Publication detail

Multi-Horizon Equity Returns Predictability via Machine Learning

Author(s): Mgr. Lenka Nechvátalová ,
Type: IES Working Papers
Year: 2021
Number: 2
ISSN / ISBN:
Published in: IES Working Papers 2/2021
Publishing place: Prague
Keywords: Machine learning, asset pricing, horizon predictability, anomalies
JEL codes: G11, G12, G15, C55
Suggested Citation: Nechvatalova L. (2021): "Multi-Horizon Equity Returns Predictability via Machine Learning" IES Working Papers 2/2021. IES FSV. Charles University.
Abstract: We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, before and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting horizon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reducing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S.
Downloadable: wp_2021_02_nechvatalova

Partners

Deloitte

Sponsors

CRIF
McKinsey
Patria Finance