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This study examines whether the Hong Kong stock market overreacts. By using monthly return data of all the common stocks listed on the Hong Kong Stock Exchange from January 1980 to December 1995, it examines the profitability of a contrarian strategy of buying prior losers and selling prior winners. The evidence shows that prior losers outperform prior winners by up to 68.59% in the subsequent five‐year test period. This finding can be interpreted as investors' tendency to react over‐optimistically to positive information and over‐pessimistically to negative information, thus causing stock prices to take temporary swing away from their intrinsic values and then reverse back subsequently. Our result is consistent with that documented by Debondt and Thaler (1985) for the U.S. market. This study also investigates whether seasonality accounts for the abnormal return but finds that the overreaction effect is not caused by the well‐known January effect. Further tests are conducted to investigate whether changes in betas of the winners and losers account for the abnormal return. The evidence shows that such changes are also minor, which cannot explain the price reversal phenomenon.

Behavioral researchers argue that although individuals often rely on heuristics or rules of thumb that reduce the complexity involved in predicting values, such heuristics can lead to severe and systematic errors. I test this argument in an investment context by focusing on a simple heuristic whereby momentum traders are attracted to buying stocks that have recently doubled in price in anticipation of further gains. I show that such a strategy can lead to predictable disappointment for these investors and severe underperformance relative to the market (‐28% over a 4‐year period), whereas investors who avoid relying on this simple heuristic are likely to perform as expected, on average similar to the overall market. I also find that underperformance is more severe for stocks that have doubled faster. The “doubling” variable is a significant predictor of future price reversals in addition to past performance per se, as uncovered by the previous researchers.

The investments industry is made up of two major groups of security analysts: fundamentalists and technicians. Fundamentalists make investment decisions by analysing a company's “fundamentals,” which are risk and performance factors specific to that firm. Technicians, on the other hand, believe that patterns in historical price and volume data for a stock can be used to make profitable trading decisions. In keeping with the latter approach, DeBondt and Thaler (1985, 1987) find evidence of price reversals in three‐year stock returns. Specifically, they determine that stock prices overreact to information, suggesting that a contrarian strategy of buying stocks that performed poorly in the past (i.e. losers) and selling stocks that performed well in the past (i.e. winners), produces significant abnormal returns. Additional support to this “overreaction phenomenon” is documented by Chan (1988), Lo and MacKinlay (1990), and Zarowin (1990).

Evidence of mean reversion in U.S. stock prices during the post‐World War II era is mixed. I find that using the standard portfolio formation method to construct size‐sorted portfolios is inadequate for detecting mean reversion. Using alternative portfolio formation methods and additional cross‐sectional power gained from size‐sorted portfolios during the period 1963 to 1998, I find strong evidence of mean reversion in portfolio prices. My findings imply a significantly positive speed of reversion with a half‐life of approximately three and a half years. Parametric contrarian investment strategies that exploit mean reversion outperform buy‐and‐hold and standard contrarian strategies.

The paper draws on the key themes raised at a Round Table discussion on behavioural finance attended by academics and practitioners. The paper provides a background to the key aims of behavioural finance research and the development of the discipline over time. The purpose of this paper is to indicate some future research issues on behavioural finance that emanate from the financial crisis and highlight areas of mutual benefit to both behavioural finance academics and the finance industry so as to encourage a creative cross‐fertilisation.

The paper draws on a Round Table discussion on behavioural finance that was organized by the Behavioural Finance Working Group, the Centre for the Study of Financial Innovation and Financial Services Knowledge Transfer Network.

The paper highlights numerous benefits that behavioural finance research can contribute to the financial industry, but at the same time there is an evident discrepancy between the academic and the professional world when it comes to utilising behavioural finance research.

The paper highlights several areas where behavioural finance can contribute significant benefits to a wide array of aspects of the finance industry.

The paper seeks to inform behavioural finance issues so as to encourage collaboration between the academic world and finance practitioners. In so doing, the paper aims to encourage a greater awareness of individual decision‐making frames and heuristics and how industry can apply these concepts to improve the allocation of finance products to society.

The paper brings together a wide array of finance professionals and academics to encourage greater collaboration and mutual respect of each others interest in and uses for behavioural finance.

The paper aims to provide the individual routes of the authors into behavioural finance in order to introduce the special issue.

The paper provides the background to the authors' personal route into behavioural finance.

The paper highlights general themes of development and influence of behavioural finance and relationships with practice and other areas of academic finance.

The paper offers the perspectives of the authors on how they feel the research area of behavioural finance will develop in the future.

In a recent article Aharony and Swary considered the synchronous nature of earnings and dividends announcements in examination of the information content of dividend hypothesis. They concluded that their results support the information content of dividends hypothesis—that announcements of changes in dividends provide information beyond that contained in quarterly earnings announcements. A shortcoming of the Aharony and Swary study is that it considers only dividend and earnings announcements that occur at least 11 trading days from each other. In fact, as their Table I indicates the majority of such pairs of announcements occur within ten days of each other. Further, approximately one‐half of their observations of earnings and dividends announcements are separated by at least 21 trading days (close to one calendar month). These considerations lead one to wonder how synchronous their announcements are and whether their results can be generalized to earnings and dividend announcements that actually occur in close proximity to each other.

This paper investigates whether stock‐price indexes of emerging markets can be characterized as random walk (unit root) or mean reversion processes. We implement a panelbased test that exploits cross‐sectional information from seventeen emerging equity markets during the period January 1985 to April 2002. The gain in power allows us to reject the null hypothesis of random walk in favor of mean reversion at the 5 percent significance level. We find a positive speed of reversion with a half‐life of about 30 months. These results are similar to those documented for developed markets. Our findings provide an interesting comparison to existing studies on more matured markets and reduce the likelihood of earlier mean reversion findings as attributable to data mining.