The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Edward Leung; Harald Lohre; David Mischlich; Yifei Shea; Maximilian Stroh

doi:10.3905/jfds.2021.1.062

The Promises and Pitfalls of Machine Learning for Predicting Stock Returns

Edward Leung, Harald Lohre, David Mischlich, Yifei Shea and Maximilian Stroh

The Journal of Financial Data Science Spring 2021, 3 (2) 21-50; DOI: https://doi.org/10.3905/jfds.2021.1.062

Edward Leung

is a senior research analyst at Invesco Quantitative Strategies in New York, NY

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Harald Lohre

is the director of research at Invesco Quantitative Strategies in Frankfurt, Germany; a Fellow of the Centre for Endowment Asset Management (CEAM) in the Cambridge Judge Business School at the University of Cambridge in Cambridge, UK; and visiting research fellow in the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy (EMP) at Lancaster University Management School Bailrigg in Lancaster, UK

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David Mischlich

is a research analyst at Invesco Quantitative Strategies in Frankfurt, Germany

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Yifei Shea

is a senior research analyst at Invesco Quantitative Strategies in Boston, MA

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Maximilian Stroh

is the head of research forecasts at Quoniam Asset Management in Frankfurt, Germany

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The Journal of Financial Data Science

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The Promises and Pitfalls of Machine Learning for Predicting Stock Returns