Investable and Interpretable Machine Learning for Equities

Li, Yimou; Simon, Zachary; Turkington, David

doi:10.3905/jfds.2021.1.084

Cited by 6 publications

(1 citation statement)

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“…Switching from short-term to long-term return prediction is reflected in the importance of characteristics for the machine learning predictions. Long-term equity return predictions tend to relate more strongly to slow-moving factors, such as low volatility and beta; see for example Li, Simon, and Turkington (2022). Instead of considering forecast horizons longer than 1 month, one can also go in the opposite direction.…”

Section: Choice Of Targetmentioning

confidence: 99%

How Can Machine Learning Advance Quantitative Asset Management?

Blitz¹,

Hoogteijling²,

Lohre³

et al. 2023

JPM

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The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modelling approaches. In this paper, we discuss the promises and pitfalls of applying machine learning to asset management, by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.

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Section: Choice Of Targetmentioning

confidence: 99%