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We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected. The direction of HFTs' trading predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.

We analyze the welfare consequences of a monopolist having additional information about consumers' tastes, beyond the prior distribution; the additional information can be used to charge different prices to different segments of the market, i.e., carry out \"third degree price discrimination.\" We show that the segmentation and pricing induced by the additional information can achieve every combination of consumer and producer surplus such that: (i) consumer surplus is nonnegative, (ii) producer surplus is at least as high as profits under the uniform monopoly price, and (iii) total surplus does not exceed the surplus generated by efficient trade.

We show that stock prices are more accurate when short sellers are more active. First, in a large panel of NYSE-listed stocks, intraday informational efficiency of prices improves with greater shorting flow. Second, at monthly and annual horizons, more shorting flow accelerates the incorporation of public information into prices. Third, greater shorting flow reduces post-earnings-announcement drift for negative earnings surprises. Fourth, short sellers change their trading around extreme return events in a way that aids price discovery and reduces divergence from fundamental values. These results are robust to various econometric specifications, and their magnitude is economically meaningful.

This article presents a study of how stock price efficiency and return distributions are affected by short-sale constraints. The study is based on a global dataset, from 2005 to 2008, that includes more than 12,600 stocks from 26 countries. We present two main findings. First, lending supply has a significant impact on efficiency. Stocks with higher short-sale constraints, measured as low lending supply, have lower price efficiency. Second, relaxing short-sales constraints is not associated with an increase in either price instability or the occurrence of extreme negative returns.

This paper deals with a basic issue: How does one approach the problem of designing the \"right\" objective for a given resource allocation problem? The notion of what is right can be fairly nebulous; we consider two issues that we see as key: efficiency and fairness. We approach the problem of designing objectives that account for the natural tension between efficiency and fairness in the context of a framework that captures a number of resource allocation problems of interest to managers. More precisely, we consider a rich family of objectives that have been well studied in the literature for their fairness properties. We deal with the problem of selecting the appropriate objective from this family. We characterize the trade-off achieved between efficiency and fairness as one selects different objectives and develop several concrete managerial prescriptions for the selection problem based on this trade-off. Finally, we demonstrate the value of our framework in a case study that considers air traffic management. This paper was accepted by Yossi Aviv, operations management.

Using a broad panel of NYSE-listed stocks between 1983 and 2004, we study the relation between institutional shareholdings and the relative informational efficiency of prices, measured as deviations from a random walk. Stocks with greater institutional ownership are priced more efficiently, and we show that variation in liquidity does not drive this result. One mechanism through which prices become more efficient is institutional trading activity, even when institutions trade passively. But efficiency is also directly related to institutional holdings, even after controlling for institutional trading, analyst coverage, short selling, variation in liquidity, and firm characteristics.

Using data from 56 markets, we find that short-term reversal, post-earnings drift, and momentum strategies earn similar returns in emerging and developed markets. Variance ratios and market delay measures often show greater deviations from random walk pricing in developed markets. Conceptually, we show that commonly used efficiency tests can yield misleading inferences because they do not control for the information environment. Our evidence corrects misperceptions that emerging markets feature larger trading profits and higher return autocorrelation, highlights crucial limitations of weak and semi-strong form efficiency measures, and points to the importance of measuring informational aspects of efficiency.

We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high-frequency data that identify computer-generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of highfrequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.

Traditional theories argue that governance is strongest under a single large blockholder, as she has high incentives to undertake value-enhancing interventions. However, most firms are held by multiple small blockholders. This article shows that, while such a structure generates free-rider problems that hinder intervention, the same coordination difficulties strengthen a second governance mechanism: disciplining the manager through trading. Since multiple blockholders cannot coordinate to limit their orders and maximize combined trading profits, they trade competitively, impounding more information into prices. This strengthens the threat of disciplinary trading, inducing higher managerial effort. The optimal blockholder structure depends on the relative effectiveness of manager and blockholder effort, the complementarities in their outputs, information asymmetry, liquidity, monitoring costs, and the manager's contract.

Accounting measurement and disclosure rules have a significant impact on the real decisions that firms make. In this essay, we provide an analytical framework to illustrate how such real effects arise. Using this framework, we examine three specific measurement issues that remain controversial: (1) How does the measurement of investments affect a firm's investment efficiency? (2) How does the measurement and disclosure of a firm's derivative transactions affect a firm's choice of intrinsic risk exposures, risk management strategy, and the incentive to speculate? (3) How could marking-to-market the asset portfolios of financial institutions generate procyclical real effects? We draw upon these real effects studies to generate sharper and novel insights that we believe are useful not only for the development of accounting standards, but also for guiding future empirical research.