Testing The Profitability Of Contrarian Trading Strategies Based On The Overreaction Hypothesis
National Bank of Belgium
Below is our 13F roundup for some high profile hedge funds for the three months to the end of March 2021 (Q1). Q1 2021 hedge fund letters, conferences and more The statements only include equity positions as 13Fs do not include cash and debt holdings. They also only include US equity holdings. Funds may hold Read More
IESEG School of Management
Louvain School of Management (UCL); Catholic University of Louvain (UCL) – Center for Operations Research and Econometrics (CORE)
September 17, 2014
Bankers, Markets, and Investors, Vol. 133, 2014
We develop 200 contrarian trading strategies based on significant market variations to test whether it is possible to benefit from the well-known psychological bias of overreaction that plagues investors. We conduct the most recent and appropriate statistical tests to ensure that none of these active strategies beats the buy-and-hold strategy due to pure luck only. Each of these strategies are tested on 13 different underlying assets, including exchange rates and stock indexes. When both transaction and borrowing costs are taken into account, our empirical results suggest that the use of significant market variations as daily reversal signals does not lead to any abnormal profit.
Testing The Profitability Of Contrarian Trading Strategies Based On The Overreaction Hypothesis – Introduction
Investors have always scouted throughout the globe to find the highest returns on their investment. Since the 1980’s, this long-lasting quest for the Holy Grail has nevertheless been taking place in a constantly moving environment, accompanied by the rise and fall of new trading venues, participants, instruments, and technologies. In such market conditions, market variations can be extreme and even caused by exceptional events, the Black Monday (on October 19, 1987) and the Flash Crash (on May 6, 2010) being two notable examples. Not surprisingly, extreme explanations have also been proposed, from purely `rational’ computer-based trading to purely `irrational’ human behavior. It is nevertheless undisputable that investor psychology bears on the determination of market prices. On December 5, 1996, even Alan Greenspan who was a fierce advocate of the free market used the term `irrational exuberance’ to characterize the behavior of investors. Interestingly enough, markets kept rising until the middle of the year 2000. A more recent example concerns the European debt crisis when Mario Draghi, President of the European Central Bank (ECB), asserted on July 26, 2012 that the ECB was ready to do `whatever it takes’ to preserve the Euro. Following the speech, the mood turned upbeat as European stock exchanges started to rise, with the French CAC and the Spanish IBEX indexes closing 4.07% and 6.06% higher respectively. These two short examples remind us of the role that psychology plays in trading and investment decisions.
Applied psychology has considerably affected the way finance research is nowadays conducted. In behavioral finance, a particular attention is given to the degree of investor irrationality through the study of the effects of social, cognitive and emotional factors on market prices, returns and asset allocation. Market inefficiencies and anomalies are viewed as evidence of under- or over-reactions to information, leading to extended market trends or abrupt market reversals. Such market imperfections are attributed to a combination of cognitive biases such as bounded rationality, overconfidence, overreaction, representative bias, mimicry (herding instinct), and other predictable human errors in reasoning and information processing. Its increasing importance has been acknowledged by the 2002 and 2013 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, that has been attributed respectively to Daniel Kahneman and Robert Shiller.
We contribute to the literature on behavioral finance by investigating whether the well-known psychological bias of overreaction that plagues investors is predictable enough to lead to abnormal profits, justifying the claim that investor’s behavior can be modeled appropriately. In particular, we test the predictive power of reversal signals based on significant market variations and control for transaction costs, borrowing costs, and randomness. The basic assumption is that investor overreaction drives excessive market movements that ultimately lead to price reversals. Under the null hypothesis (of no predictability), such reversal pattern cannot be anticipated and no abnormal profit can be realized. To the best of our knowledge, no previous work has studied overreaction-based strategies in the way we attempt to test them, using robust statistical procedures. To test the hypothesis of no predictability, we apply the Superior Predictive Ability (SPA) test and its stepwise version (SSPA), which corrects for data snooping. As in Duvinage et al. (2013), a double-or-out approach for trading strategies is used, which consists in following a benchmark by holding a long position modulated by one buy or sell transaction depending upon the previous day’s price movement. This approach is recommended for SSPA tests since it delivers the same number of observations for each simulation.
See full PDF below.