Skip to main content

Neural Network Model for Classifying the Economic Recession and Construction of Financial Stress Index

  • Conference paper
  • First Online:
Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

  • 1391 Accesses

Abstract

In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are created. A detailed experiment is presented and demonstrates that the neural network models with proper regularization and dropout achieve 98% accuracy in the training set, 97% accuracy in validation set and 100% accuracy in test accuracy. The financial stress indices outperform other existing financial stress indices in many scenes and can accurately locate crisis events even the most recent 2018 US Bear Market. With these models and new indices, contraction can be detected before NBER’s announcement and action could be taken as early as the situation get worse.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Brave, S.A., Kelly, D.L., et al.: Introducing the Chicago fed’s new adjusted national financial conditions index. Chicago Fed Letter (386) (2017)

    Google Scholar 

  2. Caccioli, F., Barucca, P., Kobayashi, T.: Network models of financial systemic risk: a review. J. Comput. Soc. Sci. 1(1), 81–114 (2018)

    Article  Google Scholar 

  3. Cambón, M.I., Estévez, L.: A Spanish financial market stress index (FMSI). Span. Rev. Financ. Econ. 14(1), 23–41 (2016)

    Article  Google Scholar 

  4. Carlson, J.B., Pelz, E.A., et al.: A retrospective on the stock market in 2000. Economic Commentary, 15 January 2001 (2001)

    Google Scholar 

  5. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  6. Chen, M.Y.: Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Syst. Appl. 38(9), 11261–11272 (2011)

    Article  Google Scholar 

  7. Delen, D., Kuzey, C., Uyar, A.: Measuring firm performance using financial ratios: a decision tree approach. Expert Syst. Appl. 40(10), 3970–3983 (2013)

    Article  Google Scholar 

  8. Dueker, M.J., et al.: Regime-dependent recession forecasts and the 2001 recession. Rev.-Fed. Reserv. Bank St. Louis 84(6), 29–36 (2002)

    Google Scholar 

  9. National Bureau of Economic Research: Us business cycle expansions and contractions (2010). https://users.nber.org/cycles/cyclesmain.html

  10. Elsalamony, H.A., Elsayad, A.M.: Bank direct marketing based on neural network and C5.0 models. Int. J. Eng. Adv. Technol. (IJEAT) 2(6), 392–400 (2013)

    Google Scholar 

  11. Gepp, A., Kumar, K.: Predicting financial distress: a comparison of survival analysis and decision tree techniques. Procedia Comput. Sci. 54, 396–404 (2015)

    Article  Google Scholar 

  12. Hakkio, C.S., Keeton, W.R., et al.: Financial stress: what is it, how can it be measured, and why does it matter? Econ. Rev. 94(2), 5–50 (2009)

    Google Scholar 

  13. Khomo, M.M., Aziakpono, M.J.: Forecasting recession in South Africa: a comparison of the yield curve and other economic indicators. S. Afr. J. Econ. 75(2), 194–212 (2007)

    Article  Google Scholar 

  14. Lam, M.: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis. Support Syst. 37(4), 567–581 (2004)

    Article  Google Scholar 

  15. Madhani, P.M.: Rebalancing fixed and variable pay in a sales organization: a business cycle perspective. Compens. Benefits Rev. 42(3), 179–189 (2010)

    Article  Google Scholar 

  16. Nyberg, H.: Dynamic probit models and financial variables in recession forecasting. J. Forecast. 29(1–2), 215–230 (2010)

    Article  MathSciNet  Google Scholar 

  17. Federal Reserve Bank of St. Louis: St. Louis Fed Financial Stress Index [STLFSI]. https://fred.stlouisfed.org/series/STLFSI. Accessed 13 Dec 2018

  18. Yasin, H., Arifin, A.W.B., Warsito, B.: Classification of company performance using weighted probabilistic neural network. J. Phys: Conf. Ser. 1025, 012095 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lujia Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shen, L., Du, T., Ji, S. (2019). Neural Network Model for Classifying the Economic Recession and Construction of Financial Stress Index. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_44

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0121-0_44

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics