IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v71y2020ics1057521920301514.html
   My bibliography  Save this article

Machine learning as an early warning system to predict financial crisis

Author

Listed:
  • Samitas, Aristeidis
  • Kampouris, Elias
  • Kenourgios, Dimitris

Abstract

This paper studies on “Early Warning Systems” (EWS) by investigating possible contagion risks, based on structured financial networks. Early warning indicators improve standard crisis prediction models performance. Using network analysis and machine learning algorithms we find evidence of contagion risk on the dates where we observe significant increase in correlations and centralities. The effectiveness of machine learning reached 98.8%, making the predictions extremely accurate. The model provides significant information to policymakers and investors about employing the financial network as a useful tool to improve portfolio selection by targeting assets based on centrality.

Suggested Citation

  • Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:finana:v:71:y:2020:i:c:s1057521920301514
    DOI: 10.1016/j.irfa.2020.101507
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521920301514
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2020.101507?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Mei Li & Frank Milne & Junfeng Qiu, 2016. "Uncertainty in an Interconnected Financial System, Contagion, and Market Freezes," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(6), pages 1135-1168, September.
    3. Chuluun, Tuugi & Prevost, Andrew & Upadhyay, Arun, 2017. "Firm network structure and innovation," Journal of Corporate Finance, Elsevier, vol. 44(C), pages 193-214.
    4. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. Kantar, Ersin & Deviren, Bayram & Keskin, Mustafa, 2011. "Hierarchical structure of Turkey’s foreign trade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3454-3476.
    7. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    8. Gilmore, Claire G. & Lucey, Brian M. & Boscia, Marian W., 2010. "Comovements in government bond markets: A minimum spanning tree analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4875-4886.
    9. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
    10. Baele, Lieven, 2005. "Volatility Spillover Effects in European Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(2), pages 373-401, June.
    11. Jeffrey D. Sachs & Aaron Tornell & Andrés Velasco, 1996. "Financial Crises in Emerging Markets: The Lessons from 1995," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(1), pages 147-216.
    12. Gilmore, Claire G. & Lucey, Brian M. & Boscia, Marian, 2008. "An ever-closer union? Examining the evolution of linkages of European equity markets via minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(25), pages 6319-6329.
    13. Eryiğit, Mehmet & Eryiğit, Resul, 2009. "Network structure of cross-correlations among the world market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3551-3562.
    14. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    15. Hausenblas, Václav & Kubicová, Ivana & Lešanovská, Jitka, 2015. "Contagion risk in the Czech financial system: A network analysis and simulation approach," Economic Systems, Elsevier, vol. 39(1), pages 156-180.
    16. Zhang, Xiaobo & Wang, Jianzhou & Gao, Yuyang, 2019. "A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM," Energy Economics, Elsevier, vol. 81(C), pages 899-913.
    17. Coelho, R. & Hutzler, S. & Repetowicz, P. & Richmond, P., 2007. "Sector analysis for a FTSE portfolio of stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 615-626.
    18. Bhatti, M. Ishaq & Nguyen, Cuong C., 2012. "Diversification evidence from international equity markets using extreme values and stochastic copulas," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(3), pages 622-646.
    19. Corsetti, Giancarlo & Pericoli, Marcello & Sbracia, Massimo, 2005. "'Some contagion, some interdependence': More pitfalls in tests of financial contagion," Journal of International Money and Finance, Elsevier, vol. 24(8), pages 1177-1199, December.
    20. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    21. Geert Bekaert & Campbell R. Harvey & Angela Ng, 2005. "Market Integration and Contagion," The Journal of Business, University of Chicago Press, vol. 78(1), pages 39-70, January.
    22. Ana Babus, 2016. "The formation of financial networks," RAND Journal of Economics, RAND Corporation, vol. 47(2), pages 239-272, May.
    23. Haishu Qiao & Yue Xia & Ying Li, 2016. "Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    24. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    25. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    26. Haishu Qiao & Ying Li & Yue Xia, 2015. "Analysis of Linkage Effects among Currency Networks Using REER Data," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-9, May.
    27. Fabrizio Durante & Piotr Jaworski, 2010. "Spatial contagion between financial markets: a copula‐based approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(5), pages 551-564, September.
