IDEAS home Printed from https://ideas.repec.org/p/mad/wpaper/2024-270.html
   My bibliography  Save this paper

Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models

Author

Listed:
  • Mahalakshmi Manian

    (Research Scholar)

  • Parthajit Kayal

    ((corresponding author), Assistant Professor Madras School of Economics, Chennai)

Abstract

This research investigates the phenomenon of economic or financial bubbles within the Indian stock market context, characterized by pronounced asset price inflation exceeding the intrinsic worth of the underlying assets. Leveraging data from the NIFTY 500 index spanning the period 2003 to 2021, the study utilizes the Phillips, Shi, and Yu (PSY) method (Phillips et. al., 2015b), which employs a right-tailed unit root test, to discern the presence of financial bubbles. Subsequently, machine learning algorithms are employed to predict real-time occurrences of such bubbles. Analysis reveals the manifestation of financial bubbles within the Indian stock market notably in the years 2007 and 2017. Moreover, empirical evidence underscores the superior predictive efficacy of Artificial Neural Networks, Random Forest, and Gradient Boosting algorithms vis-à-vis conventional statistical methodologies in forecasting financial bubble occurrences within the Indian stock market. Policymakers should use advanced machine learning techniques for real-time financial bubble detection to improve regulation and mitigate market risks.

Suggested Citation

  • Mahalakshmi Manian & Parthajit Kayal, 2024. "Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models," Working Papers 2024-270, Madras School of Economics,Chennai,India.
    • Handle: RePEc:mad:wpaper:2024-270
    as

    Download full text from publisher

    File URL: https://www.mse.ac.in/wp-content/uploads/2024/11/WORKING-PAPER-270.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    2. Ye ChenCapital & Peter C B Phillips & Shuping Shi, 2023. "Common Bubble Detection in Large Dimensional Financial Systems," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 989-1063.
    3. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    4. James Galbraith & Sara Hsu & Wenjie Zhang, 2009. "Beijing Bubble, Beijing Bust: Inequality, Trade, and Capital Inflow into China," Journal of Current Chinese Affairs - China aktuell, Institute of Asian Studies, GIGA German Institute of Global and Area Studies, Hamburg, vol. 38(2), pages 3-26.
    5. Robert A. Jarrow & Simon S. Kwok, 2021. "Inferring financial bubbles from option data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 1013-1046, November.
    6. Öğüt, Hulisi & Doğanay, M. Mete & Ceylan, Nildağ Başak & Aktaş, Ramazan, 2012. "Prediction of bank financial strength ratings: The case of Turkey," Economic Modelling, Elsevier, vol. 29(3), pages 632-640.
    7. Kim Long Tran & Hoang Anh Le & Cap Phu Lieu & Duc Trung Nguyen, 2023. "Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam," IJFS, MDPI, vol. 11(4), pages 1-18, November.
    8. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.
    Full references (including those not matched with items on IDEAS)

    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. Felix Chan & Les Oxley, 2023. "A pulse check on recent developments in time series econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 3-6, February.
    2. Shuping Shi & Peter C. B. Phillips, 2022. "Econometric Analysis of Asset Price Bubbles," Cowles Foundation Discussion Papers 2331, Cowles Foundation for Research in Economics, Yale University.
    3. H. Peter Boswijk & Jun Yu & Yang Zu, 2024. "Testing for an Explosive Bubble using High-Frequency Volatility," Working Papers 202402, University of Macau, Faculty of Business Administration.
    4. Christie Smith & Aaron Kumar, 2018. "Crypto‐Currencies – An Introduction To Not‐So‐Funny Moneys," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1531-1559, December.
    5. Caspi, Itamar & Graham, Meital, 2018. "Testing for bubbles in stock markets with irregular dividend distribution," Finance Research Letters, Elsevier, vol. 26(C), pages 89-94.
    6. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
    7. In Choi & Sanghyun Jung, 2021. "Cross-sectional quasi-maximum likelihood and bias-corrected pooled least squares estimators for short dynamic panels," Empirical Economics, Springer, vol. 60(1), pages 177-203, January.
    8. Paulo M.M. Rodrigues & Rita Fradique Lourenço, 2015. "House prices: bubbles, exuberance or something else? Evidence from euro area countries," Working Papers w201517, Banco de Portugal, Economics and Research Department.
    9. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    10. Francisco Blasques & Siem Jan Koopman & Gabriele Mingoli, 2023. "Observation-Driven filters for Time- Series with Stochastic Trends and Mixed Causal Non-Causal Dynamics," Tinbergen Institute Discussion Papers 23-065/III, Tinbergen Institute, revised 01 Mar 2024.
    11. Wegener, Christoph & Kruse, Robinson & Basse, Tobias, 2019. "The walking debt crisis," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 382-402.
    12. Escobari, Diego & Garcia, Sergio & Mellado, Cristhian, 2017. "Identifying bubbles in Latin American equity markets: Phillips-Perron-based tests and linkages," Emerging Markets Review, Elsevier, vol. 33(C), pages 90-101.
    13. Umar, Muhammad & Su, Chi-Wei & Rizvi, Syed Kumail Abbas & Lobonţ, Oana-Ramona, 2021. "Driven by fundamentals or exploded by emotions: Detecting bubbles in oil prices," Energy, Elsevier, vol. 231(C).
    14. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 14, pages 93-122, Emerald Group Publishing Limited.
    15. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    16. Lajos Horvath & Lorenzo Trapani, 2021. "Changepoint detection in random coefficient autoregressive models," Papers 2104.13440, arXiv.org.
    17. Beckers, Benjamin & Bernoth, Kerstin, 2016. "Monetary Policy and Asset Mispricing," VfS Annual Conference 2016 (Augsburg): Demographic Change 145684, Verein für Socialpolitik / German Economic Association.
    18. In Choi, 2019. "Unit Root Tests for Dependent Micropanels," The Japanese Economic Review, Springer, vol. 70(2), pages 145-167, June.
    19. KIRKPINAR, Aysegul & ERER, Elif & ERER, Deniz, 2019. "Is There A Rational Bubble In Bist 100 And Sector Indices?," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 23(3), pages 21-33, September.
    20. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).

    More about this item

    Keywords

    Financial Bubbles; Machine Learning; K-nearest Neighbour; Random Forest Classifier; Artificial Neural Network; Naïve Bayes;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:mad:wpaper:2024-270. 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: Geetha G (email available below). General contact details of provider: https://edirc.repec.org/data/mseacin.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.