This paper documents that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. We find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented

/pdf/returns-to-buying-winners-and-selling-losers-implications-47r2et8t86.pdf

Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

Previous work shows that average returns on common stocks are related to firm characteristics like size, earnings/price, cash flow/price, book-to-market equity, past sales growth, long-term past return, and short-term past return. Because these patterns in average returns apparently are not explained by the CAPM, they are called anomalies. We find that, except for the continuation of short-term returns, the anomalies largely disappear in a three-factor model. Our results are consistent with rational ICAPM or APT asset pricing, but we also consider irrational pricing and data problems as possible explanations. RESEARCHERS HAVE IDENTIFIED MANY patterns in average stock returns. For example, DeBondt and Thaler (1985) find a reversal in long-term returns; stocks with low long-term past returns tend to have higher future returns. In contrast, Jegadeesh and Titman (1993) find that short-term returns tend to continue; stocks with higher returns in the previous twelve months tend to have higher future returns. Others show that a firm's average stock return is related to its size (ME, stock price times number of shares), book-to-marketequity (BE/ME, the ratio of the book value of common equity to its market value), earnings/price (E/P), cash flow/price (C/P), and past sales growth. (Banz (1981), Basu (1983), Rosenberg, Reid, and Lanstein (1985), and Lakonishok, Shleifer and Vishny (1994).) Because these patterns in average stock returns are not explained by the capital asset pricing model (CAPM) of Sharpe (1964) and Lintner (1965), they are typically called anomalies. This paper argues that many of the CAPM average-return anomalies are related, and they are captured by the three-factor model in Fama and French (FF 1993). The model says that the expected return on a portfolio in excess of the risk-free rate [E(Ri) - Rf] is explained by the sensitivity of its return to three factors: (i) the excess return on a broad market portfolio (RM - Rf); (ii) the difference between the return on a portfolio of small stocks and the return on a portfolio of large stocks (SMB, small minus big); and (iii) the difference between the return on a portfolio of high-book-to-market stocks and the return on a portfolio of low-book-to-market stocks (HML, high minus low). Specifically, the expected excess return on portfolio i is,

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1996.tb05202.x

Multifactor Explanations of Asset Pricing Anomalies

We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors’ confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations ~“momentum”!, short-run earnings “drift,” but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy. IN RECENT YEARS A BODY OF evidence on security returns has presented a sharp challenge to the traditional view that securities are rationally priced to ref lect all publicly available information. Some of the more pervasive anomalies can be classified as follows ~Appendix A cites the relevant literature!: 1. Event-based return predictability ~public-event-date average stock returns of the same sign as average subsequent long-run abnormal performance! 2. Short-term momentum ~positive short-term autocorrelation of stock returns, for individual stocks and the market as a whole!

/pdf/investor-psychology-and-security-market-under-and-108faczcym.pdf

Investor Psychology and Security Market Under- and Overreactions

For many years, scholars and investment professionals have argued that value strategies outperform the market. These value strategies call for buying stocks that have low prices relative to earnings, dividends, book assets, or other measures of fundamental value. While there is some agreement that value strategies produce higher returns, the interpretation of why they do so is more controversial. This article provides evidence that value strategies yield higher returns because these strategies exploit the suboptimal behavior of the typical investor and not because these strategies are fundamentally riskier. FOR MANY YEARS, SCHOLARS and investment professionals have argued that

/pdf/contrarian-investment-extrapolation-and-risk-53e80nd609.pdf

Contrarian Investment, Extrapolation, and Risk

https://scholar.harvard.edu/files/shleifer/files/model_invest_sent.pdf

A model of investor sentiment

Measuring abnormal performance: Do stocks overreact?