    28. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    29. Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung & Jeong, Hawoong & Kim, Seunghwan, 2009. "Topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 900-906.
    30. Ulusoy, Tolga & Keskin, Mustafa & Shirvani, Ayoub & Deviren, Bayram & Kantar, Ersin & Çaǧrı Dönmez, Cem, 2012. "Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5121-5131.
    31. Coelho, Ricardo & Gilmore, Claire G. & Lucey, Brian & Richmond, Peter & Hutzler, Stefan, 2007. "The evolution of interdependence in world equity markets—Evidence from minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 455-466.
    32. Samitas, Aristeidis & Tsakalos, Ioannis, 2013. "How can a small country affect the European economy? The Greek contagion phenomenon," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 25(C), pages 18-32.
    33. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    34. Dornbusch, Rudiger & Park, Yung Chul & Claessens, Stijn, 2000. "Contagion: Understanding How It Spreads," The World Bank Research Observer, World Bank, vol. 15(2), pages 177-197, August.
    35. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    36. Gogas, Periklis & Papadimitriou, Theophilos & Agrapetidou, Anna, 2018. "Forecasting bank failures and stress testing: A machine learning approach," International Journal of Forecasting, Elsevier, vol. 34(3), pages 440-455.
    37. Chen, Kun & Luo, Peng & Sun, Bianxia & Wang, Huaiqing, 2015. "Which stocks are profitable? A network method to investigate the effects of network structure on stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 224-235.
    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. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    2. Cui, Jinxin & Maghyereh, Aktham, 2024. "Unveiling interconnectedness: Exploring higher-order moments among energy, precious metals, industrial metals, and agricultural commodities in the context of geopolitical risks and systemic stress," Journal of Commodity Markets, Elsevier, vol. 33(C).
    3. Noori, Mohammad & Hitaj, Asmerilda, 2023. "Dissecting hedge funds' strategies," International Review of Financial Analysis, Elsevier, vol. 85(C).
    4. Samitas, Aristeidis & Kampouris, Elias & Polyzos, Stathis, 2022. "Covid-19 pandemic and spillover effects in stock markets: A financial network approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
    5. Wang, Peiwan & Zong, Lu, 2023. "Does machine learning help private sectors to alarm crises? Evidence from China’s currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    6. Shu-Ling Lin & Xiao Jin, 2023. "Does ESG Predict Systemic Banking Crises? A Computational Economics Model of Early Warning Systems with Interpretable Multi-Variable LSTM based on Mixture Attention," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    7. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Business model contributions to bank profit performance: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. 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).
    9. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.
    10. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    11. Alonso-Alvarez, Irma & Molina, Luis, 2023. "How to foresee crises? A new synthetic index of vulnerabilities for emerging economies," Economic Modelling, Elsevier, vol. 125(C).
    12. Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
    13. Baur, Dirk G. & Hoang, Lai T. & Hossain, Md Zakir, 2022. "Is Bitcoin a hedge? How extreme volatility can destroy the hedge property," Finance Research Letters, Elsevier, vol. 47(PB).
    14. An, Hui & Wang, Hao & Delpachitra, Sarath & Cottrell, Simon & Yu, Xiao, 2022. "Early warning system for risk of external liquidity shock in BRICS countries," Emerging Markets Review, Elsevier, vol. 51(PA).
    15. Yuan, Ying & Wang, Haiying & Jin, Xiu, 2022. "Pandemic-driven financial contagion and investor behavior: Evidence from the COVID-19," International Review of Financial Analysis, Elsevier, vol. 83(C).
    16. Elena G. Shershneva, 2024. "CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks," Journal of New Economy, Ural State University of Economics, vol. 25(2), pages 130-152, July.
    17. Dimitrios Kenourgios & Spyros Papathanasiou & Anastasia Christina Bampili, 2022. "On the predictive power of CAPE or Shiller’s PE ratio: the case of the Greek stock market," Operational Research, Springer, vol. 22(4), pages 3747-3766, September.
    18. Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022. "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers 2204.12657, arXiv.org, revised Oct 2022.
    19. Semen Budennyy & Alexey Kazakov & Elizaveta Kovtun & Leonid Zhukov, 2022. "New drugs and stock market: how to predict pharma market reaction to clinical trial announcements," Papers 2208.07248, arXiv.org, revised Aug 2022.
    20. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).
    21. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).
    22. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    23. Baker, H. Kent & Kumar, Satish & Goyal, Kirti & Sharma, Anuj, 2021. "International review of financial analysis: A retrospective evaluation between 1992 and 2020," International Review of Financial Analysis, Elsevier, vol. 78(C).

