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Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?

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  • Kian Tehranian

Abstract

Quantitative models are an important decision-making factor for policy makers and investors. Predicting an economic recession with high accuracy and reliability would be very beneficial for the society. This paper assesses machine learning technics to predict economic recessions in United States using market sentiment and economic indicators (seventy-five explanatory variables) from Jan 1986 - June 2022 on a monthly basis frequency. In order to solve the issue of missing time-series data points, Autoregressive Integrated Moving Average (ARIMA) method used to backcast explanatory variables. Analysis started with reduction in high dimensional dataset to only most important characters using Boruta algorithm, correlation matrix and solving multicollinearity issue. Afterwards, built various cross-validated models, both probability regression methods and machine learning technics, to predict recession binary outcome. The methods considered are Probit, Logit, Elastic Net, Random Forest, Gradient Boosting, and Neural Network. Lastly, discussed different models performance based on confusion matrix, accuracy and F1 score with potential reasons for their weakness and robustness.

Suggested Citation

  • Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.
    • Handle: RePEc:arx:papers:2308.16200
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    References listed on IDEAS

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    1. Fornari, Fabio & Lemke, Wolfgang, 2010. "Predicting recession probabilities with financial variables over multiple horizons," Working Paper Series 1255, European Central Bank.
    2. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    3. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    4. Periklis Gogas & Theophilos Papadimitriou & Maria Matthaiou & Efthymia Chrysanthidou, 2015. "Yield Curve and Recession Forecasting in a Machine Learning Framework," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 635-645, April.
    5. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    6. Rickard Nyman & Paul Ormerod, 2020. "Understanding the Great Recession Using Machine Learning Algorithms," Papers 2001.02115, arXiv.org.
    7. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    8. Arturo Estrella & Frederic S. Mishkin, 1996. "The yield curve as a predictor of U.S. recessions," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 2(Jun).
    9. Allan Layton & Anirvan Banerji, 2003. "What is a recession?: A reprise," Applied Economics, Taylor & Francis Journals, vol. 35(16), pages 1789-1797.
    10. Maria Kovacova & Tomas Kliestik, 2017. "Logit and Probit application for the prediction of bankruptcy in Slovak companies," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 12(4), pages 775-791, December.
    11. Sunil Sapra, 2005. ""A regression error specification test (RESET) for generalized linear models"," Economics Bulletin, AccessEcon, vol. 3(1), pages 1-6.
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    Cited by:

    1. Chanjuan Liu & Ruining Zhang & Yu Zhang & Enqiang Zhu, 2023. "A Formal Representation for Intelligent Decision-Making in Games," Mathematics, MDPI, vol. 11(22), pages 1-11, November.

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