are three possible reasons for this lack of effectiveness:
1 the difficulty in identifying arbitrage opportunities,
2 arbitrage is costly and risky, or
3 there are few if any market participants willing to engage in arbitrage.
Clearly stocks are difficult to value and so there is validity to the first reason. But even when the price distortion can be accurately estimated, such as with closed-end funds, the distortions persist. Cost and risk clearly make arbitrage difficult, but one would think that there is sufficient incentive to attract a large number of arbitragers into the stock market.
However, recent results by Bradford Cornell of the California Institute of Technology, and Wayne Landsman and Stephen Stubben, both of the University of North Carolina, are discouraging in this regard. They find a tendency for both mutual funds and sell-side analysts to exacerbate sentiment-driven price movements, rather than dampen them as one would expect of supposedly rational investors. That is, institutional professionals tend to join the Emotional Crowds rather than act as BDIs.
David McLean of MIT and Jeffrey Pontiff of Boston College explore the limits when arbitraging academically-identified anomalies. Starting with a sample of 82 such anomalies, they find that two-thirds of resulting excess returns remain even five years after publication. Furthermore, they find that the effectiveness of arbitrage has not improved in recent years, even with steep declines in transaction costs and the greater dominance of supposedly rational institutional investors.
Indeed, emotion trumps arbitrage.
Finally, Hersh Shefrin’s insightful observation is of interest:
“Finance is in the midst of a paradigm shift, from a neoclassical based framework to a psychologically based framework. Behavioral finance is the application of psychology to financial decision making and financial markets. Behavioralizing finance is the process of replacing neoclassical assumptions with behavioral counterparts. … the future of finance will combine realistic assumptions from behavioral finance and rigorous analysis from neoclassical finance.” [6]
Thus Basic Principle I, that Emotional Crowds dominate pricing, is a logical first step in building an effective decision process for investing.
Basic Principle II: Behavioral data investors earn superior returns
Basic Principle I would seem to open the door for BDIs to earn superior returns by taking positions opposite the Crowd. This is not necessarily the case, since even though there is little doubt emotions increase volatility, the resulting distortions might be random and unpredictable, making it difficult if not impossible to take advantage of them. So beyond the fact that emotions drive prices, it is necessary to show that the resulting distortions are measurable and persistent.
The behavioral finance literature is full of examples of measurable stock price distortions. [7] It would therefore seem easy to build superior performing portfolios, but in order to do so means taking positions that are different from the Crowd. The powerful need for social validation acts as a strong deterrent for many investors, discouraging them from pursuing such an approach. It is tough to leave the Emotional Crowd and become a BDI. Thus price distortions are measurable and persistent, but building a portfolio benefiting from these distortions is emotionally difficult.
In order to demonstrate that it is possible to earn superior returns, I turn to active equity mutual fund research. This group of investors is one of the most studied in finance because of the availability of extensive, long-time-period data. One stream within this large body of research reveals that active equity funds are successful stock pickers. [8] Rather than focus on long-term fund performance, these studies examine individual fund holdings and confirm that a fund’s top stock picks produce superior returns. [9] The most compelling results are reported by Randy Cohen, Christopher Polk and Bernhard Silli (CPS), and these are reproduced in Figure 1.1.
Figure 1.1: Analysis of funds’ top stock picks
Based on Graph 3 in Cohen, Polk, and Silli (2010). The graph shows, over the subsequent quarter, the average six factor adjusted annual alpha for the largest relative overweighted stock in a mutual fund portfolio, the next most overweighted, and so forth. Based on all active US equity mutual funds from 1991 to 2005.
Figure 1.1 reveals that a fund’s best idea, as measured by the largest relative portfolio weight, generates an average six factor, annualized after-the-fact alpha of 6%. What is more, the next best idea stocks also generate positive alphas. This is evidence that it is possible to build a superior stock portfolio. CPS did not explore the source of these returns, but it is reasonable to conjecture that much of the return is the result of fund BDIs (buy-side analysts and portfolio managers) harnessing behavioral factors. Probably of less importance is the investment team’s ability to build a superior information mosaic for the stocks in which they invest. [10]
Reconciling two stock picking skill research streams
A better known conclusion from this line of research is that the average active equity mutual fund earns a return that is less than, or at best equal to, the index return. [11] That is, the average fund earns a zero or negative alpha. This leads to the oft-stated conclusion that equity fund managers lack stock picking skill, which is in fact the opposite of what was just presented.
One would think that professional investors, such as mutual funds, hedge funds, and institutional managers, would be BDIs. Indeed, the analysts within such organizations are most often BDIs, but the further up one goes in the organization and the larger the fund, the more like the Crowd it becomes.
In order to grow AUM, funds must attract and retain emotional investors, which means the fund often caters to client emotions and thus ends up taking on the features of the Crowd. As the fund grows in size, it increasingly invests in those stocks favored by the Crowd, since it is easier to attract and retain clients by investing in those stocks to which clients are emotionally attached. [12] What often starts out as BDIs harnessing behavioral factors ends up with a fund morphing into something that is acceptable to the Crowd, a process I refer to as “bubble wrapping” the portfolio. Such behavior is rational on the part of the fund, as revenues are based on AUM. [13] Consistent with this argument, others have found that returns decline as the fund grows large. [14]
The combination of the many documented price distortions and the excess returns earned by active equity mutual funds on their best idea stocks provides empirical support for basic principle II. But many investors will find it more difficult to assimilate principle II than principle I, since the emotional barrier of social validation must be overcome in order to build a successful BDI portfolio.
Basic Principle III: Investment risk is the chance of underperformance
There is no more confusing issue regarding the role of investor emotions than how to measure investment risk. Those measures currently used to capture investment risk, once carefully examined, are mostly measures of emotion. As an example take volatility, as measured by return standard deviation. Earlier I reviewed the evidence regarding stock market volatility which concludes that most volatility is generated by Crowds overreacting to information flowing into the market. Indeed, almost none of the current volatility can be explained by changes in underlying economic fundamentals at both the market and individual stock level.
