Efficient Market Hypotheses

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In his 2006 book Hedgehogging, investor Barton Biggs described the angst of fellow hedge fund managers trying to earn alpha. Explained one exasperated manager: “The larger capital and the bigger talent pool now being deployed by hedge funds mean that the pricing of everything from asset classes to individual securities is under intense scrutiny by manic investors, who stare at screens all day, have massive databases, and swing large amounts of money with lightning speed. This has the effect of bidding up the prices and reducing the returns of all mispriced investments. Obvious anomalies now disappear, almost instantly. In effect, the alpha available for capture by hedge funds has to be spread over more funds with bigger money, resulting in lower returns on invested capital for hedge funds as an asset class.” Another lamented: “It‟s a jungle in [global] macro right now. There are so many macro players… they‟re bumping into each other. There must be a couple of hundred new macro hedge formed in the last six months… Some of these guys are so green, they can confuse you with their stupidity, and they are big and clumsy. It‟s all very disorienting!”1 Through the lens of the Efficient Market Hypotheses (EMH), Andrew Loi and Burton Malkielii attempt to explain why these phenomena might occur, what signals prices offer in the short- and long-runs, and whether or not EMH actually holds in practice. Lo‟s study settles on biological and evolutionary theories to explain behavior of security prices and investors, while Malkiel leans on his random walk theory to describe market efficiency. Ultimately, both share the mixed conclusions that, despite non- persistence of rationality, which may cause short-run mispricing, conveyance of information is indeed reflected in a stock‟s price. The joy of market inefficiency, then, is that it is ephemeral. Security prices are signals – signals to buy or sell, signals of relative valuation, and so on – and, if they happen to be incorrect, then investors who can identify that information will exploit the mispricing until it is negated. Over time, stocks do exhibit behavioral patterns, caused by innumerable factors. These patterns, like stocks themselves, have a lifecycle. In infancy, a few traders may notice that a stock or asset class shows a distinct signal – the January effect, for instance, and they will trade profitably on their proprietary knowledge. However, as patterns become increasingly exploited, traders‟ ability to act profitably shrinks, and the effect eventually changes entirely or dies. Quantitative traders, proprietary desks, and hedge funds – indeed, the hobgoblins of Biggs‟ anecdote – will profit and profit until no alpha remains, so that “the more potentially profitable a discoverable pattern is, the less likely it will survive.” (Malkiel, 72) Richard Roll summed up this frustrating experience succinctly: “I have personally tried to invest money… in every single anomaly and predictive device that academics have dreamed up… And I have yet to make a nickel on any of these supposed market inefficiencies… a true market inefficiency ought to be an exploitable 1 Biggs, Barton. 2006. Hedgehogging. Hoboken, NJ: John Wiley & Sons, Inc.

