Abstract:

Paired trading and multi-factor stock selection are typical quantitative trading strategies. The former is based on statistical arbitrage, which takes advantage of the short-term price deviation of two assets and then hedges the risk to obtain the Alpha return of the two assets. The core assumption is that the spread of paired assets has the property of mean reversion. The latter selects stocks with high comprehensive scores on factors with logical meanings. Due to the high degree of economic connection between China and the United States, there exists price co-movement in the stock markets of the two countries. We construct US equities-A share paired factor according to the ideas of the paired trading. Then the factor is used to select stocks. The empirical analysis reveals that the constructed factor has a relatively good performance in differentiating A share. And it has a low correlation with traditional stock selection factors. Therefore, the factor can be used in the multi-factor model to select stocks. The contribution of this paper lies in the fact that we propose a stock selection factor not examined in the previous research, and verify the validity of this factor in the empirical research.

Key words: quantitative investment, paired trading, algorithmic trading, corrdlation of the Sino-US financial market linka

摘要：

量化交易是指借助现代统计学和数学的方法，利用计算机技术进行交易的证券投资方式。配对交易和多因子选股是典型的量化交易策略。配对交易是基于统计套利，利用两个资产的短暂价格偏离，进行风险对冲以获取两个资产的Alpha收益，其核心假设是配对资产的价差具有均值回复性。多因子选股则将多个具有逻辑背景的因子策略相结合，选取在各个因子上综合得分较高的股票构建投资组合，其核心是如何挖掘具有逻辑背景的因子。由于中美经济关联度很大，两国股市间存在着联动性。根据配对交易的思想构建美股-A股配对因子，利用该因子对A股进行选股，通过实证分析，发现配对因子对A股股票有较好的区分度，且与传统的选股因子存在较低的相关性。因此，根据配对交易思想构建的美股-A股配对因子可用于多因子模型进行选股。贡献在于提出了以往研究未提出的因子，通过实证研究证明了该因子的有效性。

关键词: 量化投资, 配对交易, 算法交易, 中美金融市场联动