Using a comprehensive database of headlines about individual companies, I examine monthly returns following public news. I compare them to stocks with similar returns, but no identifiable public news. There is a di#erence between the two sets. I find strong drift after bad news. Investors seem to react slowly to this information. I also find reversal after extreme price movements unaccompanied by public news. The separate patterns appear even after adjustments for risk exposure and other e#ects. They are, however, mainly seen in smaller, more illiquid stocks. These findings support some integrated theories of investor over- and underreaction.

We build a model that helps explain why increases in liquidity⎯such as lower bid-ask spreads, a lower price impact of trade, or higher turnover⎯predict lower subsequent returns in both firm-level and aggregate data. The model features a class of irrational investors, who underreact to the information contained in order flow, thereby boosting liquidity. In the presence of short-sales constraints, high liquidity is a symptom of the fact that the market is dominated by these irrational investors, and hence is overvalued. This theory can also explain how managers might successfully time the market for seasoned equity offerings, by simply following a rule of thumb that involves issuing when the SEO market is particularly liquid. Empirically, we find that: i) aggregate measures of equity issuance and share turnover are highly correlated; yet ii) in a multiple regression, both have incremental predictive power for future equal-weighted market returns.

A growing number of researchers argue that time-series patterns in returns are due to investor irrationality and thus can be translated into abnormal profits. Continuation of short-term returns or momentum is one such pattern that has defied any rational explanation and is at odds with market efficiency. This paper shows that profits to momentum strategies can be explained by a set of lagged macroeconomic variables and payoffs to momentum strategies disappear once stock returns are adjusted for their predictability based on these macroeconomic variables. Our results provide a possible role for time-varying expected returns as an explanation for momentum payoffs. THIS PAPER EXAMINES THE RELATIVE importance of common factors and firmspecific information in explaining the profitability of momentum-based trading strategies, first documented by Jegadeesh and Titman ~1993!. The profitability of momentum strategies has been particularly intriguing, as it remains the only CAPM-related anomaly unexplained by the Fama–French

We document the equity trading practices of approximately 1,200 institutions from the third quarter of 1987 through the third quarter of 1995. We decompose trading by institutions into the initiation of new positions (entry), the termination of previous positions (exit), and adjustments to ongoing holdings. Institutions act as momentum traders when they enter stocks but as contrarian traders when they exit or make adjustments to ongoing holdings. We find significant differences in trading practices among different types of institutions. In a celebrated article published almost a half century ago, Friedman (1953) argues that rational speculation must stabilize asset prices. More recently, DeLong, Shleifer, Summers and Waldmann (DSSW, 1990) show that momentum traders (also referred to as trend chasers or positive feedback traders) can in fact destabilize stock prices and thereby threaten the efficiency of financial markets. DSSW’s proof has inspired numerous empirical investigations that focus almost exclusively on the behavior of institutional investors. There are at least two reasons for this focus. First, a large fraction of corporate equity is held by institutional investors; institutional ownership of shares in U.S. firms increased from approximately seven percent in

Abstract: We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded in favor of an alternative—yet equally simple—model that would have done better over the same period. This theory makes several distinctive predictions, which, for concreteness, we develop in a stock-market setting. For example, starting with symmetric and homoskedastic fundamentals, the theory yields forecastable variation in the size of the value/glamour differential, in volatility, and in the skewness of returns. Some of these features mirror familiar accounts of stock-price bubbles.

We test overreaction theories of short-run momentum and long-run reversal in the cross section of stock returns. Momentum profits depend on the state of the market, as predicted. From 1929 to 1995, the mean monthly momentum profit following positive market returns is 0.93%, whereas the mean profit following negative market returns is −0.37%. The up-market momentum reverses in the long-run. Our results are robust to the conditioning information in macroeconomic factors. Moreover, we find that macroeconomic factors are unable to explain momentum profits after simple methodological adjustments to take account of microstructure concerns.

A large body of evidence has emerged in recent studies confirming that macroeconomic factors play an important role in determining investor risk premia and the ultimate path of equity returns. This paper illustrates how widely tested financial and economic variables from these studies can be employed in a time varying dynamic sector allocation model for U.S. equities. The model developed here is evaluated using Bayesian parameter estimation and model selection criteria. We find that using the Kalman filter to estimate time varying sensitivities to predetermined risk factors results in significantly improved sector return predictability over static or rolling parameter specifications. A simple trading strategy developed here using Kalman filter predicted returns as input provides for potentially robust long run profit opportunities. JEL classification: G12; C11

This paper analyzes the relation between momentum strategies (strategies that buy stocks with high returns over the previous three to 12 months and sell stocks with low returns over the same period) and turnover (number of shares traded divided by the number of shares outstanding) for the German stock market. Our main finding is that momentum strategies are more profitable among highturnover stocks. We present various robustness checks, long-horizon results, evidence on seasonality, and control for size-, book-to-market-, and industry-effects. We argue that our results are useful to empirically evaluate competing explanations for the momentum effect.

This paper applies the newly developed methodology of Bai and Ng (2002, 2003), providing the decomposition of large panel data into systematic and idiosyncratic components, to both returns and turnover. When combined with a GLS-based principal components approach, we demonstrate their procedure works well for both returns and turnover, despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. Using this method, we provide a new test of Lo and Wang’s (2000) theoretical model’s restriction that returns and turnover should have the same number of systematic factors. We find this restriction is strongly rejected by the data, suggesting that stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We further demonstrate that several commonly used turnover measures may understate the price impact of stock trading. 1

We are grateful to Jeff Fisher and NCREIF for providing turnover and constant liquidity price data, and This paper evaluates competing explanations for time variation in private real estate market liquidity documented in Fisher et al. (2003). We present a search-based model of incomeproperty transactions and pricing, based on Fisher et al. (2003) and Goetzmann and Peng (2004) and use it to test three classes of models that provide alternative explanations for time variation in liquidity. In the first, seller estimates of property value lag market conditions because of an asymmetric information problem. Sellers in part base their estimates of value on observations of signals from the market, but the presence of noise means a change in signal is not fully reflected in sellers ’ updated value estimates. The second class of models incorporates the option value of waiting, or opportunity cost of not transacting, recently introduced by Krainer (2001) and Nov-Marx (2004), into seller’s optimal valuation strategy. In the third, we allow for the possibility of noise traders, or investors who are not fully rational in the sense that they trade on market sentiment. We follow Baker and Stein (2003) and consider a model that links stock market-wide liquidity to investor sentiment with higher liquidity being due to the