Behavioral Finance: Shorting 'First Announced' Stocks

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 |  Includes: DBC, DIA, EEM, GLD, GXC, PGJ, QQQ, SPY, TAO
by: Susan Weerts

Global trading volume has grown drastically in the last few decades. The accelerated growth of hedge funds and the involvement of a wider range of investors have lead to a surge of trading activity. Market participants with different beliefs utilize a wide spectrum of information sources and tools to detect pricing anomalies. Quants deploy sophisticated mathematic/statistic models to achieve higher absolute returns. Technical traders analyze the past price histories to identify trends. Fundamental analysts comb through vast sets of financial data to determine underlying asset values. Increasingly, these participants engage in some sort of speculative trading. Even for those who claim to be fundamental analysts, most of their information sources are third-party based instead of from insider information. The heterogeneity of these speculative trading provides the liquidity to the market. In a normal-functioning market, speculative trading fosters price discovery and ensures market efficiency.

However, when one-sided opinions drown out opposing opinions and heterogeneous beliefs arise from the agents of overconfidence, it induces herding behavior and the market ceases functioning rationally. Myriad researches, from live cattle futures to Treasury bills, demonstrate that speculative herding destabilizes the market by creating bubbles. Speculative herding poses a severe threat to financial stability and expands global contagion.

Many financial regulations have been proposed to contain these contagions. However, Scheinkman and Xiong (2003) of Princeton University find that regulations such as the Tobin tax might reduce speculative trading, but would have a limited effect on the size of bubbles and price volatility.

The recent boom and bust illustrates how painful speculative herding can be. When investors, large and small, buy into the notion that an asset will always go up, speculative herding pushes the asset values to a levels that even the best growth scenario can’t obtain. A bubble is inevitably formed.

The opposite is equally harmful. In his keynote speech at the TCFA annual conference in November 2009, Dr. Sanford Grossman, founder of QFS Asset Management and the recipient of 1987 John Bates Clark Medal, points out that the panic behavior caused by speculative herding incurred excess damage in the recent financial meltdown. When everyone rushes out of the door at the same time, the financial markets inevitably drop to the floor.

Herding and Flocking

Investing is a social activity. Herding occurs when the market participants seek signals from others in matters of knowledge and behavior and therefore align their convictions with the leading voices within the group. More than ever, through various social networks such as conferences, media, financial blogs, even chat rooms and investment clubs, market participants exchange their perspectives, share the hot stock tips and often engage in passionate debates with others who hold opposing opinions around the world. These social interactions allow information dissemination and facilitate global financial market integration. Investors then incorporate the feedback into the decision-making process to extrapolate the underlying asset value and execute trades accordingly.

Often, the market participants gravitate to a set of “Expert” opinions. These experts are usually professionals who have better information accesses and successful track records. Prechter (2001) pointed out that ubiquitous herding behavior in the financial market, even among long-term investors, are impulsive responses to signals from the behavior of others. The net present value of future cash flows becomes meaningless as the result of herding. He wrote:

(Herding is) dependence upon the behavior of others most easily substitutes for rigorous reasoning when knowledge is lacking or logic irrelevant. In a realm such as investing, where so few are knowledgeable, or in a realm such as fads and fashion, where logic is inappropriate and the whole point is to impress other people, the tendency toward dependence is pervasive. Trends in such activities are steered not by the rational decisions of individual minds but by the peculiar collective sensibilities of the herd. … To forecast on the basis of the current sentiments of the herd is to “forecast” the present mood, not future events. Success is simply a matter of whether the present mood maintains, which it usually does not.

Hump-shaped Asset Pricing Pattern

In his paper “Stock Prices and Social Dynamics”, Dr. Robert Shiller proposed a model of the diffusion of opinions to incorporate social psychology in determining prices. His model offers the same intuitive as George Soros’ Reflexivity. Dr. Shiller decomposes asset prices into its fundamental component and the speculative component from the demands of investors who overreact to news or are vulnerable to fads. I.E.

