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Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research

Author

Listed:
  • Efstathios Polyzos

    (Zayed University)

  • Costas Siriopoulos

    (Zayed University)

Abstract

This paper provides evidence on the use of Random Regression Forests (RRF) for optimal lag selection. Using an extended sample of 144 data series, of various data types with different frequencies and sample sizes, we perform optimal lag selection using RRF and compare the results with seven “traditional” information criteria as well as with three other machine learning approaches. We show that the different information criteria produce differing outcomes in terms of optimal lag selection. To quantify performance, we compare the forecast errors on autoregressive models using the optimal lags selected by the criteria and demonstrate that RRF outperforms other approaches. We provide suggestions to researchers as to which approach to use, under different combinations of data type/data frequency and data type/sample size.

Suggested Citation

  • Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
  • Handle: RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10429-9
    DOI: 10.1007/s10614-023-10429-9
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    as
    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Venus Khim-Sen Liew, 2004. "Which Lag Length Selection Criteria Should We Employ?," Economics Bulletin, AccessEcon, vol. 3(33), pages 1-9.
    3. T. Speed & Bin Yu, 1993. "Model selection and prediction: Normal regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 35-54, March.
    4. Shaofeng Zhang & Wei Xiong & Wancheng Ni & Xin Li, 2015. "Value of big data to finance: observations on an internet credit Service Company in China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    5. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    6. Nicoletta Batini & Edward Nelson, 2001. "The Lag from Monetary Policy Actions to Inflation: Friedman Revisited," International Finance, Wiley Blackwell, vol. 4(3), pages 381-400.
    7. Tomas Havranek & Marek Rusnak, 2013. "Transmission Lags of Monetary Policy: A Meta-Analysis," International Journal of Central Banking, International Journal of Central Banking, vol. 9(4), pages 39-76, December.
    8. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    9. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    10. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    11. Jesús Gonzalo & Jean‐Yves Pitarakis, 2002. "Lag length estimation in large dimensional systems," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 401-423, July.
    12. Kock, Anders Bredahl, 2016. "Consistent And Conservative Model Selection With The Adaptive Lasso In Stationary And Nonstationary Autoregressions," Econometric Theory, Cambridge University Press, vol. 32(1), pages 243-259, February.
    13. J. M. Culbertson, 1960. "Friedman on the Lag in Effect of Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 68(6), pages 617-617.
    14. Wenbo Wu & Jiaqi Chen & Liang Xu & Qingyun He & Michael L. Tindall, 2019. "A statistical learning approach for stock selection in the Chinese stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-18, December.
    15. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting realized volatility of bitcoin returns: tail events and asymmetric loss," The European Journal of Finance, Taylor & Francis Journals, vol. 27(16), pages 1626-1644, November.
    16. Babak Fazelabdolabadi, 2019. "A hybrid Bayesian-network proposition for forecasting the crude oil price," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-21, December.
    17. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    18. Duguay, Pierre, 1994. "Empirical evidence on the strength of the monetary transmission mechanism in Canada: An aggregate approach," Journal of Monetary Economics, Elsevier, vol. 33(1), pages 39-61, February.
    19. Swanson, Norman R & Zeng, Tian, 2001. "Choosing among Competing Econometric Forecasts: Regression-Based Forecast Combination Using Model Selection," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 425-440, September.
    20. Angeliki Skoura, 2019. "Detection of Lead-Lag Relationships Using Both Time Domain and Time-Frequency Domain; An Application to Wealth-To-Income Ratio," Economies, MDPI, vol. 7(2), pages 1-27, April.
    21. Baltagi, Badi H. & Bresson, Georges, 2011. "Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris," Journal of Urban Economics, Elsevier, vol. 69(1), pages 24-42, January.
    22. Francesco Audrino & Robert Fernholz & Roberto Ferretti, 2007. "A Forecasting Model for Stock Market Diversity," Annals of Finance, Springer, vol. 3(2), pages 213-240, March.
    23. A. Hatemi-J & R. S. Hacker, 2009. "Can the LR test be helpful in choosing the optimal lag order in the VAR model when information criteria suggest different lag orders?," Applied Economics, Taylor & Francis Journals, vol. 41(9), pages 1121-1125.
    24. Omer Ozcicek & W. DOUGLAS McMILLIN, 1999. "Lag length selection in vector autoregressive models: symmetric and asymmetric lags," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 517-524.
    25. Engle, Robert F., 1984. "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 13, pages 775-826, Elsevier.
    26. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    27. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
    28. Cagan, Phillip & Gandolfi, Arthur, 1969. "The Lag in Monetary Policy as Implied by the Time Pattern of Monetary Effects on Interest Rates," American Economic Review, American Economic Association, vol. 59(2), pages 277-284, May.
    29. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    30. Tanner, J. Ernest, 1979. "Are the lags in the effects of monetary policy variable?," Journal of Monetary Economics, Elsevier, vol. 5(1), pages 105-121, January.
    31. Fotiadis, Anestis & Polyzos, Stathis & Huan, Tzung-Cheng T.C., 2021. "The good, the bad and the ugly on COVID-19 tourism recovery," Annals of Tourism Research, Elsevier, vol. 87(C).
    32. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-179, April.
    33. Scott Hacker & Abdulnasser Hatemi‐J, 2012. "A bootstrap test for causality with endogenous lag length choice: theory and application in finance," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 39(2), pages 144-160, May.
    34. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
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    More about this item

    Keywords

    Random regression forest; Optimal lag; Lasso; Ridge regression; Bayesian model averaging;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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