IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v51y2020ics1062940818304108.html
   My bibliography  Save this article

Predicting stock market crises using daily stock market valuation and investor sentiment indicators

Author

Listed:
  • Fu, Junhui
  • Zhou, Qingling
  • Liu, Yufang
  • Wu, Xiang

Abstract

The purpose of this paper is to develop a daily early warning system for stock market crises using daily stock market valuation and investor sentiment indicators. To achieve this goal, we use principal components analysis to propose a comprehensive index of daily market indicators that reflects stock market valuation and investor sentiment. Based on the comprehensive index, we employ a logit model with Ensemble Empirical Mode Decomposition to develop a daily early warning system for stock market crises. Finally, we apply the proposed system to the early warning for stock market crises in China. The in-sample forecasting results show that investor sentiment and the forecast horizon by Ensemble Empirical Mode Decomposition improve the forecasting performance of conventional early warning systems. The out-of-sample forecasting results indicate that the proposed warning system still has a good performance.

Suggested Citation

  • Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:ecofin:v:51:y:2020:i:c:s1062940818304108
    DOI: 10.1016/j.najef.2019.01.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940818304108
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.najef.2019.01.002?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. Peng, Duan & Bajona, Claustre, 2008. "China's vulnerability to currency crisis: A KLR signals approach," China Economic Review, Elsevier, vol. 19(2), pages 138-151, June.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Kumar, Mohan & Moorthy, Uma & Perraudin, William, 2003. "Predicting emerging market currency crashes," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 427-454, September.
    4. Coudert, Virginie & Gex, Mathieu, 2008. "Does risk aversion drive financial crises? Testing the predictive power of empirical indicators," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 167-184, March.
    5. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
    6. Ciarlone, Alessio & Trebeschi, Giorgio, 2005. "Designing an early warning system for debt crises," Emerging Markets Review, Elsevier, vol. 6(4), pages 376-395, December.
    7. Barrell, Ray & Davis, E. Philip & Karim, Dilruba & Liadze, Iana, 2010. "Bank regulation, property prices and early warning systems for banking crises in OECD countries," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2255-2264, September.
    8. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
    9. Oet, Mikhail V. & Bianco, Timothy & Gramlich, Dieter & Ong, Stephen J., 2013. "SAFE: An early warning system for systemic banking risk," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4510-4533.
    10. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone, 2014. "Early warning systems and systemic banking crises in low income countries: A multinomial logit approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 258-269.
    11. 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.
    12. Kimmel, Randall K. & Thornton, John H. & Bennett, Sara E., 2016. "Can statistics-based early warning systems detect problem banks before markets?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 190-216.
    13. Candelon, Bertrand & Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2014. "Currency crisis early warning systems: Why they should be dynamic," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1016-1029.
    14. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    15. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    16. Babecký, Jan & Havránek, Tomáš & Matějů, Jakub & Rusnák, Marek & Šmídková, Kateřina & Vašíček, Bořek, 2014. "Banking, debt, and currency crises in developed countries: Stylized facts and early warning indicators," Journal of Financial Stability, Elsevier, vol. 15(C), pages 1-17.
    17. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    18. Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
    19. Manasse, Paolo & Roubini, Nouriel, 2009. ""Rules of thumb" for sovereign debt crises," Journal of International Economics, Elsevier, vol. 78(2), pages 192-205, July.
    20. Berg, Andrew & Pattillo, Catherine, 1999. "Predicting currency crises:: The indicators approach and an alternative," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 561-586, August.
    21. Virtanen, Timo & Tölö, Eero & Virén, Matti & Taipalus, Katja, 2018. "Can bubble theory foresee banking crises?," Journal of Financial Stability, Elsevier, vol. 36(C), pages 66-81.
    22. Geršl, Adam & Jašová, Martina, 2018. "Credit-based early warning indicators of banking crises in emerging markets," Economic Systems, Elsevier, vol. 42(1), pages 18-31.
    23. Li, Wei-Xuan & Chen, Clara Chia-Sheng & French, Joseph J., 2015. "Toward an early warning system of financial crises: What can index futures and options tell us?," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 87-99.
    24. Hirose, Takehide & Kato, Hideaki Kiyoshi & Bremer, Marc, 2009. "Can margin traders predict future stock returns in Japan?," Pacific-Basin Finance Journal, Elsevier, vol. 17(1), pages 41-57, January.
    25. Eichler, Stefan & Karmann, Alexander & Maltritz, Dominik, 2009. "The ADR shadow exchange rate as an early warning indicator for currency crises," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 1983-1995, November.
    26. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    27. Helmut Herwartz & Konstantin A. Kholodilin, 2014. "In‐Sample and Out‐of‐Sample Prediction of stock Market Bubbles: Cross‐Sectional Evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 15-31, January.
    28. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    29. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone & Kapetanios, George, 2016. "Comparing logit-based early warning systems: Does the duration of systemic banking crises matter?," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 104-116.
    30. V. Coudert & M. Gex, 2008. "Does risk aversion drive financial crises? Testing the predictive power of empirical indicators," Post-Print halshs-00321667, HAL.
    31. Nitschka, Thomas, 2011. "About the soundness of the US-cay indicator for predicting international banking crises," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 237-256.
    32. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    33. Goldfajn, Ilan & Valdes, Rodrigo O., 1998. "Are currency crises predictable?," European Economic Review, Elsevier, vol. 42(3-5), pages 873-885, May.
    34. Fuertes, Ana-Maria & Kalotychou, Elena, 2006. "Early warning systems for sovereign debt crises: The role of heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1420-1441, November.
    35. Lang, Michael & Schmidt, Paul G., 2016. "The early warnings of banking crises: Interaction of broad liquidity and demand deposits," Journal of International Money and Finance, Elsevier, vol. 61(C), pages 1-29.
    36. Asli Demirgüç-Kunt & Enrica Detragiache, 1998. "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 81-109, March.
    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. Dai, Zhifeng & Zhu, Junxin & Zhang, Xinhua, 2022. "Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment," Energy Economics, Elsevier, vol. 114(C).
    2. Adnan Abo Al Haija & Rahma Lahyani, 2023. "Dynamic interactions of actual stock returns with forecasted stock returns and investors’ risk aversion: empirical evidence interplaying the impact of Covid-19 pandemic," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 1129-1149, October.
    3. Zheng, Chengli & Su, Kuangxi & Yao, Yinhong, 2021. "Hedging futures performance with denoising and noise-assisted strategies," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Krzysztof Biegun & Jacek Karwowski & Piotr Luty, 2021. "How Effective is Macroeconomic Imbalance Procedure (MIP) in Predicting Negative Macroeconomic Phenomena?," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 822-837.
    5. Ren, Tingting & Li, Shaofang & Zhang, Siying, 2024. "Stock market extreme risk prediction based on machine learning: Evidence from the American market," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    6. Yang, Qu & Yu, Yuanyuan & Dai, Dongsheng & He, Qian & Lin, Yu, 2024. "Can hybrid model improve the forecasting performance of stock price index amid COVID-19? Contextual evidence from the MEEMD-LSTM-MLP approach," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    7. Yuan Li & Yu Zhang, 2021. "Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies," SAGE Open, , vol. 11(2), pages 21582440211, June.

