IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v64y2024i1d10.1007_s10614-023-10429-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10429-9
    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/s10614-023-10429-9?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. Venus Khim-Sen Liew, 2004. "Which Lag Length Selection Criteria Should We Employ?," Economics Bulletin, AccessEcon, vol. 3(33), pages 1-9.
    2. 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.
    3. 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.
    4. 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.
    5. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    21. 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.
    22. 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).
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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).
    28. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
    34. 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.
    35. 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.
    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. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    2. Muhammad Atiq-ur-Rehman & Ismat Nasim & Muhammad Ayub & Ruqayya Ibraheem, 2022. "Transmission Lags of Monetary Policy: Probing into Pakistan's Untamed Inflation," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 4(2), pages 329-336, June.
    3. David Staines, 2023. "Stochastic Equilibrium the Lucas Critique and Keynesian Economics," Papers 2312.16214, arXiv.org, revised Jun 2024.
    4. George Hondroyiannis & Sophia Lazaretou, 2007. "Inflation persistence during periods of structural change: an assessment using Greek data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 34(5), pages 453-475, December.
    5. Michael T. Belongia & Peter N. Ireland, 2016. "Money and Output: Friedman and Schwartz Revisited," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(6), pages 1223-1266, September.
    6. Carlos Santos & Maria Alberta Oliveira, 2010. "Assessing French inflation persistence with impulse saturation break tests and automatic general-to-specific modelling," Applied Economics, Taylor & Francis Journals, vol. 42(12), pages 1577-1589.
    7. Yasin Kursat Onder & Mauricio Villamizar-Villegas, 2015. "Simultaneous Monetary Policies in the Context of the Trilemma: Evidence from the Central Bank of Turkey," Borradores de Economia 13203, Banco de la Republica.
    8. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    9. Yaser Abolghasemi & Stanko Dimitrov, 2021. "Determining the causality between U.S. presidential prediction markets and global financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4534-4556, July.
    10. Yasin Kursat Onder & Mauricio Villamizar-Villegas, 2018. "The Effects of Monetary and Exchange Rate Policy Shocks: Evidence from an Emerging Market Economy," International Journal of Central Banking, International Journal of Central Banking, vol. 14(1), pages 159-199, January.
    11. Sanchez-Fung, Jose R., 2002. "Inflation targeting and monetary analysis in Chile and Mexico," Economics Discussion Papers 2002-7, School of Economics, Kingston University London.
    12. Jones, Adam T. & Ogden, Richard E., 2017. "A day late and a dollar short: The effect of policy uncertainty on fed forecast errors," Economic Analysis and Policy, Elsevier, vol. 54(C), pages 112-122.
    13. ALAMI CHENTOUFI, Reda, 2024. "Penalized Convex Estimation in Dynamic Location-Scale models," MPRA Paper 123283, University Library of Munich, Germany.
    14. Jakub Fischer & Hana Lipovská & Daniel Němec, 2016. "Implementační zpoždění diskreční fiskální politiky [Implementation Lag of Discretionary Fiscal Policy]," Politická ekonomie, Prague University of Economics and Business, vol. 2016(3), pages 245-263.
    15. Alfred V. Guender & David R. Gillmore, 2010. "Practical Monetary Policies," International Finance, Wiley Blackwell, vol. 13(1), pages 25-53, March.
    16. Sánchez García, Javier & Galdeano Gómez, Emilio & Cruz Rambaud, Salvador, 2024. "Drivers of inflationary shocks and spillovers between Europe and the United States," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    17. Magdalena Szyszko & Aleksandra Rutkowska, 2019. "Macroeconomic Forecast Relevance in the Central Banks Decisions. The Case of European Economies," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(3), pages 257-275.
    18. KAMKOUM, Arnaud Cedric, 2023. "The Federal Reserve’s Response to the Global Financial Crisis and its Effects: An Interrupted Time-Series Analysis of the Impact of its Quantitative Easing Programs," Thesis Commons d7pvg, Center for Open Science.
    19. Nelson, Edward, 2017. "Reaffirming the Influence of Milton Friedman on U.K. Economic Policy," Working Papers 2017-01, University of Sydney, School of Economics, revised Feb 2017.
    20. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.

    More about this item

    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

    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:64:y:2024:i:1:d:10.1007_s10614-023-10429-9. 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.