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

Traditionally and understandably, the microscope of market microstructure has focused on attributes of single assets. Little theoretical attention and virtually no empirical work has been devoted to common determinants of liquidity nor to their empirical manifestation, correlated movements in liquidity. But a wider-angle lens exposes an imposing image of commonality. Quoted spreads, quoted depth, and effective spreads co-move with market- and industry-wide liquidity. After controlling for wellknown individual liquidity determinants, such as volatility, volume, and price, common influences remain signi"cant and material. Recognizing the existence of commonality is a key to uncovering some suggestive evidence that inventory risks and asymmetric information both affect intertemporal changes in liquidity.

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Spreads, depths, and trading activity for U.S. equities are studied over an extended time sample. Daily changes in market averages of liquidity and trading activity are highly volatile, negatively serially dependent, and influenced by a variety of factors. Liquidity plummets significantly in down markets but increases weakly in up markets. Trading activity increases in either up or down markets. Recent market volatility induces less trading activity and reduces spreads. There are strong day-of-the-week effects; Fridays are relatively sluggish and illiquid while Tuesdays are the opposite. Long- and shortterm interest rates influence liquidity and trading activity. Depth and trading activity increase just prior to major macroeconomic announcements.

Financial Research at the Wharton School of the University of Pennsylvania is gratefully acknowledged. All We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26 % o f t h e d a y-to-day variation in yields on days without major macroeconomic announcemen ts. The effect of orderflow on yields is permanent and strongest when liquidity is low. All of the evidence points toward an important role of price discovery in understanding the behavior of the yield curve. The use of riskless interest rates permeates virtually every facet of economics and finance. It is therefore critical to understand the behavior of the term structure of riskless interest rates, or the yield curve, which gives the mapping between the maturity of a riskless loan and its rate. Much of the term structure literature focuses on factor models in which, at each date, the yields on all bonds with different maturities are determined by the realizations of a few common factors (e.g., Vasicek (1977); Cox, Ingersoll and Ross (1985)). The consensus is that more than one, but not

: This paper implements a model for the valuation of the default risk implicit in the prices of corporate bonds. The analytical approach considers the two essential ingredients in the valuation of corporate bonds: interest rate uncertainty and default risk. The former is modeled as a diffusion process. The latter is modeled as a spread following a diffusion process, with the magnitude of this spread impacting on the probability of a Poisson process governing the arrival of the default event. We apply two variants of this model to the valuation of fixed-for-floating swaps. In the first, the swap is default-free, and the spread represents the appropriate discounted expected value of the instantaneous TED spread; in the second, we allow the swap to incorporate default risk. We propose to test our models using the entire term structure of corporate bonds prices for different ratings and industry categories, as well as the term structure of fixed-for-floating swaps. The Marketing of Close...

This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), returns, volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and volatility prove to be significantly correlated, suggesting that common factors drive liquidity and volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between “macro ” liquidity, or money flows, and “micro ” or transactions liquidity.

This paper explores the competition between two trading venues, Electronic Communication Networks (ECNs) and Nasdaq market makers. ECNs o¡er the advantages of anonymity and speed of execution, which attract informed traders. Thus, trades are more likely to occur on ECNs when information asymmetry is greater and when trading volume and stock-return volatility are high. ECN trades have greater permanent price impacts and more private information is revealed through ECN trades than though market-maker trades. However, ECN trades have higher ex ante trading costs because market makers can preference or internalize the less informed trades and o¡er them better executions. TECHNOLOGICAL INNOVATIONS THAT ENABLE HIGH-SPEED, low-cost electronic trading systems are dramatically changing the structure of ¢nancial markets. Exchanges and markets around the world are merging or forming alliances to improve liquidity and reduce costs in the face of increased competition from each other and from these computerized trading systems.Trading volume on Electronic Communications Networks (ECNs) has grown rapidly over the past several years. ECNs are now involved in more than a third of Nasdaq trading volume and are attempting to increase their market share in NYSE-listed

Exchange for providing the requisite data, and Lawrence Glosten and an anonymous reviewer for their extensive and insightful comments and suggestions. This paper is dedicated to Teppo Martikainen who contributed substantially to earlier versions of this paper but did not live to witness its completion.

Traditionally, volume has provided the link between trading activity and returns. We focus on a hitherto unexplored but intuitive measure of trading activity: the aggregate daily order imbalance, buy orders less sell orders, on the New York Stock Exchange. Order imbalance increases following market declines and vice versa, which reveals that investors are contrarians on aggregate. Order imbalances in either direction, excess buy or sell orders, reduce liquidity. Marketwide returns are strongly affected by contemporaneous and lagged order imbalances. Market returns reverse themselves after high-negative-imbalance, large-negative-return days. Even after controlling for aggregate volume and liquidity, market returns are affected by order imbalance.