Individual investors who hold common stocks directly pay a tremendous performance penalty for active trading. Of 66,465 households with accounts at a large discount broker during 1991 to 1996, those that trade most earn an annual return of 11.4 percent, while the market returns 17.9 percent. The average household earns an annual return of 16.4 percent, tilts its common stock investment toward high-beta, small, value stocks, and turns over 75 percent of its portfolio annually. Overconfidence can explain high trading levels and the resulting poor performance of individual investors. Our central message is that trading is hazardous to your wealth. The investor’s chief problem—and even his worst enemy—is likely to be himself. Benjamin Graham In 1996, approximately 47 percent of equity investments in the United States were held directly by households, 23 percent by pension funds, and 14 percent by mutual funds ~Securities Industry Fact Book, 1997!. Financial economists have extensively analyzed the return performance of equities managed by mutual funds. There is also a fair amount of research on the performance of equities managed by pension funds. Unfortunately, there is little research on the return performance of equities held directly by households, despite their large ownership of equities.

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios. THE SINGLE FACTOR CAPITAL ASSET PRICING MODEL ~CAPM! of Sharpe ~1964! and Lintner ~1965! has come under recent scrutiny. Tests indicate that the crossasset variation in expected returns cannot be explained by the market beta alone. For example, a growing number of studies show that “fundamental” variables such as size, book-to-market value, and price to earnings ratios

This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work and on the trade-off between risk and return. Modern research seeks to understand the behavior of the stochastic discount factor ~SDF! that prices all assets in the economy. The behavior of the term structure of real interest rates restricts the conditional mean of the SDF, whereas patterns of risk premia restrict its conditional volatility and factor structure. Stylized facts about interest rates, aggregate stock prices, and cross-sectional patterns in stock returns have stimulated new research on optimal portfolio choice, intertemporal equilibrium models, and behavioral finance. This paper surveys the field of asset pricing. The emphasis is on the interplay between theory and empirical work. Theorists develop models with testable predictions; empirical researchers document “puzzles”—stylized facts that fail to fit established theories—and this stimulates the development of new theories. Such a process is part of the normal development of any science. Asset pricing, like the rest of economics, faces the special challenge that data are generated naturally rather than experimentally, and so researchers cannot control the quantity of data or the random shocks that affect the data. A particularly interesting characteristic of the asset pricing field is that these random shocks are also the subject matter of the theory. As Campbell, Lo, and MacKinlay ~1997, Chap. 1, p. 3! put it: What distinguishes financial economics is the central role that uncertainty plays in both financial theory and its empirical implementation. The starting point for every financial model is the uncertainty facing investors, and the substance of every financial model involves the impact of uncertainty on the behavior of investors and, ultimately, on mar-* Department of Economics, Harvard University, Cambridge, Massachusetts

Theoretical models predict that overcon�dent investors trade excessively. We test this prediction by partitioning investors on gender. Psychological research demonstrates that, in areas such as �nance, men are more overcon�dent than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997. We document that men trade 45 percent more than women. Trading reduces men’s net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women. It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so. Josh Billings, nineteenth century American humorist It is dif�cult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors. Rational investors make periodic contributions and withdrawals

We use a new database to perform a comprehensive analysis of the mutual fund industry. We find that funds hold stocks that outperform the market by 1.3 percent per year, but their net returns underperform by one percent. Of the 2.3 percent difference between these results, 0.7 percent is due to the underperformance of nonstock holdings, whereas 1.6 percent is due to expenses and transactions costs. Thus, funds pick stocks well enough to cover their costs. Also, high-turnover funds beat the Vanguard Index 500 fund on a net return basis. Our evidence supports the value of active mutual fund management. DO MUTUAL FUND MANAGERS WHO actively trade stocks add value? Academics have debated this issue since the seminal paper of Jensen ~1968!. Although some controversy still exists, the majority of studies now conclude that actively managed funds ~e.g., the Fidelity Magellan fund!, on average, underperform their passively managed counterparts ~e.g., the Vanguard Index 500 fund!. 1 For example, Gruber ~1996! finds that the average mutual fund underperforms

