Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy

doi:10.1007/s10614-021-10169-8

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	Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy
	Zhan, Baoqiang 1; Zhang, Shu 2; Du, Helen S.2; Yang, Xiaoguang 3
	2021-11-19
发表期刊	COMPUTATIONAL ECONOMICS
ISSN	0927-7099
页码	22
摘要	Arbitrage opportunity exploration is important to ensure the profitability of statistical arbitrage. Prior studies that concentrate on cointegration model and other predictive models suffer from various problems in both prediction and transaction. To prevent these problems, we propose a novel strategy based on machine learning to explore arbitrage opportunities and further predict whether they will make a profit or not. The experiment is conducted in the context of Chinese financial markets with high-frequency data of CSI 300 exchange traded fund (ETF) and CSI 300 index futures (IF) from 2012 to 2020. We find that machine learning strategy can explore more arbitrage opportunities with lower risks, which outperforms cointegration strategy in different aspects. Besides, we compare different algorithms and find that LSTM achieve better performance in predicting the positive arbitrage samples and obtaining higher ROI and Sharpe ratio. The profitability of machine learning strategy validate the mean reversion and price discovery function of asset price between spot market and futures market, which further substantiate the market efficiency. Our empirical results provide practical significance to the development of quantitative finance.
关键词	Statistical arbitrage Cointegration Machine learning Opportunities exploration
DOI	10.1007/s10614-021-10169-8
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71532013] ; National Natural Science Foundation of China[71431008] ; National Natural Science Foundation of China[71572050]
WOS研究方向	Business & Economics ; Mathematics
WOS类目	Economics ; Management ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000720620200001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59593
专题	中国科学院数学与系统科学研究院
通讯作者	Zhan, Baoqiang
作者单位	1.Harbin Inst Technol, Sch Management, Harbin, Peoples R China 2.Guangdong Univ Technol, Sch Management, Guangzhou, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhan, Baoqiang,Zhang, Shu,Du, Helen S.,et al. Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy[J]. COMPUTATIONAL ECONOMICS,2021:22.
APA	Zhan, Baoqiang,Zhang, Shu,Du, Helen S.,&Yang, Xiaoguang.(2021).Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy.COMPUTATIONAL ECONOMICS,22.
MLA	Zhan, Baoqiang,et al."Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy".COMPUTATIONAL ECONOMICS (2021):22.