The predictability of cross-sectional returns in high frequency

dc.creatorWang, Yifan
dc.date2022-05-07
dc.date.accessioned2022-11-03T21:00:16Z
dc.date.available2022-11-03T21:00:16Z
dc.identifierhttps://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/view/84399
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5044682
dc.descriptionStock return forecast is of great importance to trading, hedging, and portfolio management. In this article, we apply LASSO and random forest to make rolling one-minute-ahead return forecasts of Dow Jones stocks, using the cross-section of lagged returns of S&P 500 components as candidate predictors. Although the number of candidate variables is large, the negative out-of-sample R2 suggests that the predictions from LASSO and random forest give larger mean-squared error than the historical average. So, there is no evidence of predictability in the cross-sectional returns of large stocks in high frequency. The predictability presented by Chinco et al. (2019) might be due to the interaction between large and small stocks.en-US
dc.formatapplication/pdf
dc.languageeng
dc.publisherLociedade Brasileira de Finançasen-US
dc.relationhttps://bibliotecadigital.fgv.br/ojs/index.php/rbfin/article/view/84399/80882
dc.rightsCopyright (c) 2022 Revista Brasileira de Finançaspt-BR
dc.sourceBrazilian Review of Finance; Vol. 20 No. 1 (2022): January-March; 105-126en-US
dc.sourceRevista Brasileira de Finanças; v. 20 n. 1 (2022): Janeiro-Março; 105-126pt-BR
dc.source1984-5146
dc.source1679-0731
dc.subjectMachine learningen-US
dc.subjectForecasting returnsen-US
dc.subjectG12en-US
dc.subjectG17en-US
dc.titleThe predictability of cross-sectional returns in high frequencyen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeDouble blind reviewed articlesen-US
dc.typeAvaliado por Parespt-BR


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