Stop-loss adjusted labels for machine learning-based trading of risky assets

【Author】 Hwang, Yoontae; Park, Junpyo; Lee, Yongjae; Lim, Dong-Young

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U. S. futures and cryptocurrencies show that this simple tweak significantly reduces risk.

【Keywords】Stop -loss trading; Asset price prediction; Cryptocurrency; Machine learning

【发表时间】2023 DEC

【收录时间】2023-09-21

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-市场分析