An on-line machine learning return prediction

Lu, Yueliang (Jacques) and Weidong Tian

Pacific-Basin Finance Journal, 2023, vol. 79, issue C

Abstract: This paper introduces a novel methodology for predicting relative asset returns using a large dataset. Our approach utilizes on-line universal portfolio construction and generates a closed-form prediction formula based solely on historical data. Our results demonstrate that the predictive error can be as low as 2% and is robust. These findings suggest that on-line machine learning techniques have the potential to predict relative asset returns when sufficient data is available.

Keywords: On-line machine learning; Relative return predictability; Universal portfolio; Information theory (search for similar items in EconPapers)
JEL-codes: C53 G11 G12 G15 G17 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:79:y:2023:i:c:s0927538x23001154

DOI: 10.1016/j.pacfin.2023.102049

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