Artikel

Empirical asset pricing via machine learning: evidence from the European stock market

This paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

Sprache
Englisch

Erschienen in
Journal: Journal of Asset Management ; ISSN: 1479-179X ; Volume: 22 ; Year: 2021 ; Issue: 7 ; Pages: 507-538 ; London: Palgrave Macmillan UK

Klassifikation
Wirtschaft
Portfolio Choice; Investment Decisions
Asset Pricing; Trading Volume; Bond Interest Rates
Information and Market Efficiency; Event Studies; Insider Trading
Financial Forecasting and Simulation
Thema
Stock return prediction
Machine learning
Active trading strategy

Ereignis
Geistige Schöpfung
(wer)
Drobetz, Wolfgang
Otto, Tizian
Ereignis
Veröffentlichung
(wer)
Palgrave Macmillan UK
(wo)
London
(wann)
2021

DOI
doi:10.1057/s41260-021-00237-x
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Drobetz, Wolfgang
  • Otto, Tizian
  • Palgrave Macmillan UK

Entstanden

  • 2021

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