Embedded draw-down constraint using ensemble learning for stock trading

Mu En Wu, Sheng Hao Lin, Jia Ching Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)5651-5659
Number of pages9
JournalJournal of Intelligent and Fuzzy Systems
Volume38
Issue number5
DOIs
StatePublished - 2020

Keywords

  • ensemble learning
  • Kelly criterion
  • money managemen
  • Monte Carlo simulation

Fingerprint

Dive into the research topics of 'Embedded draw-down constraint using ensemble learning for stock trading'. Together they form a unique fingerprint.

Cite this