This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual-stock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34-year period, the average return on stocks with high sensitivities to liquidity exceeds that for stocks with low sensitivities by 7.5 % annually, adjusted for exposures to the market return as well as size, value, and momentum factors.

Abstract not found

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We model a dynamic limit order market as a stochastic sequential game. Since the model is analytically intractable, we provide an algorithm based on Pakes and McGuire (2001) to find a stationary Markov-perfect equilibrium. Given the stationary equilibrium, we generate artificial time series and perform comparative dynamics. As we know the data generating process, we can compare transaction prices to the true value of the asset, as well as explicitly determine the welfare gains accruing to investors.

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This paper investigates the components of liquidity risk that are important for asset-pricing anomalies. Firm-level liquidity is decomposed into variable and fixed price effects and estimated using intraday data for the period 1983-2001. Unexpected systematic (market-wide) variations of the variable component rather than the fixed component of liquidity are shown to be priced within the context of momentum and post-earnings-announcement drift (PEAD) portfolio re-turns. As the variable component is typically associated with private information (e.g., Kyle (1985)), the results suggest that a substantial part of momentum and PEAD returns can be viewed as compensation for the unexpected variations in the aggregate ratio of informed traders to noise traders. JEL classification: G12; G14

in \Accounting and Finance in Tel-Aviv " for their comments and suggestions. All remaining errors are the authors ’ responsibility. The idea that extreme trading activity (as measured by trading volume) contains information about the future evolution of stock prices is investigated. We nd that stocks experiencing unusually high (low) trading volume over a period of one day to a week tend to appreciate (depreciate) over the course of the following month. This eect is consistent across rm sizes, portfolio formation strategies, and volume measures. Surprisingly, the eect is even stronger when the unusually high or low trading activity is not accompanied by extreme returns, and appears to be permanent. The signicantly positive returns of our volume-based strategies are not due to compensation for excessive risk taking, nor are they due to rm announcement eects. Previous studies have documented the positive contemporaneous correlation between a stock’s trading volume and its return, and the autocorrelation in returns. The high volume return premium that we document in this paper is not an artifact of these results. Finally, we also show that protable trading strategies can be implemented to take advantage of the information contained in trading volume. 1

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 studies the impact of trading on government bond prices surrounding the release of macroeconomic news. The results show a significant increase in the informational role of trading following economic announcements, which suggests the release of public information increases the level of information asymmetry in the government bond market. The informational role of trading is greater after announcements with a larger initial price impact, and the relation is associated with the surprise component of the announcement and the precision of the public information. The results provide evidence that government bond order flow reveals fundamental information about riskless rates.

High-frequency financial data are not only discretely sampled in time but the time separating successive observations is often random. We analyze the consequences of this dual feature of the data when estimating a continuous-time model. In particular, we measure the additional effects of the randomness of the sampling intervals over and beyond those due to the discreteness of the data. We also examine the effect of simply ignoring the sampling randomness. We find that in many situations the randomness of the sampling has a larger impact than the discreteness of the data.