opportunity. If there‟s nothing investors can exploit in a systematic way, time in and time out, then it‟s very hard to say that information is not being properly incorporated into stock prices.” (Malkiel, 72) While Malkiel‟s work is not as reliant on behavior as Lo‟s, it does share some commonalities. For one, high volatility, such as was experienced in October 1987, may, in fact, be explained by fundamental factors conspiring to drive fear into the market. A decline in US Treasury levels – perhaps indicative of heightened appetite for equity risk – combined with a threatened merger tax and falling US Dollar exchange rates likely changed risk perceptions. Investors, then, were not entirely irrational to sit on the sidelines as nearly one-third of the market‟s value fell. In reality, risk premiums incorrectly reflected the underlying fundamentals of the market, causing correction. Similarly, the technology stock bubble caused a misallocation; in this case, of capital funding. Investors, fooled by irrational exuberance poured so much money into the asset class that some eventually needed to be pulled back, resulting in a correction of prices. In both cases, investors acted irrationally – then, rationally. The overriding theme of Lo‟s work is that the markets are a living, dynamic organism, and that natural laws such as evolution, human cognition, and experiential-based behavior contribute to their behavior. Synthesized, these elements lead to the adaptive markets hypothesis (AMH), an attempt to “reconcile the EMH with all of its behavioral alternatives.” He writes, “One particularly promising direction is to view the financial markets from a biological perspective and, specifically, within an evolutionary framework in which markets, instruments, institutions and investors interact and evolve dynamically according to the law of economic selection. Under this view, financial agents compete and adapt, but they do not necessarily do so in an optimal fashion.” (14-15) It is not hard to overlay Lo‟s framework on Malkiel‟s findings. Since they evolve, markets do tend to correct, which satisfies Lo‟s reconciliation of EMH as well as Malkiel‟s evidence of investor behavior in particular cases. In fact, Malkiel even blames the technology bubble partially on “psychological contagion.” (61) Lo goes even further to note that investor risk tolerance in 2000 was strongly influenced by a large population of investors who had never experienced a true bear market. (20) Why else, then, might markets act inefficiently in the short-run? Both authors cite news as an overriding factor for some price discrepancies. Investors tend to react inappropriately to news, which is by nature unpredictable, yet empirical research suggests that markets eventually revert to the mean in the long-run. This is evidence for Lo‟s assertion that organisms learn and adapt – and that, given enough mistakes, weak investors and investment strategies eventually give way to stronger, fitter investors and strategies. Borrowing from Darwinian theory, survival of the fittest truly rules the markets. (19) As for market characteristics, Lo asserts that diverse groups of investors competing for scarce resources (i.e. alpha) in large, well-developed markets (e.g. the 10-year U.S. Treasury market) will

effectively and quickly move prices towards equilibrium. This theory may not hold up in less well- developed or smaller markets, so the AMH is highly dependent on context. Malkiel, for his part, uses small company outperformance versus that of large companies to suggest that if size is a more critical measure of risk – as opposed to the traditional beta – then the market‟s views on small companies is inefficient. Fama and French agreed, but argued that markets are in fact efficient and size is a better indicator of equity risk. Some trading strategies may generate persistently outsized returns over long periods. For instance, contrarian strategies take advantage of the tendency to revert to means in the long-run. Because positive serial correlation exists over a short horizon, stocks exhibit negative serial correlation and mean reversion over longer periods. (Malkiel, 63) According to Lo, “In some cases investors may overreact to performance, selling stocks that have experienced recent losses or buying stocks that have enjoyed recent gains. Such overreaction tends to push prices beyond „fair‟ or „rational‟ market value, only to have rational investors take the other side of the trades and bring prices back in line eventually. Another implication is that contrarian investment strategies – strategies in which „losers‟ are purchased and „winners‟ are sold – will earn superior returns.” (6) So, then, what do stock prices tell us – and is EMH doomed if the answer is little at all? Malkiel argues that even though markets may convey inefficient prices, they still utilize information efficiently. The incentive for investors to uncover information in order to exploit security mispricing is too great, and this information will be conveyed back to the price, usually sooner rather than later. Lo, too, concludes that investor behavior is to be blamed (or lauded) for stock price discrepancies and subsequent correction. He uses evolutionary psychology and cognitive neuroscience to explain that markets tend to move towards efficiency despite seeming randomness. Like Malkiel, he has concluded that certain profitable trading schemes will be exploited until no alpha remains, much like Biggs‟ global macro fund managers‟ experience. In time, we might assume that, assuming their complaints are true, that global macro will fall out of fashion according to Malkiel‟s and Lo‟s hypotheses. The lessons of Andrew Lo and Burton Malkiel are timeless, yet the irony of EMH is that our markets today suffer from extreme inefficiencies. The idea that information is transferred to investors via prices is meaningless when illiquidity has caused a dearth of useful data. Until assets are able to express information, not befuddlement, markets will look like the anomalies described herein. Perhaps these experiences will cause us to become more adept at spotting inefficiencies and their causes. i Lo, Andrew. “Efficient Markets Hypothesis” in The New Palgrave: A Dictionary of Economics, Second Edition, 2007. New York: Palgrave McMillan. ii Malkiel, Burton. “The Efficient Market Hypothesis and Its Critics” in Journal of Economic Perspectives, Volume 17, Number 1, Winter 2003.

Efficient Market Hypotheses

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Efficient Market Hypotheses — Document Transcript