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Incorporating causal notions to forecasting time series: a case study

Author

Listed:
  • Werner Kristjanpoller

    (Universidad Técnica Federico Santa María)

  • Kevin Michell

    (Universidad Técnica Federico Santa María)

  • Cristian Llanos

    (Universidad Técnica Federico Santa María)

  • Marcel C. Minutolo

    (Robert Morris University)

Abstract

Financial time series have been analyzed with a wide variety of models and approaches, some of which can forecast with great accuracy. However, most of these models, especially the machine learning ones, cannot show additional information for the decision maker or the financial analyst. The notion of causality is a concept that provides a more complete understanding of a problem beyond improved forecasts. In this study, we propose integrating the treatment/control concept of causality into a forecasting framework to better predict financial time series. Our results show that the proposed methodology outperforms classic econometric approaches such as ARIMA and Random Walk, as well as machine learning approaches without the proposed methodology. This improvement is statistically significant, as indicated by the Model Confidence Set test in the complete test set and quarterly analysis.

Suggested Citation

  • Werner Kristjanpoller & Kevin Michell & Cristian Llanos & Marcel C. Minutolo, 2025. "Incorporating causal notions to forecasting time series: a case study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-22, December.
  • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-024-00681-9
    DOI: 10.1186/s40854-024-00681-9
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    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Luc Bauwens & Arie Preminger & Jeroen V. K. Rombouts, 2010. "Theory and inference for a Markov switching GARCH model," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 218-244, July.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Francis X. Diebold & Roberto S. Mariano, 1991. "Comparing predictive accuracy I: an asymptotic test," Discussion Paper / Institute for Empirical Macroeconomics 52, Federal Reserve Bank of Minneapolis.
    5. A. Verma & R. J. Buonocore & T. Di Matteo, 2019. "A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 981-996, June.
    6. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    7. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    8. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    9. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    10. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    11. Runde, Jochen, 1998. "Assessing Causal Economic Explanations," Oxford Economic Papers, Oxford University Press, vol. 50(2), pages 151-172, April.
    12. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    13. Anatolyev, Stanislav & Baruník, Jozef, 2019. "Forecasting dynamic return distributions based on ordered binary choice," International Journal of Forecasting, Elsevier, vol. 35(3), pages 823-835.
    14. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    15. Noemi Schmitt & Frank Westerhoff, 2017. "Herding behaviour and volatility clustering in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1187-1203, August.
    16. 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.
    17. Muhammad Ali & Dost Muhammad Khan & Muhammad Aamir & Amjad Ali & Zubair Ahmad & A. Dionisio, 2021. "Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine," Complexity, Hindawi, vol. 2021, pages 1-13, December.
    18. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    19. Malliaris, A.G. & Malliaris, Mary, 2015. "What drives gold returns? A decision tree analysis," Finance Research Letters, Elsevier, vol. 13(C), pages 45-53.
    20. James B. Heaton & Nicholas Polson & Jan H. Witte, 2017. "Rejoinder to ‘Deep learning for finance: deep portfolios’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 19-21, January.
    21. Bodendorf, Frank & Sauter, Maximilian & Franke, Jörg, 2023. "A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning," International Journal of Production Economics, Elsevier, vol. 256(C).
    22. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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