    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. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    2. Allaj, Erindi & Sanfelici, Simona, 2023. "Early Warning Systems for identifying financial instability," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1777-1803.
    3. 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).
    4. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    5. Mirjana Jemović & Srđan Marinković, 2021. "Determinants of financial crises—An early warning system based on panel logit regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 103-117, January.
    6. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
    7. Kehinde Damilola Ilesanmi & Devi Datt Tewari, 2021. "An Early Warning Signal (EWS) Model for Predicting Financial Crisis in Emerging African Economies," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(1), pages 101-110, January.
    8. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
    9. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    10. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    11. Filippopoulou, Chryssanthi & Galariotis, Emilios & Spyrou, Spyros, 2020. "An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach," Journal of Economic Behavior & Organization, Elsevier, vol. 172(C), pages 344-363.
    12. Peiwan Wang & Lu Zong & Ye Ma, 2019. "An Integrated Early Warning System for Stock Market Turbulence," Papers 1911.12596, arXiv.org.
    13. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    14. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    15. Medina Moral, Eva & Salvador Perucha, David, 2018. "Medición de la vulnerabilidad monetaria en el área latinoamericana bajo un enfoque de señales ?móviles?/Measurement of Monetary Vulnerability in the Latin American Area using a," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 603-634, Mayo.
    16. Sondermann, David & Zorell, Nico, 2019. "A macroeconomic vulnerability model for the euro area," Working Paper Series 2306, European Central Bank.
    17. Li, Wei-Xuan & Chen, Clara Chia-Sheng & French, Joseph J., 2015. "Toward an early warning system of financial crises: What can index futures and options tell us?," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 87-99.
    18. Chung‐Hua Shen & Hsing‐Hua Hsu, 2022. "The determinants of Asian banking crises—Application of the panel threshold logit model," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 248-277, March.
    19. Mikkel Hermansen & Oliver Röhn, 2017. "Economic resilience: The usefulness of early warning indicators in OECD countries," OECD Journal: Economic Studies, OECD Publishing, vol. 2016(1), pages 9-35.
    20. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.

    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:ecofin:v:51:y:2020:i:c:s1062940818304108. 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/620163 .

    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.