IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v38y2023ics2214635023000187.html
   My bibliography  Save this article

Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries

Author

Listed:
  • van Eyden, Reneé
  • Gupta, Rangan
  • Nielsen, Joshua
  • Bouri, Elie

Abstract

Firstly, we use the log-periodic power law singularity multi-scale confidence indicator (LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock market indices of the G7 countries. Secondly, we apply heterogeneous coefficients panel data-based regressions to analyse the impact of investor sentiment, proxied by business and consumer confidence indicators, on the indicators of bubbles of the G7. Controlling for the impacts of output growth, inflation, monetary policy, stock market volatility, and growth in trading volumes, we find that investor sentiment increases the positive and reduces the negative LPPLS-CIs, primarily at the medium- and long-term scales for the G7, considered together, with the result being driven by at least five of the seven countries. Our results have important implications for both investors and policymakers, as the collapse (improvement) of investor sentiment can lead to a crash (recovery) in a bull (bear) market.

Suggested Citation

  • van Eyden, Reneé & Gupta, Rangan & Nielsen, Joshua & Bouri, Elie, 2023. "Investor sentiment and multi-scale positive and negative stock market bubbles in a panel of G7 countries," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:beexfi:v:38:y:2023:i:c:s2214635023000187
    DOI: 10.1016/j.jbef.2023.100804
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2214635023000187
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbef.2023.100804?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    2. Oguzhan Cepni & Rangan Gupta & Qiang Ji, 2023. "Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 365-381, July.
    3. Deven Bathia & Don Bredin & Dirk Nitzsche, 2016. "International Sentiment Spillovers in Equity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 332-359, October.
    4. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    5. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    6. Demirer, Riza & Gabauer, David & Gupta, Rangan & Ji, Qiang, 2021. "Monetary policy and speculative spillovers in financial markets," Research in International Business and Finance, Elsevier, vol. 56(C).
    7. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    8. Plakandaras, Vasilios & Tiwari, Aviral Kumar & Gupta, Rangan & Ji, Qiang, 2020. "Spillover of sentiment in the European Union: Evidence from time- and frequency-domains," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 105-130.
    9. Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022. "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, vol. 231(2), pages 387-409.
    10. Cepni, Oguzhan & Gupta, Rangan, 2021. "Time-varying impact of monetary policy shocks on US stock returns: The role of investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    11. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    12. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    13. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    14. Wei-Fong Pan, 2020. "Does Investor Sentiment Drive Stock Market Bubbles? Beware of Excessive Optimism!," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 27-41, January.
    15. Balcilar, Mehmet & Gupta, Rangan & Jooste, Charl & Wohar, Mark E., 2016. "Periodically collapsing bubbles in the South African stock market," Research in International Business and Finance, Elsevier, vol. 38(C), pages 191-201.
    16. Krippner, Leo, 2013. "Measuring the stance of monetary policy in zero lower bound environments," Economics Letters, Elsevier, vol. 118(1), pages 135-138.
    17. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    18. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    19. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    20. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    21. Zhang, Qunzhi & Sornette, Didier & Balcilar, Mehmet & Gupta, Rangan & Ozdemir, Zeynel Abidin & Yetkiner, Hakan, 2016. "LPPLS bubble indicators over two centuries of the S&P 500 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 126-139.
    22. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    23. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    24. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    25. Lee, Chi-Chuan & Lee, Chien-Chiang, 2023. "International spillovers of U.S. monetary uncertainty and equity market volatility to China’s stock markets," Journal of Asian Economics, Elsevier, vol. 84(C).
    26. Aviral Kumar Tiwari & Deven Bathia & Elie Bouri & Rangan Gupta, 2021. "Investor Sentiment Connectedness: Evidence From Linear And Nonlinear Causality Approaches," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-29, December.
    27. Deven Bathia & Don Bredin, 2013. "An examination of investor sentiment effect on G7 stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 19(9), pages 909-937, October.
    28. Didier SORNETTE & Guilherme DEMOS & Zhang QUN & Peter CAUWELS & Vladimir FILIMONOV & Qunzhi ZHANG, 2015. "Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash," Swiss Finance Institute Research Paper Series 15-32, Swiss Finance Institute.
    29. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    30. Anna Scherbina & Bernd Schlusche, 2014. "Asset price bubbles: a survey," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 589-604, April.
    31. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099, arXiv.org, revised Jun 2013.
    32. Jawadi, Fredj & Namouri, Hela & Ftiti, Zied, 2018. "An analysis of the effect of investor sentiment in a heterogeneous switching transition model for G7 stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 469-484.
    33. Rahman, Md Lutfur & Shamsuddin, Abul, 2019. "Investor sentiment and the price-earnings ratio in the G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 55(C), pages 46-62.
    34. Lee, Chien-Chiang & Chen, Mei-Ping, 2020. "Happiness sentiments and the prediction of cross-border country exchange-traded fund returns," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    35. Leo Krippner, 2020. "A Note of Caution on Shadow Rate Estimates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 951-962, June.
    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. Oguzhan Cepni & Rangan Gupta & Jacobus Nel & Joshua Nielsen, 2023. "Monetary Policy Shocks and Multi-Scale Positive and Negative Bubbles in an Emerging Country: The Case of India," Working Papers 202305, University of Pretoria, Department of Economics.
    2. Riza Demirer & David Gabauer & Rangan Gupta & Joshua Nielsen, 2023. "Gold-to-Platinum Price Ratio and the Predictability of Bubbles in Financial Markets," Working Papers 202317, University of Pretoria, Department of Economics.
    3. Rangan Gupta & Jacobus Nel & Joshua Nielsen & Christian Pierdzioch, 2023. "Stock Market Volatility and Multi-Scale Positive and Negative Bubbles," Working Papers 202310, University of Pretoria, Department of Economics.
    4. Renee van Eyden & Rangan Gupta & Xin Sheng & Joshua Nielsen, 2023. "Predicting Multi-Scale Positive and Negative Stock Market Bubbles in a Panel of G7 Countries: The Role of Oil Price Uncertainty," Working Papers 202332, University of Pretoria, Department of Economics.

