Embedded draw-down constraint using ensemble learning for stock trading

Issue title: Special section: Intelligent data analysis and applications & smart vehicular technology, communications and applications

Guest editors: Valentina Emilia Balas and Lakhmi C. Jain

Article type: Research Article

Authors: Wu, Mu-En^{a; *} | Lin, Sheng-Hao^b | Wang, Jia-Ching^b

Affiliations: [a] Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan | [b] Department of Computer Science & Information Engineering, National Central University, Taoyuan, Taiwan

Correspondence: [*] Corresponding author. Mu-En Wu, Department of Information and Finance Management, National Taipei University of Technology, No. 1, Section 3, Zhongxiao E Rd, Da’an District, Taipei City, 106 Taipei, Taiwan. E-mail: [email protected].

Abstract: 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.

Keywords: Kelly criterion, ensemble learning, Monte Carlo simulation, money managemen

DOI: 10.3233/JIFS-179654

Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 5651-5659, 2020