Are Pair Trading Strategies Profitable During COVID-19 Period?

Mohammad Khalid Sohail, A. Raheman, Javid Iqbal, M. Sindhu, Abdul Staar, Muhammad Mushafiq, Humaira Afzal
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引用次数: 1

Abstract

Pair trading strategy is a well-known profitable strategy in stock, forex, and commodity markets. As most of the world stock markets declined during COVID-19 period, therefore this study is going to observe whether this strategy is still profitable after COVID-19 pandemic. One of the powerful algorithms of DBSCAN under the umbrella of unsupervised machine learning is applied and three clusters were formed by using market and accounting data. The formation of these three clusters was based on book value per share, earning per share, classification of sector, market capitalisation and with other factors formed from PCA on the returns of daily data of six months of the 80 sample firms for year 2019–2020. An average of [Formula: see text] average excess monthly return with Sharpe ratio of [Formula: see text] and Treynor ratio of [Formula: see text] is to be observed in COVID-19 pandemic period. However, the result of risk-adjusted performance under Jensen’s alpha is observed to be insignificant. The policy implication of this study, for different portfolios and fund managers is suggested to use machine learning approach to get positive and higher returns for their clients.
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在COVID-19期间,配对交易策略是否有利可图?
配对交易策略是股票、外汇和商品市场中众所周知的盈利策略。由于全球大部分股市在COVID-19期间下跌,因此本研究将观察该策略在COVID-19大流行后是否仍然有利可图。采用无监督机器学习框架下的DBSCAN算法,利用市场数据和会计数据组成三个聚类。这三个集群的形成是基于每股账面价值、每股收益、行业分类、市值以及其他因素,这些因素是根据80家样本公司2019-2020年6个月的每日数据回报通过PCA形成的。以[公式:见文]的夏普比率[公式:见文]和[公式:见文]的特雷纳比率[公式:见文]计算2019冠状病毒病大流行期间[公式:见文]的平均超额月收益的平均值。然而,风险调整后的业绩在Jensen alpha下的结果是不显著的。本研究的政策含义是,对于不同的投资组合和基金经理,建议使用机器学习方法为其客户获得正的和更高的回报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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