{"title":"What drives stock returns across countries? Insights from machine learning models","authors":"","doi":"10.1016/j.irfa.2024.103569","DOIUrl":null,"url":null,"abstract":"<div><div>We employ machine learning techniques to examine cross-sectional variation in country equity returns by aggregating information across multiple market characteristics. Our models reveal significant return predictability, which translates into discernible patterns in portfolio performance. In addition, variable importance analysis uncovers a sparse factor structure that varies across forecast horizons. A handful of critical predictors—such as long-term reversal, momentum, earnings yield, and market size—capture most of the return differences, while country risk measures play a minor role. Consistent with the partial segmentation perspective, return predictability persists in small, illiquid, and unintegrated markets and weakens over time as the constraints on capital mobility diminish. As a result, attempts to forge them into profitable strategies can be challenging at best.</div></div>","PeriodicalId":48226,"journal":{"name":"International Review of Financial Analysis","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Review of Financial Analysis","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1057521924005015","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
We employ machine learning techniques to examine cross-sectional variation in country equity returns by aggregating information across multiple market characteristics. Our models reveal significant return predictability, which translates into discernible patterns in portfolio performance. In addition, variable importance analysis uncovers a sparse factor structure that varies across forecast horizons. A handful of critical predictors—such as long-term reversal, momentum, earnings yield, and market size—capture most of the return differences, while country risk measures play a minor role. Consistent with the partial segmentation perspective, return predictability persists in small, illiquid, and unintegrated markets and weakens over time as the constraints on capital mobility diminish. As a result, attempts to forge them into profitable strategies can be challenging at best.
期刊介绍:
The International Review of Financial Analysis (IRFA) is an impartial refereed journal designed to serve as a platform for high-quality financial research. It welcomes a diverse range of financial research topics and maintains an unbiased selection process. While not limited to U.S.-centric subjects, IRFA, as its title suggests, is open to valuable research contributions from around the world.