We investigate the role of information-based trading in affecting asset returns. We show in a rational expectation example how private information affects equilibrium asset returns. Using a market microstructure model, we derive a measure of the probability of information-based trading, and we estimate this measure using data for individual NYSE-listed stocks for 1983 to 1998. We then incorporate our estimates into a Fama and French ~1992! asset-pricing framework. Our main result is that information does affect asset prices. A difference of 10 percentage points in the probability of information-based trading between two stocks leads to a difference in their expected returns of 2.5 percent per year. ASSET PRICING IS FUNDAMENTAL to our understanding of the wealth dynamics of an economy. This central importance has resulted in an extensive literature on asset pricing, much of it focusing on the economic factors that influence asset prices. Despite the fact that virtually all assets trade in markets, one set of factors not typically considered in asset-pricing models are the features

Given the cross-sectional and temporal variation in their liquidity, emerging equity markets provide an ideal setting to examine the impact of liquidity on expected returns. Our main liquidity measure is a transformation of the proportion of zero daily firm returns, averaged over the month. We find that it significantly predicts future returns, whereas alternative measures such as turnover do not. Consistent with liquidity being a priced factor, unexpected liquidity shocks are positively correlated with contemporaneous return shocks and negatively correlated with shocks to the dividend yield. We consider a simple asset pricing model with liquidity and the market portfolio as risk factors and transaction costs that are proportional to liquidity. The model differentiates between integrated and segmented countries and time periods. Our results suggest that local market liquidity is an important driver of expected returns in emerging markets, and that the liberalization process has not fully eliminated its impact.

An analysis of predictions of six interest rates over 3-months-ahead and 6-months-ahead horizons, surveyed regularly over eight years, casts doubt on the hypothesis that market participants ' expectations are 'rational' in Muth's sense. Tests show that the survey respondents did not make unbiased predictions, that (especially for the 6-months-ahead predictions) they did not efficiently exploit the information contained in past interest rate movements, that their respective 3-months-ahead and 6-months-ahead predictions failed to be consistent in the sense required for 'rationality', and that (for long-term but not short-term interest rates) their predictions failed to exploit efficiently the information contained in common macroeconomic and macro-policy variables other than the money stock.

ABSTRACT. No-arbitrage is the fundamental principle of economic rationality which unifies normative decision theory, game theory, and market theory. In economic environments where money is available as a medium of measurement and exchange, no-arbitrage is synonymous with subjective expected utility maximization in personal decisions, competitive equilibria in capital markets and exchange economies, and correlated equilibria in noncooperative games. The arbitrage principle directly characterizes rationality at the market level; the appearance of deliberate optimization by individual agents is a consequence of their adaptation to the market. Concepts of equilibrium behavior in games and markets can thus be reconciled with the ideas that individual rationality is bounded, that agents use evolutionarily-shaped decision rules rather than numerical optimization algorithms, and that personal probabilities and utilities are inseparable and to some extent indeterminate. Risk-neutral probability distributions, interpretable as products of probabilities and marginal utilities, play a central role as observable quantities in economic systems.

Information. We are grateful for insightful comments to the Editor, David Levine, and several referees and thank participants at seminars at American University, Carnegie Mellon University,

It has been well-recognized that markets can aggregate less-than-perfect information across market participants. With two differently designed securities, this work examines the impact of security design on the information aggregation ability of markets in laboratory experiments. Results show that markets with one security aggregate information significantly better than markets with the other security, implying that information aggregation ability of markets is affected by the security design. Behavior of individual participants is then investigated to understand the observed market behavior. JEL Classification: C92; C91; D80

We analyze theoretically and empirically the implications of heterogeneous information for equilibrium asset pricing and portfolio choice. Our theoretical framework, directly inspired by Admati (1985), implies that with partial information aggregation, portfolio separation fails, buy-and-hold strategies are not optimal, and investors should structure their portfolios using the information contained in prices in order to cope with winner's curse problems. We implement empirically such a price-contingent portfolio allocation strategy and show that it outperforms economically and statistically the passive/indexing buy-and-hold strategy. We thus demonstrate that prices reveal information, in contrast with the homogeneous information CAPM, but only partially, consistent with a Noisy Rational Expectations Equilibrium. The success of our pricecontingent strategy does not proxy for the success of trading strategies based purely on historical performance, such as momentum investment.

Sigatures are on file in the Graduate School. iii In almost all walks of life, predicting uncertain future events plays an essential role in decision-making processes. However, information related to future events frequently exists only as dispersed opinions, insights, and intuitions of individuals. Each individual only knows a little, but aggregating the dispersed information together may make considerable contribution to decision making. This is typical in many domains including business, politics, and entertainment. Therefore, how to aggregate such dispersed information for useful decision support is a crucial task. Markets have shown great potential as one of the most effective mechanisms for gathering distributed information and generating accurate forecasts, often surpassing many existing methods in practice. This research studies information markets, markets that are specially designed for information aggregation and forecasting, from four different perspectives: theoretical examination, experimental evaluation, empirical analysis, and design.

This paper presents a dynamic model to demonstrate that, when di¤erences-of-opinion over individual securities have a common component, the valuation of the aggregate market can be higher than its fundamental even if all investors agree on the market fundamental, and the common disagreement drives discount rate news. Using analyst forecast dispersion to measure disagreement, I …nd empirical evidence that individual stock disagreements co-move and the common component mean-reverts, the common disagreement has substantial explanatory power for the time-series variation of equity premium, and the common disagreement correlates with discount-rate news rather than cash-‡ow news and has explanatory power for the time-series variation of value premium.

Standard asset pricing models assume (i) that there is complete agreement among investors about probability distributions of future payoffs on assets, and (ii) investors choose asset holdings based solely on anticipated payoffs; that is, investment assets are not also consumption goods. Both assumptions are unrealistic. We provide a simple framework for studying how disagreement and tastes for assets as consumption goods can affect asset prices.