High-Volume Return Premiums and Their Link to Economic Activity
Summary
This research review studies whether stocks with unusually high trading volume earn a premium that reflects information about the real economy. It constructs high-volume premiums from stocks ranked by recent abnormal volume, then tests whether the premiums predict industrial production and other economic indicators. The reported U.S. evidence finds predictive power for industrial production growth over horizons extending to nine months, including after controls for established equity factors, liquidity measures, and business-cycle variables. The signal’s strength falls when systemic financial risk is included, but it retains information in the reported models.
The article also evaluates a risk-based explanation by testing whether common return factors and macroeconomic exposures explain the premium. High-volume stocks show greater exposure to some economic risks, yet the tested models explain only part of the premium. Behavioral mispricing models also fail to account for it fully, leaving its source unresolved. The study spans a long historical sample and includes in-sample and out-of-sample analysis, but its conclusions concern U.S. data and do not prove that the premium is tradable after costs or that its predictive relationship will persist.
Key ideas
- The high-volume premium is formed by comparing returns of stocks with unusually high and low recent trading volume.
- The study reports that the premium predicts industrial production growth for multiple months.
- Its predictive information partly overlaps with business-cycle and systemic-risk measures but is not fully explained by them.
- High-volume stocks have greater exposure to selected macroeconomic risks, though tested risk models explain only part of the premium.
- The premium’s source remains uncertain, and historical prediction does not establish profitable implementation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.