We propose a deep reinforcement learning method to improve pairs trading by identifying nonlinear relationships in stock news. Using the CSI 300 index constituents from 2015 to 2022, we integrated cointegration and news co-occurrence analysis in asset pairing and used a threshold-based approach in trading design. Results showed our NEWS-CO-DRL method, fusing deep learning and news co-occurrence, outperformed in return generation and risk control, indicating its potential for the Chinese A-share market.

This paper investigates market effects associated with China Security Index 300 (CSI 300) reconstitutions with sample period from April 2005 to Feb 2008. Several findings are listed as followings: Firstly, cumulative abnormal returns for added stocks increase slightly after announcements, while the returns for removed stocks decrease significantly though reverse immediately after index reconstitutions. Considering the whole event period, prices for deletions do not fall dramatically; it’s consistent with asymmetric change of investors’ awareness proposed by H Chen et al (2004). Secondly, both the results of cumulative abnormal returns and volume ratios do not provide evidence to support price pressure hypothesis or index membership hypothesis. We attribute those results to few funds tracking stock indices exactly with the same components and weights as which in the underlying indices in emerging markets, i.e., enhanced index funds are more familiar. Thirdly, the percentage of the additions’ (or deletions’) shares held by funds is not affected obviously by CSI 300 reconstitutions. Finally, we examine index change effects due to IPO that frequently occur in emerging markets, and find that additions witness a full reversal after the first trading day.