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- W4385403765 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." @default.
- W4385403765 created "2023-07-31" @default.
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- W4385403765 date "2023-12-01" @default.
- W4385403765 modified "2023-09-27" @default.
- W4385403765 title "Stop-loss adjusted labels for machine learning-based trading of risky assets" @default.
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- W4385403765 doi "https://doi.org/10.1016/j.frl.2023.104285" @default.
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