Psychological Aspects of Stock Returns Accompanied by High Trading Volumes

Andrey Kudryavtsev

Journal of Risk & Control, 2019, vol. 6, issue 1, 1-17

Abstract: Present study explores the effect of the availability heuristic (representing people's tendency to determine the likelihood of an event according to the easiness of recalling similar instances, and, thus, to overweight current information, as opposed to processing all relevant information) on stock price dynamics following days of extremely high trading volumes. I hypothesize that if the sign of a stock's return on the day when it registers an extremely high trading volume corresponds to the sign of the same day's stock market index return, then because of the effect of the availability heuristic, investors may consider the underlying important news to have a greater subjective probability of leading to stock returns of the respective sign, amplifying the latter and creating overreaction, which results in subsequent price reversal. Defining high-volume days according to a number of alternative proxies, I document that, in line with my hypothesis, both positive and negative high-volume day stock returns accompanied by the same-sign contemporaneous daily market returns are followed by significant reversals on the next trading day and over five- and twenty-day intervals following the event, the magnitude of the reversals increasing over longer post-event windows, while high-volume day stock price changes taking place on the days when the market index moves in the opposite direction are followed by non-significant price drifts. The results remain robust after accounting for additional company-specific (size, beta, historical volatility) and event-specific (event-day stock's return) factors, and are stronger for low capitalization and high volatility stocks.

Keywords: Availability Heuristic; Behavioral Finance; High Trading Volumes; Overreaction; Stock Price Reversals. (search for similar items in EconPapers)
JEL-codes: G11 G14 G19 (search for similar items in EconPapers)
Date: 2019
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