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Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets

Author

Listed:
  • Wenbin Hu

    (Hangzhou Dianzi University)

  • Junzi Zhou

    (Zhejiang Financial College)

Abstract

Algorithmic trading is one important financial area of interest to both academic and industrial researchers. With the development of machine learning and deep learning, all kinds of models and techniques are utilized in algorithmic trading. This paper proposes a novel framework for enhancing stock technical analysis strategies by survival analysis. The main idea is to integrate an existing trading strategy with a survival model and make them complementary to each other. By means of survival analysis, the original trading strategy can be extended to introduce an investment target, which is treated as the event of interest. On the other hand, the original trading signal provides survival analysis with a simple and clear starting time point of observation. The trained survival models are used to filter out false trading signals to improve the strategy performance. Under the framework, we propose different filtering methods, utilize different deep survival models, and compare their performance from both trading and model perspectives. We perform extensive and strict backtesting on the daily trading data of 380 plus stocks. The experimental results show that the framework can well improve the performance of technical analysis strategies in different market situations.

Suggested Citation

  • Wenbin Hu & Junzi Zhou, 2024. "Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3473-3507, December.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:6:d:10.1007_s10614-024-10567-8
    DOI: 10.1007/s10614-024-10567-8
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    References listed on IDEAS

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    1. Yoojeong Song & Jae Won Lee & Jongwoo Lee, 2022. "Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 27-38, January.
    2. Souza, Thársis T.P. & Aste, Tomaso, 2019. "Predicting future stock market structure by combining social and financial network information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    3. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    4. Wenbin Hu & Tomasz Zastawniak, 2020. "Pricing high-dimensional American options by kernel ridge regression," Quantitative Finance, Taylor & Francis Journals, vol. 20(5), pages 851-865, May.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    6. Juan Yao & Graham Partington & Max Stevenson, 2005. "Run length and the predictability of stock price reversals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 45(4), pages 653-671, December.
    7. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    8. Håvard Kvamme & Ørnulf Borgan, 2021. "Continuous and discrete-time survival prediction with neural networks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 710-736, October.
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    Cited by:

    1. Hu, Wenbin & Zhou, Junzi, 2025. "Measuring and forecasting financial system resilience under multiple shocks: A survival analysis approach," Pacific-Basin Finance Journal, Elsevier, vol. 94(C).
    2. Wenbin Hu & Junzi Zhou, 2025. "Building Technical Analysis Strategies Using Multivariate Longitudinal and Time-to-Event Data in Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 1911-1942, September.

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