Liquidity biases in asset pricing tests.

By Elena Asparouhova, H. Bessembinder, Ivalina Kalcheva. 2010

217 citations recorded by the source corpus.

Subfields: Accounting, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/78c2136d52e7cc449366c09265f361ccc03f0800

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In Accounting

1. Nicholas Barberis
2. Turan G. Bali
3. Shane A. Corwin
4. Zhi Da
5. Yi Tang
6. Yannan Shen
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In Economics and Econometrics

1. Turan G. Bali
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3. Zhi Da
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6. Nicholas Barberis
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In Finance

1. Turan G. Bali
2. Yi Tang
3. Nicholas Barberis
4. Zhi Da
5. Baolian Wang
6. Yannan Shen
7. Lin Peng
8. Amit Goyal

Citations in the corpus

• Informed Traders and Limit Order Markets (2008, methodology): “For this updating, we use equation ( 10 ), with t0 = t and vt0 = vt.”
• The Illiquidity Premium: International Evidence (2015, unknown): “393) and Asparouhova et al. (2013). The drawback of this procedure”
• Liquidity Shocks and Stock Market Reactions (2014, methodology): “We further account for the possibility that market microstructure effects can bias the Fama-MacBeth regression coefficients using the technique of Asparouhova, Bessembinder, and Kalcheva (2010).”
• Do Portfolio Distortions Reflect Superior Information or Psychological Biases? (2013, unknown): “Since microstructure effects are not limited to stocks priced below $5, for greater accuracy, we directly account for liquidity biases using the Asparouhova, Bessembinder, and Kalcheva (2010) method.”
• Speculative Retail Trading and Asset Prices (2013, unknown): “All these results remain about the same when we follow the Asparouhova et al. (2010) method to account for potential microstructure noise by using gross returns from the previous period to weight the observations.”
• Prospect Theory and Stock Returns: An Empirical Test (2016, methodology): “First, we compute “returnweighted” portfolio returns in which the return of an individual stock is weighted by one plus its lagged monthly return; Asparouhova, Bessembinder, and Kalcheva (2010, 2013) note that this approach helps to correct for biases in equal-weighted returns.”
• The Cross-Section of Expected Stock Returns: What Have We Learnt from the Past Twenty-Five Years of Research? (2010, unknown): “Brennan and Wang (2009) and Asparouhova et al. (2009) show that this bias can create issues in interpreting the results of predictive return regressions when the explanatory variables are correlated with noise.”
• Liquidity Biases and the Pricing of Cross-Sectional Idiosyncratic Volatility around the World (2015, unknown): “Our analysis is related to, but quite distinct from, that of Asparouhova, Bessembinder, and Kalcheva (2010), (2013).”
• Clientele Change, Liquidity Shock, and the Return on Financially Distressed Stocks (2010, background): “The bid-ask bounce could cause a sizable upward bias in average return computation, as noted by Blume and Stambaugh (1983) and recently by Bessembinder and Kalcheva (2007). Computation of the bid-ask bounce bias and recalculation of the monthly stock returns excluding the …rst trading day after port”