So Young Jun , Dong Sung Kim , Suk Yoon Jung , Sang Gyung Jun , Jong Woo Kim
{"title":"Stock investment strategy combining earnings power index and machine learning","authors":"So Young Jun , Dong Sung Kim , Suk Yoon Jung , Sang Gyung Jun , Jong Woo Kim","doi":"10.1016/j.accinf.2022.100576","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":47170,"journal":{"name":"International Journal of Accounting Information Systems","volume":"47 ","pages":"Article 100576"},"PeriodicalIF":4.1000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Accounting Information Systems","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1467089522000288","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
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.
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.