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.

我们提出了基于基本面分析和机器学习的中期股票投资策略。该方法使用来自收益能力指数 (EPI) 的预测变量作为来自公司财务报表的横截面和时间序列数据的输入变量。机器学习的分析方法使我们能够直接验证财务因素与超额收益之间的联系。然后，我们选择在下一次披露财务报表时收益可能会增加的股票。为了验证所提出方法的有效性，我们使用了 2013 年至 2019 年在韩国股票市场公开上市的公司数据。我们基于十种机器学习技术，通过三种不同的衡量指标形成多头、空头和对冲投资组合来检验交易策略的盈利能力. 结果，大多数投资组合，包括与 EPI 相关的变量，无论时期如何，都呈现正回报。特别是具有 sigmoid 函数的两层神经网络分别在 3 个月和 6 个月期间表现出最佳性能。我们的结果表明，结合机器学习对中期股票投资很有用。有必要进一步研究财务报表分析和机器学习技术的可能融合。