bond, is in terms of its fair value. This is the notion that there is a single value that the security is intrinsically worth at any given time.

A rigorous way in which to think about the intrinsic value of a security is to consider the future cash flows that the security will generate for the owner, and then discount those cash flows back to today’s money. This concept relies on the idea that an expected cash flow at some future date is less valuable than money in hand today, because of the opportunity cost of not having access to the cash today, and the risks associated with future events. Where an investor or trader knows the future cash flows from a security for certain, and knows the rate at which to discount them, calculating the fair value of that security is simple – a few lines of work on a spreadsheet.

But, in practice, things are not so easy.

Consider, for example, a bond issued by a highly credit-worthy government. The cash flows are documented in the bond’s prospectus and are known almost with certainty: each regular coupon payment and the return of principal at maturity of the bond are highly likely to transpire. So far, so simple.

But what is the correct discount rate?

Ambiguity over the appropriate discount rate makes it difficult in practice to estimate the fair value of a security. Analysts develop techniques for coping with this problem – one of the most popular of which is taking the implied discount rate from similar securities, and using this to discount the cash flows from the security in question.

Now consider a riskier security, such as a corporate bond. In this case, the cash flows to the bond’s owner are less certain, as default risk is now higher. Consequently, the intrinsic value of a corporate bond is thus (generally) more difficult to evaluate than for a government bond.

And difficulties do not end with corporate bonds.

Equities offer greater problems – think about the unreliability of cash flows and dividends attributable to shareholders. An extreme example would be, say, a biotechnology company, where cash flows might be zero for the foreseeable future and in the long-run might depend upon success in developing new drugs. Long-term cash flows could be huge…or non-existent!

Uncertainty over both cash flows and the appropriate discount rates leads to great uncertainty over the fair value of a security. Two analysts, each using the same theoretical discounted cash-flow approach, could place very different values on the same security, depending on their cash-flow projections and the discount rates they choose to use. Because of such ambiguity over the true value of a security, some analysts dismiss the notion of a fair value. Instead, they think of securities as having observed market prices and estimated cash flows, and simply use the implied discount rate in the market to compare securities to one another.

Many financial models assume a fair value exists

Whether or not it is sensible in practice to think about the fair value of a security, a number of financial models assume that there is such a thing as the fair value of a security. A quick trawl through published articles and working papers on asset pricing reveals that this is a very popular assumption in academic work. Some models go further still: not only do they assume that there is a fair value to a security, but this fair value is known with certainty to some market actors, such as arbitrageurs. But how do the arbitrageurs know the fair value of a security? Most papers are mute on this subject.

Why would a model-builder make such an assumption?

Mainly because it creates a framework for thinking about markets which, through further analysis, can provide illumination on how markets work. The assumption that fair value can be known with certainty might come as a surprise to some arbitrageurs. To know the fair value for say, a stock, seems like a hopelessly unrealistic assumption. However, academic work sometimes makes simplifying assumptions, to reduce the complexity of a situation, and to make the mathematics more tractable.

According to Friedman (1953), the use of unrealistic assumptions does not invalidate the work, so long as the predictions are accurate. Thus, the notion of a fair value that is known to some, but not all, market actors is a simplifying assumption to help us understand the actions of arbitrageurs and the workings of markets. It is worth bearing this in mind when looking at financial models that rely upon such assumptions. Models can provide illumination on how markets work, but a trader must avoid the mistake of relying wholly upon the predictions of models.

The problem of simplifying assumptions

Some asset-pricing models make a further, important simplifying assumption about the process of arbitrage. They assume that traders can short-sell securities as easily as they can buy. For example, a widely-taught asset pricing model, known as the arbitrage theory of capital asset pricing (Ross, 1976), assumes that there are no restrictions on short-sales, including full use of the short-sale proceeds. However, in practice, short-sellers must find securities to borrow, effectively pay securities lending fees and face collateralisation and margin requirements. These short-sale constraints limit the frequency and scope of arbitrage, and so could affect the price of assets.

So, we know that some models that seek to explain the pricing of assets make use of unrealistic simplifying assumptions.

Does this matter?

Are the predictions from such models accurate, or do they instead fail the ‘Friedman test’ that I mentioned earlier? In a canonical paper on short-selling constraints, Miller (1977) considers what happens to security prices if the two main assumptions discussed above are untrue at the same time.

While popular models such as the capital asset pricing model (see Sharpe, 1964) assume that investors have identical estimates of the expected return and probability distribution of returns from all securities, Miller suggests that investors in practice can have differing expectations about securities instead, due to uncertainty over future cash flows and the appropriate discount rate for an investment. He argues that when a divergence of opinion amongst investors is combined with barriers to short-selling, the price of a security is no longer set by the average investor, but instead by the beliefs of the most optimistic investors. Those investors with the most optimistic estimates of returns will own the securities, while pessimists and realists struggle to short-sell the overpriced asset because of constraints on short-selling. Miller concludes that

the presence of a substantial number of well informed investors will prevent there from being substantially undervalued securities, but there may be securities whose price has been bid up to excessive levels by an uninformed minority.