    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. Caraiani, Petre & Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua, 2023. "Monetary policy and bubbles in G7 economies using a panel VAR approach: Implications for sustainable development," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 133-155.
    2. Renee van Eyden & Rangan Gupta & Xin Sheng & Joshua Nielsen, 2023. "Predicting Multi-Scale Positive and Negative Stock Market Bubbles in a Panel of G7 Countries: The Role of Oil Price Uncertainty," Working Papers 202332, University of Pretoria, Department of Economics.
    3. Riza Demirer & David Gabauer & Rangan Gupta & Joshua Nielsen, 2023. "Gold-to-Platinum Price Ratio and the Predictability of Bubbles in Financial Markets," Working Papers 202317, University of Pretoria, Department of Economics.
    4. Oguzhan Cepni & Rangan Gupta & Jacobus Nel & Joshua Nielsen, 2023. "Monetary Policy Shocks and Multi-Scale Positive and Negative Bubbles in an Emerging Country: The Case of India," Working Papers 202305, University of Pretoria, Department of Economics.
    5. Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua, 2023. "US monetary policy and BRICS stock market bubbles," Finance Research Letters, Elsevier, vol. 51(C).
    6. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    7. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    8. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
    9. Rangan Gupta & Jacobus Nel & Joshua Nielsen & Christian Pierdzioch, 2023. "Stock Market Volatility and Multi-Scale Positive and Negative Bubbles," Working Papers 202310, University of Pretoria, Department of Economics.
    10. Oguzhan Cepni & Rangan Gupta & Qiang Ji, 2023. "Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 365-381, July.
    11. Hideyuki Takagi, 2021. "Exploring the Endogenous Nature of Meme Stocks Using the Log-Periodic Power Law Model and Confidence Indicator," Papers 2110.06190, arXiv.org.
    12. Rebecca Westphal & Didier Sornette, 2019. "Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model," Swiss Finance Institute Research Paper Series 19-29, Swiss Finance Institute.
    13. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    14. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    15. Papastamatiou, Konstantinos & Karakasidis, Theodoros, 2022. "Bubble detection in Greek Stock Market: A DS-LPPLS model approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    16. André, Christophe & Caraiani, Petre & Gupta, Rangan, 2023. "Fiscal policy and stock markets at the effective lower bound," Finance Research Letters, Elsevier, vol. 58(PC).
    17. Westphal, Rebecca & Sornette, Didier, 2020. "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 1-23.
    18. Aviral Kumar Tiwari & Deven Bathia & Elie Bouri & Rangan Gupta, 2021. "Investor Sentiment Connectedness: Evidence From Linear And Nonlinear Causality Approaches," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-29, December.
    19. Song, Ruiqiang & Shu, Min & Zhu, Wei, 2022. "The 2020 global stock market crash: Endogenous or exogenous?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    20. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 2021 Bitcoin Bubbles and Crashes—Detection and Classification," Stats, MDPI, vol. 4(4), pages 1-21, November.

    More about this item

    Keywords

    Multi-scale bubbles and crashes; Investor sentiment; Business and consumer confidence; Panel regressions; G7 stock markets;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:beexfi:v:38:y:2023:i:c:s2214635023000187. 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: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.