Stock investment strategy combining earnings power index and machine learning

IF 4.1 3区 管理学 Q2 BUSINESS International Journal of Accounting Information Systems Pub Date : 2022-12-01 DOI:10.1016/j.accinf.2022.100576
So Young Jun , Dong Sung Kim , Suk Yoon Jung , Sang Gyung Jun , Jong Woo Kim
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引用次数: 2

Abstract

We propose an intermediate-term stock investment strategy based on fundamental analysis and machine learning. The approach uses predictors from the Earnings Power Index (EPI) as input variables derived from cross-sectional and time-series data from a company’s financial statements. The analytical methods of machine learning allow us to validate the link between financial factors and excess returns directly. We then select stocks for which returns are likely to increase at the time of the next disclosed financial statement. To verify the proposed approach’s usefulness, we use company data listed publicly on the Korean stock market from 2013 to 2019. We examine the profitability of trading strategy based on ten machine-learning techniques by forming long, short, and hedge portfolios with three different measures. As a result, most portfolios, including EPI-related variables, present positive returns regardless of the period. Especially, the neural network of the two layers with sigmoid function presents the best performance for the period of 3 months and 6 months, respectively. Our results show that incorporating machine learning is useful for mid-term stock investment. Further research into the possible convergence of financial statement analysis and machine-learning techniques is warranted.

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结合盈利能力指数和机器学习的股票投资策略
我们提出了一种基于基本面分析和机器学习的中期股票投资策略。该方法使用来自盈利能力指数(EPI)的预测因子作为输入变量,这些变量来自公司财务报表的横截面和时间序列数据。机器学习的分析方法使我们能够直接验证金融因素与超额回报之间的联系。然后,我们选择在下次披露财务报表时收益可能增加的股票。为了验证该方法的有效性,我们使用了2013年至2019年在韩国股市公开上市的公司数据。我们通过用三种不同的方法形成多头、空头和对冲投资组合,研究了基于十种机器学习技术的交易策略的盈利能力。因此,大多数投资组合,包括与epi相关的变量,无论在哪个时期都呈现正回报。其中,具有s型函数的两层神经网络分别在3个月和6个月时表现最佳。我们的研究结果表明,结合机器学习对中期股票投资是有用的。进一步研究财务报表分析和机器学习技术可能的融合是必要的。
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来源期刊
CiteScore
9.00
自引率
6.50%
发文量
23
期刊介绍: The International Journal of Accounting Information Systems will publish thoughtful, well developed articles that examine the rapidly evolving relationship between accounting and information technology. Articles may range from empirical to analytical, from practice-based to the development of new techniques, but must be related to problems facing the integration of accounting and information technology. The journal will address (but will not limit itself to) the following specific issues: control and auditability of information systems; management of information technology; artificial intelligence research in accounting; development issues in accounting and information systems; human factors issues related to information technology; development of theories related to information technology; methodological issues in information technology research; information systems validation; human–computer interaction research in accounting information systems. The journal welcomes and encourages articles from both practitioners and academicians.
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