Under his model, smart-money investors rationally respond to fundamentals, ie, they only buy the unvalued assets and short-sell the overvalued assets. Speculators engage in trades in reaction to capricious fashions or fads, past returns, and dividend process that are unwarranted given the actual processes. Yt+k can be viewed as the speculative value of asset per share. Φ is the risk premium that would induce the Smart Money to initiate a position in consideration of future speculative activities. The prices of speculative assets have a hump-shaped pattern and exhibit a short-term momentum and a long-term reversal, which coincides with Dr. Jean-Paul Rodrigue’s pattern of bubble. For simplicity, we will use Dr. Rodrigue’s graph to illustrate how an asset price evolves with the speculative demands.

The process is initiated by the Smart Money taking positions on an undervalued asset. Dr. Shriller sees an instantaneous price jump as the Smart Money reacts to the positive news. Dr. Rodrigue envisions a gradual, stealth increase as the smart money quietly takes advantage of the emerging opportunities (which also could bear a substantial risk.) Either way, the Smart Money’s actions create a buying signal.

When professional speculators with better analytical tools, mostly institutional traders, detect this signal, they will jump in for a ride. This creates an autocorrelation in daily trading volume. Their actions might even push the price above its fundamental value. The increasingly popular strategies of “follow smart money” and “hedge fund replicators”, such as “Alphaclone”, are geared to harness the Smart Money’s buying signals,

By participating in the action, the professional speculators amplify the buying signal and magnify the price momentum. The continuous momentum will grab the general public’s attention, which will lead a wider spectrum of traders into buying. Studies show that a growing number of retail investors tends to put their money into the stocks widely-held by institutional traders and the stocks in the news. Barber and Odean (2008) observe:

The individual investors display attention-driven buying behavior. They are net buyers on high-volume days, following both extremely negative and extremely positive one-day returns, and when stocks are in the news.

The general public interests in the Mania phase propel the asset price way above its fundamental value. The past positive returns fuels the buying frenzy which risks the asset into a bubble. Yung and Liu (2009) find that speculators trade more aggressively following gains in futures market and the related stocks. They grow overconfident, assuming more risk and overestimate investment returns. Scheinkman and Xiong (2003) conjecture that this overconfidence is the result of an agent’s belief that his information is more accurate than it actually is. This overestimation of precision of the signals leads to higher trading frequency and price volatility.

In addition to overconfident speculative trading behaviors, the professional advice and research that are utilized by most fundamental analysts can be equally misleading. Lee and etl (2008) demonstrate the heuristic behaviors within financial analysts. That is, in formulating their forecasts, financial analysts often put more weight on the current state of the economy than the future one in terms of the business cycle. They tend to be over-optimistic during times of business expansion and overestimate the growth potential. Optimism is far more pronounced in the glamour stocks.

Hong, Scheinkman and Xiong (2008) observe that even a well-intentioned advisor can give an upward biased recommendation when facing the unknown, such as brand-new technologies. The upward bias of professional analysts does more damage to the retail investor, than to institutions. Retail investors in general take professional reports at their face value. Institutional investors are more likely to impose some de-bias processes in dealing with sell-side reports.

In contrast to public beliefs, Dr. Shriller observes: “Returns, however, will tend to be low during the period of price rise.” It is difficult to generate a return in the overcrowded space. Additional evidence can be found in an empirical study by Dr. Yu and Kim in their 2009 paper. In their study of the hot-growth stocks in Business Week, they finds that investors who purchased growth stocks appearing for the first time in Business Week ,suffered a significant loss, i.e., 42% in the first year, 37% in the second year and 33% in the third year on average. However, they also find that the growth stocks appearing repeatedly in the list enjoy a positive return. Hence, they recommend buying the repeated one and shorting the first timers.

Disclosure: No position