    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. Samitas, Aristeidis & Kampouris, Elias & Polyzos, Stathis, 2022. "Covid-19 pandemic and spillover effects in stock markets: A financial network approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
    2. Aristeidis Samitas & Elias Kampouris & Zaghum Umar, 2022. "Financial contagion in real economy: The key role of policy uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1633-1682, April.
    3. Haishu Qiao & Yue Xia & Ying Li, 2016. "Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-25, June.
    4. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    5. Aristeidis, Samitas & Elias, Kampouris, 2018. "Empirical analysis of market reactions to the UK’s referendum results – How strong will Brexit be?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 263-286.
    6. Maria Kasch & Massimiliano Caporin, 2013. "Volatility Threshold Dynamic Conditional Correlations: An International Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 11(4), pages 706-742, September.
    7. Kenourgios, Dimitris & Naifar, Nader & Dimitriou, Dimitrios, 2016. "Islamic financial markets and global crises: Contagion or decoupling?," Economic Modelling, Elsevier, vol. 57(C), pages 36-46.
    8. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    9. Deviren, Seyma Akkaya & Deviren, Bayram, 2016. "The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 429-439.
    10. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    11. Emerson Fernandes Marcal & Pedro Valls Pereira & Diogenes Manoel Leiva Martin & Wilson Toshiro Nakamura, 2011. "Evaluation of contagion or interdependence in the financial crises of Asia and Latin America, considering the macroeconomic fundamentals," Applied Economics, Taylor & Francis Journals, vol. 43(19), pages 2365-2379.
    12. Marçal, Emerson Fernandes & Pereira, Pedro L. Valls, 2008. "Testing the Hypothesis of Contagion Using Multivariate Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(2), November.
    13. Martin Hoesli & Kustrim Reka, 2013. "Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 47(1), pages 1-35, July.
    14. Bartram, Sohnke M. & Taylor, Stephen J. & Wang, Yaw-Huei, 2007. "The Euro and European financial market dependence," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1461-1481, May.
    15. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    16. Tiago Trancoso, 2013. "Global macroeconomic interdependence: a minimum spanning tree approach," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 5(1), pages 179-189, June.
    17. Trancoso, Tiago, 2014. "Emerging markets in the global economic network: Real(ly) decoupling?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 499-510.
    18. Marçal, Emerson F. & Valls Pereira, Pedro L., 2008. "Testando A Hipótese De Contágio A Partir De Modelos Multivariados De Volatilidade [Testing the contagion hypotheses using multivariate volatility models]," MPRA Paper 10356, University Library of Munich, Germany.
    19. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
    20. Rotta, Pedro Nielsen & Pereira, Pedro L. Valls, 2013. "Analysis of contagion from the constant conditional correlation model with Markov regime switching," Textos para discussão 340, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

    More about this item

    Keywords

    Financial crisis; Social network analysis; Contagion; Forecasting; Machine learning;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:eee:finana:v:71:y:2020:i:c:s1057521920301514. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.