Dynamic Volume-Return Relation of Individual Stocks

By G. Llorente, Roni Michaely, Gideon Saar, Jiang Wang. 2000

798 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ab0d1dc0b0bd262cfa0644f9864bfe3284e9b3c6

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Citations in the corpus

Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires (2010, neutral): “In fact, a low R2 is common when using such disaggregated data; prominent examples include labor market outcomes (e.g., Angrist and Krueger 1994, Malamud and Pop-Eleches 2010) and short-term stock returns (e.g., Llorente et al. 2002).”
Trading Volume, Information Asymmetry, and Timing Information (2005, methodology): “For example, if a utility company acquires a high risk bio-tech company, this increased risk can induce demand from risk seekers and supply (15)For example, Llorente et al. (2002), Hong, et.”
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression (2016, background): “[15] focus on the relationship between volume and return of individual stocks.”
Insider Trading Laws and Stock Price Informativeness (2009, methodology): “where θi,t is the information trading measure of Llorente et al. (2002) for country i (median θ across firms) in year t .”
Why Do Public Firms Issue Private and Public Securities? (2012, neutral): “The results on market choice reported in column 1 of Table 5 show that firms with a high degree of asymmetric information and high cash flow volatility are more likely to sell securities in the private market.”
Investor Overconfidence and Trading Volume (2006, background): “Lee and Swaminathan (2000), Llorente, Michaely, Saar, and Wang (2002) and Gervais, Kaniel”
The Impact of a Strong Bank-Firm Relationship on the Borrowing Firm (2011, methodology): “The measure of Information Asymmetry developed by Llorente et al. (2002) relies on the positive (negative) autocorrelation in returns when the level of speculative trading is proportionately high (low).”
Theory and Applications of TAR Model with Two Threshold Variables (2012, background): “( 2002 ) point out that intensive trading volume can help to identify the periods in which shocks occur.”
Trading imbalances, predictable reversals, and cross-stock price pressure (2008, background): “…that study the dynamic interaction between liquidity providers and liquidity demanders include DeLong, Shleifer, Summers, and Waldman (1990a, b), Campbell, Grossman, and Wang (1993), Madhavan and Smidt (1993), Spiegel and Subrahmanyam (1995), Barberis and Shleifer (2003), and Greenwood (2007).”