The paper “Technical Analysis Profitability without Data Snooping Bias: Evidence from Chinese Stock Market”, co-authored by Fuwei JIANG, the first author, Guokai SONG, an undergraduate student of cohort 2014 in the School of Finance, and Guoshi TONG, an assistant professor of Renmin University, was recently accepted and published by "International Review of Finance". Guokai SONG, one of the co-authors of this paper, began to join the "Distinguished Academic Talents Training Project of School of Finance" in his third year. This Distinguished Academic Talents Training Project of School of Finance started in 2011. The purpose of this project is to discover new academic talents from undergraduates, stimulate their academic interests, shape their research abilities, build their academic self-confidence and cultivate academic talents. After several years of development, many achievements have been made.

Abstract: We perform a comprehensive analysis on the profitability of a large number of technical analysis based trading rules in Chinese stock market. To counter data snooping bias, we employ a stepwise superior predictive ability test to identify genuinely profitable trading rules among more than 28,000 technical signals. Using 19?years of daily data on Chinese aggregate stock market return, we find substantial evidence on the profitability of technical trading rules measured by either the market timing ability or Sharpe ratio gain. Our results on the profitability of technical rules hold during different subperiods and remain valid under the presence of transaction costs.