We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. Many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments with active managers do not outperform passive benchmarks because of the competitive market for capital provision, combined with decreasing returns to scale in active portfolio management. Consequently, past performance cannot be used to predict future returns, or to infer the average skill level of active managers. The lack of persistence in actively managed returns does not imply that differential ability across managers is nonexistent or unrewarded, that gathering information about performance is socially wasteful, or that chasing performance is pointless. A strong relationship between past performance and the flow of funds exists in our model: indeed, this is the market mechanism that ensures that no predictability in performance exists. Choosing parameters to match the flow-performance relationship and survivorship rates, we find these features of the data are consistent with the vast majority (80%) of active managers having at least

We analyze the trading activity of the mutual fund industry from 1975 through 1994 to determine whether funds “herd ” when they trade stocks and to investigate the impact of herding on stock prices. Although we find little herding by mutual funds in the average stock, we find much higher levels in trades of small stocks and in trading by growth-oriented funds. Stocks that herds buy outperform stocks that they sell by 4 percent during the following six months; this return difference is much more pronounced among small stocks. Our results are consistent with mutual fund herding speeding the price-adjustment process. DO INSTITUTIONAL INVESTORS “F LOCK TOGETHER ” ~or “herd, ” as it is often called! when they trade securities? Do some investors follow the lead of others when they trade? Such questions have interested researchers for some time, and are central to understanding the impact of institutional trading on securities markets and to understanding the way in which information becomes incorporated into market prices. 1

Shareholder rights vary across �rms. Using the incidence of 24 governance rules, we construct a “Governance Index ” to proxy for the level of shareholder rights at about 1500 large �rms during the 1990s. An investment strategy that bought �rms in the lowest decile of the index (strongest rights) and sold �rms in the highest decile of the index (weakest rights) would have earned abnormal returns of 8.5 percent per year during the sample period. We �nd that �rms with stronger shareholder rights had higher �rm value, higher pro�ts, higher sales growth, lower capital expenditures, and made fewer corporate acquisitions. I.

People are overconfident. Overconfidence affects financial markets. How depends on who in the market is overconfident and on how information is distributed. This paper examines markets in which price-taking traders, a strategic-trading insider, and risk-averse marketmakers are overconfident. Overconfidence increases expected trading volume, increases market depth, and decreases the expected utility of overconfident traders. Its effect on volatility and price quality depend on who is overconfident. Overconfident traders can cause markets to underreact to the information of rational traders. Markets also underreact to abstract, statistical, and highly relevant information, and they overreact to salient, anecdotal, and less relevant information. MODELS OF FINANCIAL MARKETS are often extended by incorporating the imperfections that we observe in real markets. For example, models may not consider transactions costs, an important feature of real markets; so Constantinides ~1979!, Leland ~1985!, and others incorporate transactions costs into their models. Just as the observed features of actual markets are incorporated into models, so too are the observed traits of economic agents. In 1738 Daniel Bernoulli noted that people behave as if they are risk averse. Prior to Bernoulli most scholars considered it normative behavior to value a gamble at its expected value. Today, economic models usually assume agents are risk averse, though, for tractability, they are also modeled as risk neutral. In reality, people are not always risk averse or even risk neutral; millions of people engage in regular risk-seeking activity, such as buying lottery tickets. Kahne-

We use Bayesian model averaging to analyze the sample evidence on return predictability in the presence of model uncertainty. The analysis reveals in-sample and out-of-sample predictability, and shows that the out-of-sample performance of the Bayesian approach is superior to that of model selection criteria. We find that term and market premia are robust predictors. Moreover, small-cap value stocks appear more predictable than large-cap growth stocks. We also investigate the implications of model uncertainty from investment management perspectives. We show that model uncertainty is more important than estimation risk, and investors who discard model uncertainty face large utility losses.