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Yield Curve and Recession Forecasting in a Machine Learning Framework

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  • Gogas, Periklis

    (Democritus University of Thrace, Department of Economics)

  • Papadimitriou , Theophilos

    (Democritus University of Thrace, Department of Economics)

  • Matthaiou, Maria- Artemis

    (Democritus University of Thrace, Department of Economics)

  • Chrysanthidou, Efthymia

    (Democritus University of Thrace, Department of Economics)

Abstract

In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4 in conjunction with the real GDP for the same period, to create a model that can successfully forecast output fluctuations (inflation and output gaps) around its long-run trend. We focus our attention in correctly forecasting the instances of output gaps referred for the purposes of our analysis here as recessions. In this effort, we applied a Support Vector Machines (SVM) technique for classification. The results show that we can achieve an overall forecasting accuracy of 66,7% and a 100% accuracy in forecasting recessions.

Suggested Citation

  • Gogas, Periklis & Papadimitriou , Theophilos & Matthaiou, Maria- Artemis & Chrysanthidou, Efthymia, 2014. "Yield Curve and Recession Forecasting in a Machine Learning Framework," DUTH Research Papers in Economics 8-2014, Democritus University of Thrace, Department of Economics.
    • Handle: RePEc:ris:duthrp:2014_008
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    2. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    3. Yasmeen Idilbi-Bayaa & Mahmoud Qadan, 2021. "Forecasting Commodity Prices Using the Term Structure," JRFM, MDPI, vol. 14(12), pages 1-39, December.
    4. Lulin Xu & Zhongwu Li, 2021. "A New Appraisal Model of Second-Hand Housing Prices in China’s First-Tier Cities Based on Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 617-637, February.
    5. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    6. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
    7. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.
    8. Cepni, Oguzhan & Gupta, Rangan & Karahan, Cenk C. & Lucey, Brian, 2022. "Oil price shocks and yield curve dynamics in emerging markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 613-623.
    9. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2021. "Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach," Papers 2101.09394, arXiv.org, revised Jan 2022.
    10. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    11. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    12. Cheng-Feng Wu & Shian-Chang Huang & Chei-Chang Chiou & Tsangyao Chang & Yung-Chih Chen, 2022. "The Relationship Between Economic Growth and Electricity Consumption: Bootstrap ARDL Test with a Fourier Function and Machine Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1197-1220, December.
    13. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    14. Knut Lehre Seip & Dan Zhang, 2021. "The Yield Curve as a Leading Indicator: Accuracy and Timing of a Parsimonious Forecasting Model," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    15. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    16. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    17. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    18. Theodore Syriopoulos & Michael Tsatsaronis & Ioannis Karamanos, 2021. "Support Vector Machine Algorithms: An Application to Ship Price Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 55-87, January.
    19. David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
    20. Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.
    21. Andreas Psimopoulos, 2020. "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 18(1), pages 40-99.
    22. Jaehyuk Choi & Desheng Ge & Kyu Ho Kang & Sungbin Sohn, 2023. "Yield spread selection in predicting recession probabilities," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1772-1785, November.

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    More about this item

    Keywords

    Yield Curve; Recession Forecasting; SVM;
    All these keywords.

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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