IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i3d10.1007_s10614-024-10782-3.html

Building Technical Analysis Strategies Using Multivariate Longitudinal and Time-to-Event Data in Stock Markets

Author

Listed:
  • Wenbin Hu

    (Hangzhou Dianzi University)

  • Junzi Zhou

    (Zhejiang Financial College)

Abstract

Stock market prediction by machine learning techniques has been attracting more and more attention. Technical analysis trading strategies with binary classifiers that can predict market moving direction are such typical applications. However, there exist two deficiencies in applying binary classifiers. First, they only predict whether instead of when the interested event will occur. Second, they usually only use the cross-sectional information, without taking account of the longitudinal evolution of the features. In this paper, we propose to utilize multivariate functional principal component analysis (MFPCA) to overcome the second deficiency and obtain better trading strategies. MFPCA is used as a data augmentation tool to systematically extract longitudinal informative features that can replace technical indicators. Technical analysis trading strategies enhanced by survival models with MFPCA are built, with backtesting on the daily trading data of S&P 500 stocks. The experimental results show that MFPCA can significantly improve both the performance of the survival models and the trading strategies. We further show the contribution and effectiveness of MFPCA by interpreting the deep learning survival model and comparing with the long short-term memory model that can process multi-timestep information.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10782-3
    DOI: 10.1007/s10614-024-10782-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10782-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10782-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Laukaitis, Algirdas, 2008. "Functional data analysis for cash flow and transactions intensity continuous-time prediction using Hilbert-valued autoregressive processes," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1607-1614, March.
    2. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    3. 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.
    4. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    5. 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.
    6. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
    7. Álvaro Arroyo & Álvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2024. "Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers," Quantitative Finance, Taylor & Francis Journals, vol. 24(1), pages 35-57, January.
    8. Medina-Olivares, Victor & Calabrese, Raffaella & Crook, Jonathan & Lindgren, Finn, 2023. "Joint models for longitudinal and discrete survival data in credit scoring," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1457-1473.
    9. Clara Happ & Sonja Greven, 2018. "Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 649-659, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Álvarez-Liébana, Javier & Bosq, Denis & Ruiz-Medina, María D., 2016. "Consistency of the plug-in functional predictor of the Ornstein–Uhlenbeck process in Hilbert and Banach spaces," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 12-22.
    3. Haixu Wang & Jiguo Cao, 2023. "Nonlinear prediction of functional time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    4. Cedric H. A. Koffi & Viani Biatat Djeundje & Olivier Menoukeu Pamen, 2024. "Quantifying socio-temporal effects of loan delinquency drivers in microfinance," Papers 2410.13100, arXiv.org, revised Aug 2025.
    5. Sultan Amed & Tanmay Sen & Sayantan Banerjee, 2026. "FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling," Papers 2601.11134, arXiv.org.
    6. Jianwei Peng & Stefan Lessmann, 2026. "Incorporating data drift to perform survival analysis on credit risk," Papers 2601.20533, arXiv.org.
    7. Andrei Konstantinov & Lev Utkin & Vlada Efremenko & Vladimir Muliukha & Alexey Lukashin & Natalya Verbova, 2025. "Survival Analysis as Imprecise Classification with Trainable Kernels," Mathematics, MDPI, vol. 13(18), pages 1-31, September.
    8. Boukhiar, Souad & Mourid, Tahar, 2022. "Resolvent estimators for functional autoregressive processes with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    10. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    11. Hao Wang & Anthony Bellotti & Rong Qu & Ruibin Bai, 2024. "Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk," Risks, MDPI, vol. 12(2), pages 1-26, February.
    12. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    13. Amira Elayouty & Marian Scott & Claire Miller, 2022. "Time-Varying Functional Principal Components for Non-Stationary EpCO $$_2$$ 2 in Freshwater Systems," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 506-522, September.
    14. Patilea, Valentin & Wang, Sunny G․ W․, 2026. "Rate accelerated inference for integrals of multivariate random functions," Computational Statistics & Data Analysis, Elsevier, vol. 214(C).
    15. Sina Montazeri & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024. "CNN-DRL for Scalable Actions in Finance," Papers 2401.06179, arXiv.org.
    16. Jiwon Jung & Kiseop Lee, 2024. "Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book," Papers 2409.02277, arXiv.org, revised Nov 2024.
    17. Rad, Hossein & Low, Rand Kwong Yew & Miffre, Joëlle & Faff, Robert, 2023. "The commodity risk premium and neural networks," Journal of Empirical Finance, Elsevier, vol. 74(C).
    18. Noura Metawa & Mohamemd I. Alghamdi & Ibrahim M. El-Hasnony & Mohamed Elhoseny, 2021. "Return Rate Prediction in Blockchain Financial Products Using Deep Learning," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    19. Mirosław Krzyśko & Marcin Szymkowiak & Waldemar Wołyński & Dorota Wyszkowska, 2026. "Mapping Quality of Life in Polish Functional Urban Areas: A Functional Data Analysis Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 181(1), pages 1-26, January.
    20. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10782-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.