Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - The Promises and Pitfalls of Machine Learning for Predicting Stock Returns
AU - Leung, Edward
AU - Lohre, Harald
AU - Mischlich, David
AU - Shea, Yifei
AU - Stroh, Maximilian
PY - 2021/5/3
Y1 - 2021/5/3
N2 - Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. The authors confirm this finding when predicting one-month-forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, the authors demonstrate that the economic gains tend to be more limited and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics.
AB - Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. The authors confirm this finding when predicting one-month-forward-looking returns based on a set of common stock characteristics, including predictors such as short-term reversal. Despite the statistical advantage of machine learning model predictions, the authors demonstrate that the economic gains tend to be more limited and critically dependent on the ability to take risk and implement trades efficiently. Unlike traditional models, machine learning models have been somewhat more effective over the past decade at discerning valuable predictions from cross-sectional equity characteristics.
KW - Security analysis and valuation
KW - machine learning
KW - big data
KW - factor investing
U2 - 10.3905/jfds.2021.1.062
DO - 10.3905/jfds.2021.1.062
M3 - Journal article
VL - 3
SP - 21
EP - 50
JO - Journal of Financial Data Science
JF - Journal of Financial Data Science
SN - 2640-3943
IS - 2
ER -