This paper finds that, for the 1935-1986 period, the market’s risk-return relation does not have a January seasonal. The findings differ from those of other studies due to the use of value-weighted, rather than equally weighted, portfolios. Inferences are sensitive to the weighting procedure because of the small-firm return patterns in January. In particular, even in those Januaries for which the market return is negative, small-firm returns are positive, and they are more positive the higher is beta. This is consistent with the portfolio rebalancing explanation of the turn-of-the-year effect. SINCE AT LEAST 1926, THE average return on small firms in January has been unusually high, a phenomenon known as the turn-of-the-year effect. In this paper, we present new evidence regarding the risk-return characteristics of small firms in January. We find that, for small firms, there is a positively sloped riskreturn relation in both up and down markets. This pattern is difficult to reconcile with the belief that the high return on small firms in January is equilibrium compensation for risk bearing. We show how these patterns in small firms’ returns have led others to conclude that the market’s risk-return relation has a January seasonal. In particular, Rozeff and Kinney [21] and Tinic and West [22,23], among others, using equally weighted portfolios formed on the basis of historical betas, have found a steeper slope for the CAPM’s risk-return relation in January than in other months. We demonstrate that this pattern exists only for small firms. Consequently, statistical inferences about the risk-return relation are heavily dependent upon whether equally weighted or value-weighted portfolio returns are used in the estimation. We show that, for 1935-86, when monthly cross-sectional regressions are estimated using value-weighted portfolio returns, there is no more of a statistically significant difference in the estimated return to risk bearing in January than in the other eleven months of the year. The notion that there is a January seasonal in the CAPM’s risk-return relation for the US. has rapidly become accepted knowledge. For example, Corhay, Hawawini, and Michel [6] accept this conclusion and investigate whether other

/pdf/portfolio-rebalancing-and-the-turn-of-the-year-effect-56yyf160n8.pdf

Portfolio Rebalancing and the Turn‐of‐the‐Year Effect

CHEN, KAN AND MILLER (1993, best pronounced CheK'M) provide a detailed critique of our earlier paper (Lee, Shleifer, and Tlialer (1991)). CKM accuse Lee et al. of trying to kill two birds-the closed-end fund puzzle and the small firm effect-with one stone and missing both. Their approach is to throw at Lee et al. every stone they can, presumably hoping that one will hit. This reply shows that none does. CKM focus on four empirical issues: 1) the role of utilities in the Lee et al. sample of small institutional ownership firms, 2) the robustness of the relationship between fund discounts and the returns on high versus low ownership firms (their Table I), 3) the appropriate way to specify the regression in Table IV of Lee et al. (their Table II), and 4) the regressions using one closed-end fund--TriContinental (their Table III). We address these criticisms in turn. CKM begin with the finding of Lee et al. that even during the second half of their sample, when the relationship between closed-end fund discounts and small firm returns weakens, there is a significant relationship between return on stocks with low institutional ownership and changes in discounts. CKM describe as a concession the finding of Lee et al. that most of these low ownership stocks are public utilities. However, this finding makes even more vivid the main claim of Lee et al. that stocks with similar ownership structures but very different fundamentals move together in the market. After all, what do public utilities have in common with closed-end funds other than a similar ownership structure? The one plausible common factor that might similarly affect closed-end fund discounts and prices of public utility stocks is small investor sentiment. CKM also say that they "tested a portfolio of NYSE public utility firms with more than 10 percent institutional ownership," and that their "results were essentially the same as for the sample with less than 10 percent ownership." They do not report these results, but have kindly provided us with their data. To investigate their claim, we divided all utilities into three groups of equal numbers of firms with high, low, and medium institutional ownership. We then ran regressions like those in Table VIII of Lee et al. for each group. The results in Table I of this paper show that low institutional

Navin Chopra

Papers

Measuring abnormal performance: Do stocks overreact?

Portfolio Rebalancing and the Turn‐of‐the‐Year Effect

Yes, Discounts on Closed‐End Funds Are a Sentiment Index

Performance measurement methodology and the question of whether stocks overreact / 1991:130

Yes, Discounts on Closed-End Funds Are a Sentiment Index: Summing Up