This article tests whether stock market investors appropriately distinguish between new and old information about firms. I define the staleness of a news story as its textual similarity to the previous ten stories about the same firm. I find that firms' stock returns respond less to stale news. Even so, a firm's return on the day of stale news negatively predicts its return in the following week. Individual investors trade more aggressively on news when news is stale. The subsequent return reversal is significantly larger in stocks with above-average individual investor trading activity. These results are consistent with the idea that individual investors overreact to stale information, leading to temporary movements in firms' stock prices. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

[This article tests whether stock market investors appropriately distinguish between new and old information about firms. I define the staleness of a news story as its textual similarity to the previous ten stories about the same firm. I find that firms' stock returns respond less to stale news. Even so, a firm's return on the day of stale news negatively predicts its return in the following week. Individual investors trade more aggressively on news when news is stale. The subsequent return reversal is significantly larger in stocks with above-average individual investor trading activity. These results are consistent with the idea that individual investors overreact to stale information, leading to temporary movements in firms' stock prices.]

I use uniquely comprehensive data on financial news events to test four predictions from an asymmetric information model of a firm's stock price. Certain investors trade on information before it becomes public; then, public news levels the playing field for other investors, increasing their willingness to accommodate a persistent liquidity shock. Empirically, I measure public information using firms' stock returns on news days in the Dow Jones archive. I find four patterns in postnews returns and trading volume that are consistent with the asymmetric information model's predictions. Some evidence is, moreover, inconsistent with alternative theories in which traders interpret news differently for rational or behavioral reasons.

[I use uniquely comprehensive data on financial news events to test four predictions from an asymmetric information model of a firm's stock price. Certain investors trade on information before it becomes public; then, public news levels the playing field for other investors, increasing their willingness to accommodate a persistent liquidity shock. Empirically, I measure public information using firms' stock returns on news days in the Dow Jones archive. I find four patterns in postnews returns and trading volume that are consistent with the asymmetric information model's predictions. Some evidence is, moreover, inconsistent with alternative theories in which traders interpret news differently for rational or behavioral reasons.]

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.

We structurally estimate a model in which agents’ information processing biases can cause predictability in firms’ asset returns and investment inefficiencies. We generalize the neoclassical investment model by allowing for two biases—overconfidence and overextrapolation of trends—that distort agents’ expectations of firm productivity. Our model's predictions closely match empirical data on asset pricing and firm behavior. The estimated bias parameters are well identified and exhibit plausible magnitudes. Alternative models without either bias or with efficient investment fail to match observed return predictability and firm behavior. These results suggest that biases affect firm behavior, which in turn affects return anomalies.

We decompose the returns of five well‐known anomalies into cash flow and discount rate news. Common patterns emerge across the five factor portfolios and their mean‐variance efficient (MVE) combination. Whereas discount rate news predominates in market returns, systematic cash flow news drives the returns of anomaly portfolios and their MVE combination with the market portfolio. Anomaly cash flow and discount rate shocks are largely uncorrelated with market cash flow and discount rate shocks and with business cycle fluctuations. These rich empirical patterns restrict the joint dynamics of firm cash flows and the pricing kernel, thereby informing models of stocks' expected returns.

[Over-the-counter (OTC) stocks are far less liquid, disclose less information, and exhibit lower institutional holdings than do listed stocks. We exploit these different market conditions to test theories of cross-sectional return premiums. Compared with premiums in listed markets, the OTC illiquidity premium is several times higher, the size, value, and volatility premiums are similar, and the momentum premium is three times lower. The OTC illiquidity, size, value, and volatility premiums are largest among stocks held predominantly by retail investors and those not disclosing financial information. Theories of differences in investors' opinions and limits on short sales help explain these return premiums.]

Using proprietary data on millions of trades by retail investors, we provide the first large-scale evidence that retail short selling predicts negative stock returns. A portfolio that mimics weekly retail shorting earns an annualized risk-adjusted return of 9%. The predictive ability of retail short selling lasts for one year and is not subsumed by institutional short selling. In contrast to institutional shorting, retail shorting best predicts returns in small stocks and those that are heavily bought by other retail investors. Our findings are consistent with retail short sellers having unique insights into the retail investor community and small firms’ fundamentals. Received May 5, 2016; editorial decision September 6, 2016 by Editor Andrew Karolyi.

We analyze the role of retail investors in stock pricing using a database uniquely suited for this purpose. The data allow us to address selection bias concerns and to separately examine aggressive (market) and passive (limit) orders. Both aggressive and passive net buying positively predict firms’ monthly stock returns with no evidence of return reversal. Only aggressive orders correctly predict firm news, including earnings surprises, suggesting they convey novel cash flow information. Only passive net buying follows negative returns, consistent with traders providing liquidity and benefiting from the reversal of transitory price movements. These actions contribute to market efficiency.