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Liquidity Risk and Expected Stock Returns
, 2002
"... This study investigates whether marketwide liquidity is a state variable important for asset pricing. We find that expected stock returns are related crosssectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individualsto ..."
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Cited by 590 (4 self)
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This study investigates whether marketwide liquidity is a state variable important for asset pricing. We find that expected stock returns are related crosssectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individualstock measures estimated with daily data, relies on the principle that order flow induces greater return reversals when liquidity is lower. Over a 34year 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.
Is information risk a determinant of asset returns
 Journal of Finance
, 2002
"... We investigate the role of informationbased 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 informationbased trading, and we estim ..."
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Cited by 294 (12 self)
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We investigate the role of informationbased 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 informationbased trading, and we estimate this measure using data for individual NYSElisted stocks for 1983 to 1998. We then incorporate our estimates into a Fama and French ~1992! assetpricing framework. Our main result is that information does affect asset prices. A difference of 10 percentage points in the probability of informationbased 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 assetpricing models are the features
Flight to Quality, Flight to Liquidity, and the Pricing of Risk”, MIT Sloan School of Management Working paper
"... We propose a dynamic equilibrium model of a multiasset market with stochastic volatility and transaction costs. Our key assumption is that investors might be forced to liquidate their portfolios when their performance falls below a threshold. This generates a preference for liquidity which is time ..."
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Cited by 126 (12 self)
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We propose a dynamic equilibrium model of a multiasset market with stochastic volatility and transaction costs. Our key assumption is that investors might be forced to liquidate their portfolios when their performance falls below a threshold. This generates a preference for liquidity which is timevarying and increasing with volatility. We show that during volatile times, assets ’ liquidity premia increase, investors become more risk averse, the correlation between the market and the volatility becomes more negative, assets ’ pairwise correlations increase, and the market betas of illiquid assets increase. Moreover, an unconditional CAPM understates the risk of illiquid assets because these assets become riskier when investors are the most risk averse.
Market liquidity as a sentiment indicator
, 2002
"... We build a model that helps explain why increases in liquidity⎯such as lower bidask spreads, a lower price impact of trade, or higher turnover⎯predict lower subsequent returns in both firmlevel and aggregate data. The model features a class of irrational investors, who underreact to the informatio ..."
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Cited by 122 (17 self)
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We build a model that helps explain why increases in liquidity⎯such as lower bidask spreads, a lower price impact of trade, or higher turnover⎯predict lower subsequent returns in both firmlevel and aggregate data. The model features a class of irrational investors, who underreact to the information contained in order flow, thereby boosting liquidity. In the presence of shortsales constraints, high liquidity is a symptom of the fact that the market is dominated by these irrational investors, and hence is overvalued. This theory can also explain how managers might successfully time the market for seasoned equity offerings, by simply following a rule of thumb that involves issuing when the SEO market is particularly liquid. Empirically, we find that: i) aggregate measures of equity issuance and share turnover are highly correlated; yet ii) in a multiple regression, both have incremental predictive power for future equalweighted market returns.
Trading costs and returns for US equities: estimating effective costs from daily data
 Journal of Finance
, 2009
"... The effective cost of trading is usually estimated from transactionlevel data. This study proposes a Gibbs estimate that is based on daily closing prices. In a validation sample, the daily Gibbs estimate achieves a correlation of 0.965 with the transactionlevel estimate. When the Gibbs estimates ar ..."
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Cited by 114 (1 self)
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The effective cost of trading is usually estimated from transactionlevel data. This study proposes a Gibbs estimate that is based on daily closing prices. In a validation sample, the daily Gibbs estimate achieves a correlation of 0.965 with the transactionlevel estimate. When the Gibbs estimates are incorporated into asset pricing specifications over a long historical sample (1926 to 2006), the results suggest that effective cost (as a characteristic) is positively related to stock returns. The relation is strongest in January, but it appears to be distinct from size effects. INVESTIGATIONS INTO THE ROLE of liquidity and transaction costs in asset pricing must generally confront the fact that while many asset pricing tests make use of U.S. equity returns from 1926 onward, the highfrequency data used to estimate trading costs are usually not available prior to 1983. Accordingly, most studies either limit the sample to the post1983 period of common coverage or use the longer historical sample with liquidity proxies estimated from daily data. This paper falls into the latter group. Specifically, I propose a new approach to estimating the effective cost of trading and the common variation in this cost. These estimates are then used in conventional asset pricing specifications with a view to ascertaining the role of trading costs as a characteristic in explaining expected returns. 1
Share restrictions and asset pricing: Evidence from the hedge fund industry
 Journal of Financial Economics
, 2007
"... This paper finds a positive, concave relation between the returns and share restrictions of private investment funds, and shows that previously documented positive alphas can be interpreted as compensation for holding illiquid fund shares. The annual returns on funds with lockup provisions are app ..."
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Cited by 101 (1 self)
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This paper finds a positive, concave relation between the returns and share restrictions of private investment funds, and shows that previously documented positive alphas can be interpreted as compensation for holding illiquid fund shares. The annual returns on funds with lockup provisions are approximately 4 % higher than those for nonlockup funds, and the alphas of funds with the most liquid shares are either negative or insignificant. This paper also finds a positive association between share restrictions and illiquidity in fund assets, suggesting that funds facing high redemption costs use restrictions to screen for investors with lowliquidity needs. The results are consistent with previous theories which posit that liquidity is priced, and that less liquid assets are held by investors with longer investment horizons. JEL classification: G11; G12
An Empirical Analysis of Stock and Bond Market Liquidity
, 2003
"... This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Crossmarket dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bidask spreads and depth), returns, volatility, and order fl ..."
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Cited by 86 (6 self)
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This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Crossmarket dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bidask 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.
Momentum and postearningsannouncement drift anomalies: The role of liquidity risk
 Journal of Financial Economics
, 2006
"... This paper investigates the components of liquidity risk that are important for assetpricing anomalies. Firmlevel liquidity is decomposed into variable and fixed price effects and estimated using intraday data for the period 19832001. Unexpected systematic (marketwide) variations of the variable ..."
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Cited by 84 (3 self)
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This paper investigates the components of liquidity risk that are important for assetpricing anomalies. Firmlevel liquidity is decomposed into variable and fixed price effects and estimated using intraday data for the period 19832001. Unexpected systematic (marketwide) variations of the variable component rather than the fixed component of liquidity are shown to be priced within the context of momentum and postearningsannouncement drift (PEAD) portfolio returns. 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
Predictive Regressions: A ReducedBias Estimation Method
, 2003
"... We propose a direct and convenient reducedbias estimator of predictive regression coefficients, assuming that the regressors are Gaussian firstorder autoregressive with errors that are correlated with the error series of the dependent variable. For the singleregressor model, Stambaugh (1999) show ..."
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Cited by 84 (2 self)
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We propose a direct and convenient reducedbias estimator of predictive regression coefficients, assuming that the regressors are Gaussian firstorder autoregressive with errors that are correlated with the error series of the dependent variable. For the singleregressor model, Stambaugh (1999) shows that the ordinary least squares estimator of the predictive regression coefficient is biased in small samples. Our estimation method employs an augmented regression which uses a proxy for the errors in the autoregressive model. We also develop a heuristic estimator of the standard error of the estimated predictive coefficient which performs well in simulations. We analyze the case of multiple predictors that are firstorder autoregressive and derive bias expressions for both the ordinary least squares and our reducedbias estimated coefficients. The effectiveness of our estimation method is demonstrated by simulations.