Animal Spirits

Is overreaction pervasive? Is it resistant to learning, or does experience temper it? Is overconfidence a driver of overreaction? How does overreaction affect portfolio performance? Two related July 2009 papers entitled “Overreaction in Stock Forecasts and Prices” by Alen Nosic and Martin Weber and “Overreaction and Investment Choices: An Experimental Analysis” by Bruno Biais, Alen Nosic and Martin Weber tackle these questions experimentally. Using somewhat informed university students as subjects, they conclude that: Keep Reading

How real and substantial is confirmation bias as an inhibitor of individual investor and market belief adjustments? What factors affect its impact? In their July 2009 paper entitled “Feeling Validated Versus Being Correct: A Meta-Analysis of Selective Exposure to Information”, William Hart, Dolores Albarracin, Alice Eagly, Inge Brechan, Matthew Lindberg and Lisa Merrill survey a broad selection of past research measuring the degree to which people favor information that supports pre-existing attitudes, beliefs and behaviors over information that challenges pre-existing attitudes, beliefs and behaviors. Using results from 67 reports encompassing 91 separate studies of 300 statistically independent groups comprised of nearly 8,000 participants, they conclude that: Keep Reading

Does the conventional wisdom to “sell in May,” with the average stock return during November-April far exceeding that for May-October, work for the world equity market? If so, why? In the November 2005 version of his paper entitled “The Optimism Cycle: Sell in May”, flagged by a reader, Ronald Doeswijk examines the hypothesis that this seasonal pattern derives from an annual optimism cycle. Using monthly return data for markets, sectors and Initial Public Offerings (IPO) over the period 1970 through 2003 (34 years), he concludes that: Keep Reading

Do stock traders learn rationally from past trading experience? In the March 2009 version of their paper entitled “Trading and Learning: How Different Are Different Categories of Investors?”, Sankar De, Vishal Mangla, P. Bhimasankaram and Simran Singh investigate how different categories of traders revise their beliefs about the prospects for future trading success in response to past trading experiences. Using a sample of 124 million transactions on the National Stock Exchange of India within 1.2 million accounts over the period April 2006 through June 2006, they conclude that: Keep Reading

Are the marketers right in believing that $1.99 attracts many more buyers than $2.00? In other words, do buyers irrationally fixate on the left-most digit of a price? If so, is there a way to exploit any associated stock trading tendencies? In their November 2008 paper entitled “Penny Wise, Dollar Foolish: The Left-Digit Effect in Security Trading”, Utpal Bhattacharya, Craig Holden and Stacey Jacobsen investigate the extent to which traders anchor on the left digit in stock prices. Using 100 million randomly selected stock trades during normal trading hours that are either above (buys) or below (sells) the bid-ask midpoint during 2001-2006, they conclude that: Keep Reading

In his 2008 book, Predictably Irrational: The Hidden Forces That Shape Our Decisions, behavioral economist Dan Ariely “refutes the common assumption that we behave in fundamentally rational ways.” Many of his observations have implications for investing and trading from the perspectives of avoiding irrationality as individuals and exploiting the systematic irrationalities of others. Based on his past studies of irrational behaviors, he concludes that: Keep Reading

Does interaction with peers significantly affect the choices of individual investors? Are some individuals more susceptible to such pressure than others? In their April 2008 paper entitled “Susceptibility to Interpersonal Influence in an Investment Context”, A. Hoffmann and Thijs Broekhuizen investigate how interpersonal influences affect the investment decisions of individuals and which individuals are most susceptible to such influences. Combining the results of a laboratory experiment involving 154 university students and a survey of 287 investors, they conclude that: Keep Reading

Do the attention-grabbing past returns of high-flying stocks produce pre-earnings announcement buying frenzies? In the April 2008 version of their paper entitled “Limited Attention and the Earnings Announcement Returns of Past Stock Market Winners”, David Aboody, Reuven Lehavy and Brett Trueman examine whether the limited time and resources of small investors explains a striking return pattern around the earnings releases of firms with extremely strong prior year price momentum. Using daily stock return data and earnings release/forecast news from the beginning of 1971 through the third quarter of 2005 for a broad sample of companies, they conclude that: Keep Reading

Do northern hemisphere seasonal variations impact stock market volatility and return by affecting aggregate investor/trader mood? In their April 2008 paper entitled “Seasonal Affective Disorder (SAD) and Perceived Market Risk”, Guy Kaplanski and Haim Levy test the effect of seasonal environmental factors (daylight hours, temperature and fall season) on perceived market risk as indicated by the Chicago Board Options Exchange Volatility Index (VIX). VIX, also known as the Fear Index, is a measure of the risk perceived by traders of S&P 500 index options. Using VIX and actual volatility data and environmental measurements (for latitude 41 degrees north, Chicago and New York) over the period 1990-2007, they conclude that: Keep Reading

Does bias on the part of U.S. investors in favor of companies headquartered within their home states create opportunities for geographical abnormal returns? In the February 2008 draft of their paper entitled “Long Georgia, Short Colorado? The Geography of Return Predictability”, George Korniotis and Alok Kumar investigate whether the behavior of local investors in response to local economic conditions produces predictable patterns in the returns of local stocks. They define local as individual U.S. states. They define local economic conditions as the combination of: (1) growth rate of state labor income; (2) state unemployment rate relative to a moving average; and, (3) state-level housing collateral ratio (as a measure of borrowing constraints). Using quarterly state-level economic data, company headquarters locations and quarterly stock return data for 1980-2004 (covering a total of 39 states as limited by sample size), they conclude that: Keep Reading