IDEAS home Printed from https://ideas.repec.org/p/fip/fedgfe/2019-70.html
   My bibliography  Save this paper

Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning

Author

Abstract

This paper constructs a leading macroeconomic indicator from microeconomic data using recent machine learning techniques. Using tree-based methods, we estimate probabilities of default for publicly traded non-financial firms in the United States. We then use the cross-section of out-of-sample predicted default probabilities to construct a leading indicator of non-financial corporate health. The index predicts real economic outcomes such as GDP growth and employment up to eight quarters ahead. Impulse responses validate the interpretation of the index as a measure of financial stress.

Suggested Citation

  • Tyler Pike & Horacio Sapriza & Tom Zimmermann, 2019. "Bottom-up Leading Macroeconomic Indicators: An Application to Non-Financial Corporate Defaults using Machine Learning," Finance and Economics Discussion Series 2019-070, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2019-70
    DOI: 10.17016/FEDS.2019.070
    as

    Download full text from publisher

    File URL: https://www.federalreserve.gov/econres/feds/files/2019070pap.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.17016/FEDS.2019.070?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
    ---><---

    References listed on IDEAS

    as
    1. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    2. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    3. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    6. Alles, Lakshman A & Kling, John L, 1994. "Regularities in the Variation of Skewness in Asset Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 17(3), pages 427-438, Fall.
    7. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    8. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    9. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies 2007,18, Deutsche Bundesbank.
    10. Dahlquist, Magnus & Svensson, Lars E O, 1996. " Estimating the Term Structure of Interest Rates for Monetary Policy Analysis," Scandinavian Journal of Economics, Wiley Blackwell, vol. 98(2), pages 163-183, June.
    11. Lakshman A. Alles & John L. Kling, 1994. "Regularities In The Variation Of Skewness In Asset Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 17(3), pages 427-438, September.
    12. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    13. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    14. Sudheer Chava & Amiyatosh Purnanandam, 2010. "Is Default Risk Negatively Related to Stock Returns?," The Review of Financial Studies, Society for Financial Studies, vol. 23(6), pages 2523-2559, June.
    15. 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.
    16. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    17. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    18. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    19. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    20. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    21. Kay Giesecke & Francis A. Longstaff & Stephen Schaefer & Ilya Strebulaev, 2012. "Macroeconomic Effects of Corporate Default Crises: A Long-Term Perspective," NBER Working Papers 17854, National Bureau of Economic Research, Inc.
    22. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
    2. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).

    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. Howard Chan & Robert Faff & Paul Kofman, 2011. "Is default risk priced in Australian equity? Exploring the role of the business cycle," Australian Journal of Management, Australian School of Business, vol. 36(2), pages 217-246, August.
    2. Yinxia G. Nielsen , Caren, 2013. "Is Default Risk Priced in Equity Returns?," Knut Wicksell Working Paper Series 2013/2, Lund University, Knut Wicksell Centre for Financial Studies.
    3. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Pricing default risk: The good, the bad, and the anomaly," Journal of Financial Stability, Elsevier, vol. 26(C), pages 190-213.
    4. Pasaribu, Rowland Bismark Fernando, 2008. "Penggunaan Binary Logit untuk Prediksi Financial Distress Perusahaan Yang Tercatat Di Bursa Efek Jakarta [Financial Distress Prediction In Indonesian Stock Exchange]," MPRA Paper 36980, University Library of Munich, Germany.
    5. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    6. Kevin Aretz & Chris Florackis & Alexandros Kostakis, 2018. "Do Stock Returns Really Decrease with Default Risk? New International Evidence," Management Science, INFORMS, vol. 64(8), pages 3821-3842, August.
    7. Ampudia, Miguel & Busetto, Filippo & Fornari, Fabio, 2022. "Chronicle of a death foretold: does higher volatility anticipate corporate default?," Bank of England working papers 1001, Bank of England.
    8. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    9. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    10. Ye, Qing & Wu, Yuliang & Liu, Jia, 2019. "Institutional preferences, demand shocks and the distress anomaly," The British Accounting Review, Elsevier, vol. 51(1), pages 72-91.
    11. Andreou, Christoforos K. & Lambertides, Neophytos & Panayides, Photis M., 2021. "Distress risk anomaly and misvaluation," The British Accounting Review, Elsevier, vol. 53(5).
    12. Deniz Anginer & Çelim Yıldızhan, 2018. "Is There a Distress Risk Anomaly? Pricing of Systematic Default Risk in the Cross-section of Equity Returns [The risk-adjusted cost of financial distress]," Review of Finance, European Finance Association, vol. 22(2), pages 633-660.
    13. Miao, Hong & Ramchander, Sanjay & Ryan, Patricia & Wang, Tianyang, 2018. "Default prediction models: The role of forward-looking measures of returns and volatility," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 146-162.
    14. Yeh, Chung-Ying & Hsu, Junming & Wang, Kai-Li & Lin, Che-Hui, 2015. "Explaining the default risk anomaly by the two-beta model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 16-33.
    15. Idrees, Sahar & Qayyum, Abdul, 2018. "The Impact of Financial Distress Risk on Equity Returns: A Case Study of Non-Financial Firms of Pakistan Stock Exchange," MPRA Paper 85346, University Library of Munich, Germany.
    16. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    17. Guo, Hui & Jiang, Xiaowen, 2021. "Aggregate Distress Risk and Equity Returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    18. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    19. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    20. Alexander Hölzl & Sebastian Lobe, 2016. "Predicting above-median and below-median growth rates," Review of Managerial Science, Springer, vol. 10(1), pages 105-133, January.

    More about this item

    Keywords

    Corporate Default; Early Warning Indicators; Economic Activity; Machine Learning;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:fip:fedgfe:2019-70. 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: Ryan Wolfslayer ; Keisha Fournillier (email available below). General contact details of provider: https://edirc.repec.org/data/frbgvus.html .

    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.