0587009 20250210141942.120240620235959.9 85194413663 001231631500002 10.32065/CJEF.2024.02.01 48 s. E cav_un_epca*0516895FINANCE A UVER-CZECH JOURNAL OF ECONOMICS AND FINANCE742 (2024)142-190 machine learning asset pricing horizon predictability anomalies cav_un_auth*0468864 Nechvátalová Lenka UTIA-B Ekonometrie Department of Econometrics E E CZ 100 K Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2024/E/nechvatalova-0587009.pdf https://journal.fsv.cuni.cz/mag/article/show/id/1531 316521 GA UK CZ cav_un_auth*0468920 GX19-28231X GA ČR CZ cav_un_auth*0385135 We investigate the predictability of global expected stock returns across various forecasting horizons using machine learning techniques. We find that the predictability of returns decreases with longer forecasting horizons both in the U.S. and internationally. Despite this, we provide evidence that using firm-specific characteristics can remain profitable even after accounting for transaction costs, especially when we consider longer forecasting horizons. Studying the profitability of long-short portfolios, we highlight a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. Increasing the forecasting horizon while matching the rebalancing period increases risk-adjusted returns after transaction costs for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and a turnover reducing strategy, buy/hold spread. Double sorting on different horizons significantly increases profitability in the U.S. market, while buy/hold spread portfolios exhibit better risk-adjusted profitability. WOS AH 50000 50200 50202 2025 1 RVO:67985556 https://hdl.handle.net/11104/0354367 cav_un_auth*0359004 IES FSV UK CZ S A 2024 85194413663 SCOPUS PUBMED 001231631500002 WOS cav_un_epca*0516895 FINANCE A UVER-CZECH JOURNAL OF ECONOMICS AND FINANCE 74 2 2024 142 190 2464-7683