We model a market populated by two groups of boundedly rational agents: “newswatchers” and “momentum traders.” Each newswatcher observes some private information, but fails to extract other newswatchers’ information from prices. If information diffuses gradually across the population, prices underreact in the short run. The underreaction means that the momentum traders can profit by trendchasing. However, if they can only implement simple (i.e., univariate) strategies, their attempts at arbitrage must inevitably lead to overreaction at long horizons. In addition to providing a unified account of under- and overreactions, the model generates several other distinctive implications.

Various theories have been proposed to explain momentum in stock returns. We test the gradual-information-diffusion model of Hong and Stein (1999) and establish three key results. First, once one moves past the very smallest stocks, the profitability of momentum strategies declines sharply with firm size. Second, holding size fixed, momentum strategies work better among stocks with low analyst coverage. Finally, the effect of analyst coverage is greater for stocks that are past losers than for past winners. These findings are consistent with the hypothesis that firm-specific information, especially negative information, diffuses only gradually across the investing public. SEVERAL RECENT PAPERS HAVE DOCUMENTED that, at medium-term horizons ranging from three to 12 months, stock returns exhibit momentum-that is, past winners continue to perform well, and past losers continue to perform poorly. For example, Jegadeesh and Titman (1993), using a U.S. sample of NYSE/ AMEX stocks over the period from 1965 to 1989, find that a strategy that buys past six-month winners (stocks in the top performance decile) and shorts past six-month losers (stocks in the bottom performance decile) earns approximately one percent per month over the subsequent six months. Not only is this an economically interesting magnitude, but the result also appears to be robust: Rouwenhorst (1998) obtains very similar numbers in a

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 review extensive evidence about how psychological biases affect investor behavior and prices. Systematic mispricing probably causes substantial resource misallocation. We argue that limited attention and overconfidence cause investor credulity about the strategic incentives of informed market participants. However, individuals as political participants remain subject to the biases and self-interest they exhibit in private settings. Indeed, correcting contemporaneous market pricing errors is probably not government’s relative advantage. Government and private planners should establish rules ex ante to improve choices and efficiency, including disclosure, reporting, advertising, and default-option-setting regulations. Especially

Technical analysis, also known as “charting, ” has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis—the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution—conditioned on specific technical indicators such as head-and-shoulders or double-bottoms—we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. ONE OF THE GREATEST GULFS between academic finance and industry practice

For U.S. stock prices, evidence of mean reversion over long horizons is mixed, possibly due to lack of a reliable long time series. Using additional cross-sectional power gained from national stock index data of 18 countries during the period 1969 to 1996, we find strong evidence of mean reversion in relative stock index prices. Our findings imply a significantly positive speed of reversion with a halflife of three to three and one-half years. This result is robust to alternative specifications and data. Parametric contrarian investment strategies that fully exploit mean reversion across national indexes outperform buy-and-hold and standard contrarian strategies. Mean reversion refers to a tendency of asset prices to return to a trend path. The existence of mean reversion in stock prices is subject to much controversy. Fama and French ~1988a! and Poterba and Summers ~1988! are the first to provide direct empirical evidence that mean reversion occurs in U.S. stock prices over long horizons. 1 Others are critical of these results: Lo and MacKinlay ~1988! find evidence against mean reversion in U.S. stock prices using weekly data; Kim, Nelson, and Startz ~1991! argue that the mean reversion results are only detectable in prewar data; and Richardson and Stock ~1989! and Richardson ~1993! report that correcting for small-sample bias problems may reverse the Fama and French ~1988a! and Poterba and

go to Girts Graudins and Eric Nierenberg for their outstanding contributions to this project. Harvard Business School’s Division of Research provided financial support. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.

This paper studies the relation between fund performance and fund attributes in the Swedish market. Performance is measured as the alpha in a linear regression of fund returns on several benchmark assets, allowing for time-varying betas. The estimated performance is then used in a cross-sectional analysis of the relation between performance and fund attributes such as past performance, flows, size, turnover, and proxies for expenses and trading activity. The results show, among other things, that good performance is to be found among small equity funds, low-fee funds, funds whose trading activity is high, and in some cases, funds with good past performance.

and the participants of the Taipei conference on “Analysis of High-Frequency Financial Data and Market Microstructure ” for their valuable comments and suggestions. We also thank P. R. Hansen for sharing his

Using New York Stock Exchange order data, we examine the determinants of order choices. We consider order type (market and limit), order side, automatic execution vs. the auction process, order pricing aggressiveness, order cancellation, and the passage of time without order activity. Our multinomial logit specification and new statistical test allow us to comprehensively test order-choice theory. We find that: 1.) both order activity and inactivity are clustered; 2.) wider (narrower) spreads increase the probability of limit (marketable) orders; 3.) larger quoted depth elicits competition to supply liquidity; 4.) positive (negative) market returns produce more buy (sell) orders; 5.) favorable (unfavorable) private information increases the likelihood of buy (sell) orders; and, 6.) limit orders are more likely late in the trading day; 7.) positive first-order autocorrelation exists in order type; 8.) negative autocorrelation exists in the order flow process over longer horizons. Our results become richer when we consider orders’ pricing aggressiveness.