Portfolio optimization by enhanced LinUCB

He Ni, Qin Zhang, Xingjian Guo and Sultan Sikandar Mirza

Finance Research Letters, 2024, vol. 70, issue C

Abstract: We study portfolio optimization through reinforcement learning, employing the contextual LinUCB approach to build an investment portfolio with changing market conditions. Contextual information enhances the LinUCB technique recommendation process, maximizing the efficacy of the model and the cumulative return. The recommendation criteria are to achieve the maximum level of confidence regarding the uncertainty surrounding the upward trend of asset returns within the pool of assets. Employing stocks from the Chinese A-share market, Hong Kong market, and US market as empirical tests, our findings demonstrate that the proposed model consistently outperforms the benchmark indices in our backtesting process.

Keywords: Portfolio management; Contextual LinUCB; Bandit learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:70:y:2024:i:c:s1544612324012959

DOI: 10.1016/j.frl.2024.106266

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