This provides a simple explanation for why some securities might trade at inflated prices. Even if informed traders or investors know the fair value of a security, other less-informed traders push the price beyond that level, and it is difficult to short-sell the security back to its fair price. Mis-pricing develops because of ambiguity over fair value; and arbitrageurs are unable to correct the anomaly if there are barriers to short-selling.

A number of researchers have investigated Miler’s idea. Although there is some dispute over its implications, Asquith et al. (2005) state

that it is now widely accepted that if short-selling is costly and there are heterogeneous investor beliefs, a stock can be overvalued and generate low subsequent returns.

For a trader, the lesson is simple – asset pricing models can help us understand how markets work, but where the model relies upon simplifying assumptions, the predictions from the models might not always be accurate. A good trader should understand both the predictions of the model and the limitations of the model. Without both, a trader will be vulnerable – even if this vulnerability takes years to be revealed.

Informed traders versus noise traders

A noise trader is simply a trader who holds no new information about a security. Any knowledge upon which he trades is assumed to be already imputed in the security’s price. Given this definition, it might seem that noise traders would be largely irrelevant to the functioning of markets. However, Gemmill and Thomas (2002) argue that the setting of prices in a market is determined through the interactions of arbitrageurs and noise traders. Furthermore, many models for understanding how security prices are set are based on the notion that a market comprises informed traders (those who know the fair value of a security) and noise traders (those who do not know).

Who are these noise traders?

Although rarely made explicit, noise traders are implicitly assumed to include non-professional traders (e.g. retail investors) – even though it is likely that at least some retail investors have better investing track records than some professionals. Noise traders might also include traders forced to trade because of a need for liquidity. Dow and Gorton (2006) argue that “noise traders play an important role in modern finance theory”, but state that their “identities, motivations and ability to persist” are not well understood.

In other words, we do not know much about a group of people that we believe plays an important role in the workings of markets. This is quite some confession!

Noise traders can have both benign and adverse effects on markets. Black (1986) argues that with more noise trading, markets will be more liquid, in the sense of having frequent trades that allow prices to be observed. However, security prices will reflect both the information upon which information-traders trade, but also the noise upon which noise-traders trade.

As noise trading increases, information trading becomes more profitable, because of the greater noise contained in prices. However, apparent ‘information’ may already be reflected in security prices, making it difficult to differentiate information from noise. Noise can create the opportunity for profitable trading, but simultaneously makes it difficult to trade profitably.

Even without short-sale constraints, the existence of noise trading means that securities need not be rationally priced, and arbitrage becomes risky. Information can give a trader an edge, but not a guaranteed profit. Consequently, informed traders will not take large enough (i.e. risky enough) positions to eliminate the noise.

Black surmises that it will be difficult to show that information-traders perform better than noise-traders, and argues:

there will always be a lot of ambiguity about who is an information trader and who is a noise trader.

Noise traders, through their uninformed trades, can set up mis-pricing opportunities for better-informed traders to exploit. But noise traders can also overwhelm informed traders, if their scale is large and they trade in a similar fashion to one another.

Noise traders should thus be applauded for creating opportunities for traders, but also feared when they move as a pack. Superior knowledge alone is not enough to guarantee success as a trader. We know from financial history that even well-informed arbitrageurs can be quite vulnerable.

Why smart arbitrageurs don’t always win…

Shleifer and Vishny (1997) describe one of the ways in which a well-informed arbitrageur can fail. A textbook description of arbitrage suggests that the process requires no capital, entails no risk and generates guaranteed and immediate profits. This kind of arbitrage would bring prices towards equilibrium and keep markets efficient. However, the authors argue that:

the textbook description does not describe realistic arbitrage trades and, moreover, the discrepancies become particularly important when arbitrageurs manage other people’s money.

Types of arbitrage that appear to be simple, such as that between two similar bond futures contracts traded on different exchanges, can take on the characteristics of risk arbitrage when considered fully. Even mechanically hedged arbitrage positions, such as long stock/short future, can result in financial distress if the arbitrageur earns paper profits on the stock leg but is unable to meet the cash requirements arising from losses on the futures leg. Risk arbitrage bears risk of loss and requires capital – an important distinction from the textbook definition of arbitrage.

The role of clients

Furthermore, the model of arbitrage assumed in many popular asset pricing models is inconsistent with how arbitrage is practised in financial markets. Instead of vast numbers of small arbitrageurs, arbitrage is conducted in practice by relatively few specialised professionals, who generally use outsiders’ money to take large positions. An ...