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Asymmetric information and the distribution of trading volume

Journal of Corporate Finance 2023 82, 102464 open access

We propose the Volume Coefficient of Variation (VCV), the ratio of the standard deviation to the mean of trading volume, as a new and simple measure of information asymmetry in security markets. We use a microstructure model to demonstrate that VCV is strictly increasing in the proportion of informed trade. Empirically, we obtain VCV from daily observations of trading volume and provide extensive evidence supporting the hypothesis that VCV indicates information asymmetry, by studying return reversals, institutional ownership, and extant firm-level measures of asymmetric information in the cross-section of US stocks. Moreover, VCV increases following exogenous reductions in analyst coverage induced by brokerage closures, and steeply decreases around earnings announcements and other information disclosures.

Discount rates and cash flows: A local projection approach

Matthijs Lof; Henri Nyberg

Journal of Banking & Finance 2024 162, 107127 open access

We develop flexible local projections to quantify the relative contributions of expected discount rates and cash flows to the variation of dividend yields. Local projections enable the incorporation of large information sets, the use of monthly data along with annual data, and the consideration of time variation in the dividend yield decomposition. By expanding the set of state variables and allowing for time-varying parameters, our results show that the variation of expected discount rates remains the primary contributor to market volatility, whereas the contribution of expected cash flows is considerably smaller.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

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Asymmetric information and the distribution of trading volume

Discount rates and cash flows: A local projection approach

Nonstandard Errors