Embedded draw-down constraint using ensemble learning for stock trading

Mu En Wu, Sheng Hao Lin, Jia Ching Wang

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

The objective in using the Kelly criterion for money management is to maximize returns; however, in many cases, the risk level exceeds that which the investor can bear. In this study, we present an algorithm to calculate the bidding fraction, while taking into account the level of risk (i.e., the maximum drawdown). The proposed algorithm is based on ensemble learning with a combination of bagging and subset resampling. Our assessment results obtained using the FF48 (i.e., Fama-French-48) dataset revealed that when the maximum drawdown was 5% and 10%, ensemble learning outperformed the conventional approach by 2% and 4%, respectively.

原文???core.languages.en_GB???
頁(從 - 到)5651-5659
頁數9
期刊Journal of Intelligent and Fuzzy Systems
38
發行號5
DOIs
出版狀態已出版 - 2020

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