Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/317543 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 23 [Issue:] 1 [Year:] 2022 [Pages:] 1-19
Publisher: 
Vilnius Gediminas Technical University, Vilnius
Abstract: 
This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.
Subjects: 
stock market
investment algorithm
trading rules
alpha maximization
market timing
artificial intelligence
machine learning
reinforcement learning
evolutionary computation
perceptron
JEL: 
G11
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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