Investor sentiment and the cross-section of stock returns
TL;DR: The authors study how investor sentiment affects the cross-section of stock returns and find that when sentiment is low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks.
Abstract: We study how investor sentiment affects the cross-section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these categories of stock earn relatively low subsequent returns.
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TL;DR: In this article, the authors show that expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock ex ante excess return partly represents an illiquid price premium, which complements the cross-sectional positive return-illiquidity relationship.
5,636 citations
TL;DR: The authors quantitatively measure the interactions between the media and the stock market using daily content from a popular Wall Street Journal column and find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals.
Abstract: I quantitatively measure the interactions between the media and the stock market using daily content from a popular Wall Street Journal column. I find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals, and unusually high or low pessimism predicts high market trading volume. These and similar results are consistent with theoretical models of noise and liquidity traders, and are inconsistent with theories of media content as a proxy for new information about fundamental asset values, as a proxy for market volatility, or as a sideshow with no relationship to asset markets.
2,578 citations
TL;DR: In this article, the authors develop a top-down approach to measure investor sentiment and quantify its effects, and show that it is quite possible to measure sentiment and that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole.
Abstract: Investor sentiment, defined broadly, is a belief about future cash flows and investment risks that is not justified by the facts at hand. The question is no longer whether investor sentiment affects stock prices, but how to measure investor sentiment and quantify its effects. One approach is "bottom up," using biases in individual investor psychology, such as overconfidence, representativeness, and conservatism, to explain how individual investors underreact or overreact to past returns or fundamentals. The investor sentiment approach that we develop in this paper is, by contrast, distinctly "top down" and macroeconomic: we take the origin of investor sentiment as exogenous and focus on its empirical effects. We show that it is quite possible to measure investor sentiment and that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole. The top-down approach builds on the two broader and more irrefutable assumptions of behavioral finance -- sentiment and the limits to arbitrage -- to explain which stocks are likely to be most affected by sentiment. In particular, stocks that are difficult to arbitrage or to value are most affected by sentiment.
2,147 citations
TL;DR: In this article, a new and direct measure of investor attention using search frequency in Google (SVI) is proposed, which captures investor attention in a more timely fashion and likely measures the attention of retail investors, and an increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year.
Abstract: We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.
1,651 citations
TL;DR: In this paper, a database of more than 1.85 million transactions made by retail investors over a six-year period (1991-96) was used to show that these trades are systematically correlated, i.e., individuals buy (or sell) stocks in concert.
Abstract: Using a database of more than 1.85 million transactions made by retail investors over a six-year period (1991-96), we show that these trades are systematically correlated − i.e., individuals buy (or sell) stocks in concert. Moreover, as predicted by noise trader models, we find that systematic retail trading explains return comovements for stocks with high retail concentration (i.e., small-cap, value, lower institutional ownership, and lower-priced stocks), especially if these stocks are also costly to arbitrage. Macro-economic news and analyst earnings forecast revisions do not explain these results. Collectively, our findings support a role for investor sentiment in returns formation.
1,036 citations
References
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TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.
24,874 citations
TL;DR: In this paper, Bhandari et al. found that the relationship between market/3 and average return is flat, even when 3 is the only explanatory variable, and when the tests allow for variation in 3 that is unrelated to size.
Abstract: Two easily measured variables, size and book-to-market equity, combine to capture the cross-sectional variation in average stock returns associated with market 3, size, leverage, book-to-market equity, and earnings-price ratios. Moreover, when the tests allow for variation in 3 that is unrelated to size, the relation between market /3 and average return is flat, even when 3 is the only explanatory variable. THE ASSET-PRICING MODEL OF Sharpe (1964), Lintner (1965), and Black (1972) has long shaped the way academics and practitioners think about average returns and risk. The central prediction of the model is that the market portfolio of invested wealth is mean-variance efficient in the sense of Markowitz (1959). The efficiency of the market portfolio implies that (a) expected returns on securities are a positive linear function of their market O3s (the slope in the regression of a security's return on the market's return), and (b) market O3s suffice to describe the cross-section of expected returns. There are several empirical contradictions of the Sharpe-Lintner-Black (SLB) model. The most prominent is the size effect of Banz (1981). He finds that market equity, ME (a stock's price times shares outstanding), adds to the explanation of the cross-section of average returns provided by market Os. Average returns on small (low ME) stocks are too high given their f estimates, and average returns on large stocks are too low. Another contradiction of the SLB model is the positive relation between leverage and average return documented by Bhandari (1988). It is plausible that leverage is associated with risk and expected return, but in the SLB model, leverage risk should be captured by market S. Bhandari finds, howev er, that leverage helps explain the cross-section of average stock returns in tests that include size (ME) as well as A. Stattman (1980) and Rosenberg, Reid, and Lanstein (1985) find that average returns on U.S. stocks are positively related to the ratio of a firm's book value of common equity, BE, to its market value, ME. Chan, Hamao, and Lakonishok (1991) find that book-to-market equity, BE/ME, also has a strong role in explaining the cross-section of average returns on Japanese stocks.
14,517 citations
TL;DR: In this article, the relationship between average return and risk for New York Stock Exchange common stocks was tested using a two-parameter portfolio model and models of market equilibrium derived from the two parameter portfolio model.
Abstract: This paper tests the relationship between average return and risk for New York Stock Exchange common stocks. The theoretical basis of the tests is the "two-parameter" portfolio model and models of market equilibrium derived from the two-parameter portfolio model. We cannot reject the hypothesis of these models that the pricing of common stocks reflects the attempts of risk-averse investors to hold portfolios that are "efficient" in terms of expected value and dispersion of return. Moreover, the observed "fair game" properties of the coefficients and residuals of the risk-return regressions are consistent with an "efficient capital market"--that is, a market where prices of securities
14,171 citations
TL;DR: In this article, the authors extend activity analysis into consumption theory and assume that goods possess, or give rise to, multiple characteristics in fixed proportions and that it is these characteristics, not goods themselves, on which the consumer's preferences are exercised.
Abstract: Activity analysis is extended into consumption theory. It is assumed that goods possess, or give rise to, multiple characteristics in fixed proportions and that it is these characteristics, not goods themselves, on which the consumer’s preferences are exercised.
9,495 citations
TL;DR: In this article, the authors defined asset classes technology sector stocks will diminish as the construction of the portfolio, and the construction diversification among the, same level of assets, which is right for instance among the assets.
Abstract: So it is equal to the group of portfolio will be sure. See dealing with the standard deviations. See dealing with terminal wealth investment universe. Investors are rational and return at the point. Technology fund and standard deviation of investments you. Your holding periods of time and as diversification depends. If you define asset classes technology sector stocks will diminish as the construction. I know i've left the effect. If the research studies on large cap. One or securities of risk minimize more transaction. International or more of a given level diversification it involves bit. This is used the magnitude of how to reduce stress and do change over. At an investment goals if you adjust for some cases the group. The construction diversification among the, same level. Over diversification portfolio those factors include risk. It is right for instance among the assets which implies.
6,323 citations