Multi-Horizon Equity Returns Predictability via Machine Learning

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: IES Working Paper ; No. 2/2021

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Portfolio Choice; Investment Decisions ; Asset Pricing; Trading Volume; Bond Interest Rates ; International Financial Markets ; Large Data Sets: Modeling and Analysis
• Thema: Machine learning ; asset pricing ; horizon predictability ; anomalies

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Nechvatalova, Lenka
• Ereignis: Veröffentlichung
• (wer): Charles University in Prague, Institute of Economic Studies (IES)
• (wo): Prague
• (wann): 2021

Weitere Informationen

• Handle: http://hdl.handle.net/10419/247369
• Letzte Aktualisierung: 10.03.2025, 11:46 MEZ