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

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

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.

Suggested Citation

  • Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006578
    DOI: 10.1016/j.frl.2023.104285
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    References listed on IDEAS

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    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    2. Geonhwan Ju & Kyoung-Kuk Kim & Dong-Young Lim, 2019. "Learning multi-market microstructure from order book data," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1517-1529, September.
    3. Jinan Liu & Apostolos Serletis, 2019. "Volatility in the Cryptocurrency Market," Open Economies Review, Springer, vol. 30(4), pages 779-811, September.
    4. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    5. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo & Rocha, Ana Paula, 2019. "Deep learning in exchange markets," Information Economics and Policy, Elsevier, vol. 47(C), pages 38-51.
    6. Jinan Liu & Apostolos Serletis, 2019. "Volatility in the Cryptocurrency Market," Open Economies Review, Springer, vol. 30(4), pages 779-811, September.
    7. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    8. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    9. Santos, André A.P. & Torrent, Hudson S., 2022. "Markowitz meets technical analysis: Building optimal portfolios by exploiting information in trend-following signals," Finance Research Letters, Elsevier, vol. 49(C).
    10. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    11. Fuli Feng & Huimin Chen & Xiangnan He & Ji Ding & Maosong Sun & Tat-Seng Chua, 2018. "Enhancing Stock Movement Prediction with Adversarial Training," Papers 1810.09936, arXiv.org, revised Jun 2019.
    12. Jegadeesh, Narasimhan, 1991. "Seasonality in Stock Price Mean Reversion: Evidence from the U.S. and the U.K," Journal of Finance, American Finance Association, vol. 46(4), pages 1427-1444, September.
    13. Wei-Chang Yeh & Yu-Hsin Hsieh & Chia-Ling Huang, 2022. "Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm," Papers 2211.12839, arXiv.org.
    14. Kaminski, Kathryn M. & Lo, Andrew W., 2014. "When do stop-loss rules stop losses?," Journal of Financial Markets, Elsevier, vol. 18(C), pages 234-254.
    15. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
    16. Justin A. Sirignano, 2019. "Deep learning for limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 549-570, April.
    17. Bochuan Dai & Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2021. "Risk reduction using trailing stop‐loss rules," International Review of Finance, International Review of Finance Ltd., vol. 21(4), pages 1334-1352, December.
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