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

Yoontae Hwang, Junpyo Park, Yongjae Lee and Dong-Young Lim

Finance Research Letters, 2023, vol. 58, issue PA

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 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006578

DOI: 10.1016/j.frl.2023.104285

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