Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song
{"title":"通过机器学习预测韩国股票市场的回报率","authors":"Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song","doi":"10.1007/s42952-023-00245-0","DOIUrl":null,"url":null,"abstract":"<p>In this study, we aim to forecast monthly stock returns and analyze factors influencing stock prices in the Korean stock market. To find a model that maximizes the cumulative return of the portfolio of stocks with high predicted returns, we use machine learning models such as linear models, tree-based models, neural networks, and learning to rank algorithms. We employ a novel validation metric which we call the Cumulative net Return of a Portfolio with top 10% predicted return (CRP10) for tuning hyperparameters to increase the cumulative return of the selected portfolio. CRP10 tends to provide higher cumulative returns compared to out-of-sample R-squared as a validation metric with the data that we used. Our findings indicate that Light Gradient Boosting Machine (LightGBM) and Gradient Boosted Regression Trees (GBRT) demonstrate better performance than other models when we apply a single model for the entire test period. We also take the strategy of changing the model on a yearly basis by assessing the best model annually and observed that it did not outperform the approach of using a single model such as LightGBM or GBRT for the entire period.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Return prediction by machine learning for the Korean stock market\",\"authors\":\"Wonwoo Choi, Seongho Jang, Sanghee Kim, Chayoung Park, Sunyoung Park, Seongjoo Song\",\"doi\":\"10.1007/s42952-023-00245-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this study, we aim to forecast monthly stock returns and analyze factors influencing stock prices in the Korean stock market. To find a model that maximizes the cumulative return of the portfolio of stocks with high predicted returns, we use machine learning models such as linear models, tree-based models, neural networks, and learning to rank algorithms. We employ a novel validation metric which we call the Cumulative net Return of a Portfolio with top 10% predicted return (CRP10) for tuning hyperparameters to increase the cumulative return of the selected portfolio. CRP10 tends to provide higher cumulative returns compared to out-of-sample R-squared as a validation metric with the data that we used. Our findings indicate that Light Gradient Boosting Machine (LightGBM) and Gradient Boosted Regression Trees (GBRT) demonstrate better performance than other models when we apply a single model for the entire test period. We also take the strategy of changing the model on a yearly basis by assessing the best model annually and observed that it did not outperform the approach of using a single model such as LightGBM or GBRT for the entire period.</p>\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2023-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s42952-023-00245-0\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s42952-023-00245-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在本研究中,我们旨在预测韩国股市的月度股票回报率并分析影响股价的因素。为了找到一个能使预测回报率高的股票投资组合的累计回报率最大化的模型,我们使用了线性模型、树型模型、神经网络和学习排名算法等机器学习模型。我们采用了一种新颖的验证指标,称为 "预测回报率前 10%的投资组合的累计净回报率(CRP10)",用于调整超参数,以提高所选投资组合的累计回报率。在我们使用的数据中,与样本外 R 平方作为验证指标相比,CRP10 往往能提供更高的累计回报。我们的研究结果表明,当我们在整个测试期间使用单一模型时,轻梯度提升机(LightGBM)和梯度提升回归树(GBRT)比其他模型表现得更好。我们还采取了每年更换模型的策略,每年对最佳模型进行评估,结果发现,在整个测试期间使用 LightGBM 或 GBRT 等单一模型的效果并不理想。
Return prediction by machine learning for the Korean stock market
In this study, we aim to forecast monthly stock returns and analyze factors influencing stock prices in the Korean stock market. To find a model that maximizes the cumulative return of the portfolio of stocks with high predicted returns, we use machine learning models such as linear models, tree-based models, neural networks, and learning to rank algorithms. We employ a novel validation metric which we call the Cumulative net Return of a Portfolio with top 10% predicted return (CRP10) for tuning hyperparameters to increase the cumulative return of the selected portfolio. CRP10 tends to provide higher cumulative returns compared to out-of-sample R-squared as a validation metric with the data that we used. Our findings indicate that Light Gradient Boosting Machine (LightGBM) and Gradient Boosted Regression Trees (GBRT) demonstrate better performance than other models when we apply a single model for the entire test period. We also take the strategy of changing the model on a yearly basis by assessing the best model annually and observed that it did not outperform the approach of using a single model such as LightGBM or GBRT for the entire period.