The Asymmetric Positive Feedback Trading in Individual Stocks: China's Evidences

High‐Frequency Positive Feedback Trading and Market Quality: Evidence from China's Stock Market

“…Positive feedback trading means buying shares after positive returns while selling shares after negative returns. As proposed by Wan et al (), this implies that trading volumes react to past absolute returns, and if there were an increase in absolute return, either it is a price rise or drop, then positive feedback trading should lead to a rise in trading volume. So, following the definition of feedback and previous modeling results, we simply define feedback intensity as the average correlation between lagged absolute returns and current trading volume.…”

“…In fact, Wan et al () find that positive feedback trading in China's individual stock is more intensive when stock price goes up, which is just reverse to the findings in index data (Sentana and Wadhwani ; Koutmos ; Koutmos and Saidi ; Hou and Li ). Wan et al () relate the asymmetry to the herding behaviors of retail traders. They show that more retail trading in individual stock leads to more buying‐winners effect, while more rational trading like stop‐loss strategies contributes to more selling‐losers effect.…”

“…Actually, DeLong et al () propose that feedback trading is just to trade according to past performance of stocks, and this implies a relationship between past returns and current trading volume. In the regression models, positive feedback trading is usually examined by lagged returns' coefficients in the models with volume or order imbalance as dependent variables (Griffin et al ; Wan et al ).…”

“…To define the asymmetry between buying‐winners effect and selling‐losers effect, we follow Wan et al () to add a dummy term into the feedback process. Now, the feedback trading volume is as follows: where γ 1 denotes the intensity difference between buying‐winners effect and selling‐losers effect.…”

“…Third, high‐frequency data help to closely examine the relation between investors trading behaviors and their market impact. Previous studies either rely on a long‐term data to estimate the parameter of feedback intensity (Sentana and Wadhwani ) or use daily time series to estimate regression coefficients (Griffin et al ; Wan et al ). These results from low‐frequency data provide an average estimation of feedback trading intensity on the whole market or the entire sample period, while our measures based on high‐frequency data could take account of the variations of feedback intensity across time and firms to reach a more precise study.…”