[We investigate the implications of ambiguity aversion for performance and regulation of markets. In our model, agents' decision making may incorporate both risk and ambiguity, and we demonstrate that nonparticipation arises from the rational decision by some traders to avoid ambiguity. In equilibrium, these participation decisions affect the equilibrium risk premium, and distort market performance when viewed from the perspective of traditional asset pricing models. We demonstrate how regulation, particularly regulation of unlikely events, can moderate the effects of ambiguity, thereby increasing participation and generating welfare gains. Our analysis demonstrates how legal systems affect participation in financial markets through their influence on ambiguity.]

This paper delineates the link between the existence of information, the timing of trades, and the stochastic process of prices. The authors show that time affects prices, with the time between trades affecting spreads. Because the absence of trades is correlated with volume, the authors' model predicts a testable relation between spreads and normal and unexpected volume, and demonstrates how volume affects the speed of price adjustment. Their model also demonstrates how the transaction price series will be a biased representation of the true price process, with the variance being both overstated and heteroskedastic.

This paper examines the effects of price-contingent orders on security prices. The authors show that a market maker who knows the type and composition of trades will set larger spreads and adjust prices faster than if price-contingent orders were not allowed. Because traders have rational expectations over the book, the authors demonstrate that uncertainty over order type reduces the variance of prices but with a corresponding loss in price informativeness. They also show that the sequence property of price-contingent orders increases the probability of large price movements. This distinction between variance and episodic price volatility has important policy implications.

We investigate the effect of ambiguity about hedge fund investment strategies on asset prices and aggregate welfare. We model some traders (mutual funds) as facing ambiguity about the equilibrium trading strategies of other traders (hedge funds). This ambiguity limits the ability of mutual funds to infer information from prices and has negative effects on market outcomes. We use this analysis to investigate the implications of regulations that affect disclosure requirements of hedge funds or the cost of operating a hedge fund. Our analysis demonstrates how regulations affect asset prices and welfare through their influence on opaque trading.

Using the model structure of Easley and O'Hara (Journal of Finance, 47, 577–604), we demonstrate how the parameters of the market-maker's beliefs can be estimated from trade data. We show how to extract information from both trade and no-trade intervals, and how intraday and interday data provide information. We derive and evaluate tests of model specification and estimate the information content of differential trade sizes. Our work provides a framework for testing extant microstructure models, shows how to extract the information contained in the trading process, and demonstrates the empirical importance of asymmetric information models for asset prices.

David Easley, Maureen O'Hara, Adverse Selection and Large Trade Volume: The Implications for Market Efficiency, The Journal of Financial and Quantitative Analysis, Vol. 27, No. 2 (Jun., 1992), pp. 185-208

In this research, we investigate the informational role of financial analysts. Using a trade-based empirical technique, we estimate the probability of information-based trading for a sample of NYSE stocks that differ in analyst coverage. We determine how this probability differs across stocks followed by many analysts, and we investigate whether analysts increase or create the flow of information. We also determine the `normal' level of noise trading in each sample stock, thereby giving us the ability to assess the depth of the market for stocks with differing analysts followings. Our most important empirical result is that the number of financial analysts is not a good proxy for information-based trading.

We propose that the arrival of new information is a source of systematic risk for the holder of a financial security. Using several measures of information flows, we demonstrate that a stock’s sensitivity to market-wide information flow is associated with a robust cross-sectional return premium that is distinct from other return premia. We find that the amount of information impounded in prices through trading has increased in recent years consistent with declining trading costs and the rise of algorithmic trading. We show that the information flows risk premium is increasing through time.

This paper investigates the effect of trade size on security prices. We show that trade size introduces an adverse selection problem into security trading because, given that they wish to trade, informed traders perfer to trade larger amounts at any given price. As a result, market makers' pricing strategies must also depend on trade size, with large trades being made at less favorable prices. Our model provides one explanation for the price effect of block trades and demonstrates that both the size and the sequence of trades matter in determining the price-trade size relationship.

The authors investigate the informational role of volume and its applicability for technical analysis. They develop a new equilibrium model in which aggregate supply is fixed and traders receive signals with differing quality. The authors show that volume provides information on information quality that cannot be deduced from the price statistic. They show how volume, information precision, and price movements relate, and demonstrate how sequences of volume and prices can be informative. The authors also show that traders who use information contained in market statistics do better than traders who do not. Technical analysis, thus, arises as a natural component of the agents' learning process.