Risk‐Adjusted Long‐Term Contrarian Profits: Evidence from Non‐S&P 500 High‐Volume Stocks

Can trading volume help unravel the long‐term overreaction puzzle? With portfolios of non‐S&P 500 NYSE stocks, we show that (1) both the high‐ and low‐volume (abnormal volume) contrarian portfolios earn a much higher market‐adjusted excess return than the normal‐volume contrarian portfolio, (2) however, when leverage‐induced risk is factored in, excess returns from contrarian portfolios with normal‐ and low‐volume stocks are insignificant, (3) only excess returns from high‐volume contrarian stocks are significant and cannot be explained by the time‐varying risk and return framework, and (4) such high‐volume, risk‐adjusted excess returns arise mainly from winner (glamour) stocks.     

Are investors consistent in their trading strategies? An examination of individual investor-level data

While it has been demonstrated that momentum or contrarian trading strategies can be profitable in a range of institutional settings, less evidence is available concerning the actual trading strategies investors adopt. Standard definitions of momentum or contrarian trading strategies imply that a given investor applies the same strategy to both their buy and sell trades, which need not be the case. Using investor-level, transaction-based data from China, where tax effects are neutral, we examine investors' buy-sell decisions separately to investigate how past returns impact differentially on the trading strategies investors adopt when buying and selling stock. After controlling for a wide range of stock characteristics, extreme price changes and portfolio value, a clear asymmetry in trading is observed; with investors displaying momentum behavior when buying stocks, but contrarian behavior when selling stocks. This asymmetry in behavior is not driven purely by reactions to stock characteristics or extreme stocks. We discuss behavioral and cultural explanations for our findings.     

Is the Mean Return of Hotel Real Estate Stocks Apt to Overreact to Past Performance?

This study examines the return patterns of hotel real estate stocks in the U.S. during the period from 1990 to 2007.We find that the magnitude and persistence of future mean returns of hotel real estate stocks can be predicted based on past returns, past earnings surprise, trading volume, firm size, and holding period. The empirical evidence found from this paper confirms that short-horizon contrarian profits can be partially explained by the lead-lag effects, while in the intermediate-term price momentum profits and long-term contrarian profits can be partially attributed to the firms’ overreaction to past price changes. Our results support the contrarian/overreaction hypothesis, and they are inconsistent with the Fama-French risk-based hypothesis or the underreaction hypothesis. The study also confirms the earning underreaction hypothesis and finds the high volume stocks tend to earn high momentum profits in the intermediate-term. The study finds that the earning momentum effect for hotel stocks is more short-lived and smaller in magnitude than the market average. Price momentum portfolios (or contrarian portfolios) of big hotel firms underperform small hotel firms and the hotel price momentum portfolio (or contrarian portfolios) significantly underperform the overall market over the intermediate-term (or the long-term). These findings imply that the U.S. hotel industry, particularly the big hotel firms, have experienced relatively conservative growth in the sample period. It suggests that a conservative hotel growth strategy accompanied by an internal-oriented financing policy is proper in a period of prosperity.     

Do Low‐Priced Stocks Drive Long‐Term Contrarian Performance on the London Stock Exchange?

We investigate whether low‐priced stocks drive long‐term contrarian performance on the U.K. market. We find that contrarian performance at low, middle, and high price levels is positive. On the Fama‐French risk adjusted basis, we find both low‐priced and middle‐priced losers have significantly positive returns. When we adjust returns by market and liquidity risk, only middle‐priced losers maintain their positive returns. Our results reveal that low‐priced stocks are not fully responsible for contrarian performance. Our empirical evidence is generally consistent with the overreaction hypothesis and behavioral models of value investing.     

Delegated trading and the speed of adjustment in security prices

Institutional trading arrangements often involve the portfolio manager delegating the task of trade execution to a separate division within the firm. We model the agency conflict that arises in this setting and show that optimal performance benchmarks often create an incentive to execute orders contrary to concurrent information flow. We hypothesize that aggregate contrarian trading resulting from widespread application of such benchmarks leads to delays in the assimilation of information in security prices. Using institutional trading data, we document the hypothesized contrarian trading pattern and relate the pattern to price-adjustment delays in the response of individual stocks to index futures returns. The evidence supports the assertion that delegated institutional trading contributes to these delays.     

Investor preferences and portfolio selection: is diversification an appropriate strategy?

This paper analyzes the relationship between diversification and several distributional characteristics that have risk implications for stock returns. We develop a flexible three-parameter distribution to model the stock returns. Using data on the current 30 DJIA stocks, we show that an investor's strategy on diversification depends on the measures of risk for particular concerns. For example, investors who desire to increase positive skewness would hold a less diversified portfolio, while those who care more about extreme losses would hold a more diversified portfolio. Experimenting with a more general pool of stocks yields the same conclusions.     

Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz

Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the Extreme Risk Index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study seems to be the first application of extreme value techniques in portfolio management on a large scale. The primary aim of our investigation is the potential of ERI in practice. The performance of this strategy is benchmarked against the minimum variance portfolio and the equally weighted portfolio. These fundamental strategies are important benchmarks for large-scale applications. Our comparison includes annualized portfolio returns, maximal drawdowns, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator. Our results show that the ERI strategy significantly outperforms both the minimum-variance portfolio and the equally weighted portfolio on assets with heavy tails.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

Contrarian Profits and the Overreaction Hypothesis: the Case of the Athens Stock Exchange

This paper investigates the existence of contrarian profits and their sources for the Athens Stock Exchange (ASE). The empirical analysis decomposes contrarian profits to sources due to common factor reactions, overreaction to firm‐specific information, and profits not related to the previous two terms, as suggested by Jegadeesh and Titman (1995). Furthermore, in view of recent evidence that common stock returns are related to firm characteristics such as size and book‐to‐market equity, the paper decomposes contrarian profits to sources due to factors derived from the Fama and French (1993, 1996) three‐factor model. For the empirical testing, size‐sorted sub‐samples that are rebalanced annually are employed, and in addition, adjustments for thin and infrequent trading are made to the data. The results indicate that serial correlation is present in equity returns and that it leads to significant short‐run contrarian profits that persist even after we adjust for market frictions. Consistent with findings for the US market, contrarian profits decline as one moves from small stocks to large stocks, but only when market frictions are considered. Furthermore, the contribution to contrarian profits due to the overreaction to the firm‐specific component appears larger than the underreaction to the common factors.     

Betting against beta with intraday and overnight signals

The abnormal returns of the Betting Against Beta (BAB) strategy have attracted much interest among researchers and practitioners. Based on a market anomaly related to the Capital Asset Pricing Model, this strategy uses daily beta as a signal for portfolio construction. However, recent literature shows how some financial quantities, including beta, change between trading and non-trading periods. For this reason, we decided to compare the performance of the original BAB strategy with two BAB variants, where the signal for portfolio construction is given by intraday and overnight beta, respectively. Despite all strategies exhibiting positive cumulative returns, using the intraday beta signal leads to significantly higher performances. Further analyses show that the abnormal intraday BAB returns are mainly due to nano and micro-cap stocks which tend to outperform large-cap stocks, as well known from the literature.