Vol. 13 No. 4 (2025)
December 2025

Articles

The impact of economic sentiment on European stock markets

Published 2025-12-28

  • Anna Czapkiewicz
    • ,
  • Agnieszka Choczyńska
    • ,
  • Artur Machno

Anna Czapkiewicz
AGH University of Krakow

Agnieszka Choczyńska
AGH University of Krakow

Artur Machno
AGH University of Krakow

Abstract

Objective: This article aims to analyse the impact of sentiment indicators reflecting the condition of major economies on the returns and volatility of European developed, emerging, and frontier stock markets.

Research Design & Methods: We employed survey-based economic sentiment indicators, classified into forward-looking measures reflecting economic expectations and measures of current economic sentiment. We used survey-based economic sentiment indicators, namely the ZEW Economic Sentiment Index for Germany, the ZEW Economic Sentiment Index for the Eurozone, the ZEW Current Condition Index for the Eurozone and the Michigan Consumer Sentiment Index from the US. We modelled the daily stock returns for 27 markets over the period 2008-2022 using a GJR-GARCH model and a time-varying transition probability Markov switching model (TVPMS), where transition probabilities depend on lagged economic sentiment indicators.

Findings: The results from the GJR-GARCH model show that economic sentiment generally affects returns and their volatility. We observed the causal relationship between sentiment and returns for different types of markets. These results confirm that sentiment indicators in major economies have a global impact, affecting not only developed markets but also emerging and frontier markets. In addition to the GJR-GARCH methodology, the use of the TVPMS approach confirms the results and provides new insights. We found two market states: high and low volatility, and documented the impact of sentiment on market returns as a function of these states. The Michigan consumer sentiment index had the most substantial and persistent effect on European markets, with significant effects on volatility in both high- and low-volatility states; the ZEW economic sentiment indices affect volatility in high-volatility states, while the ZEW current situation index has a significant effect on volatility only in low-volatility states.

Implications & Recommendations: These findings provide investors and financial managers with valuable insights into the influence of different sentiment indicators on decision-making in various market conditions. During periods of high volatility, sentiment based on economic expectations can help to mitigate market fluctuations. In contrast, during stable periods, sentiment reflecting the current economic state is more informative. The U.S. Michigan Consumer Sentiment Index (MCSI) is particularly relevant in this regard, offering meaningful guidance about investment decisions in both stable and volatile environments. The influence of the same sentiment indicators on various European stock markets highlights the region’s strong interconnections and suggests the presence of sentiment contagion.

Contribution & Value Added: This study makes an original contribution by combining two well-established models in a novel way. The GJR-GARCH model enables us to analyse the impact of economic sentiment on expected returns and conditional volatility, while the TVP-MS model identifies periods when sentiment exerts its greatest influence. Our research demonstrates that market behaviour responds differently to sentiment indicators, depending on their nature and the volatility regime.

Keywords

stock markets, TVPMS model, AR(1)-GJR-Garch(1,1), ZEW, MCSI

(PDF) Save

Author Biography

Anna Czapkiewicz

An Associate Professor at the Department of Mathematical Applications in Economics at the AGH University in Krakow, Poland. She is also an expert at the Regional Statistical Office in Kielce, Poland. Her research interests include econometrics, financial markets, statistics, applications of mathematics in economics. Anna Czapkiewicz has published more than 60 research papers in international/national journals and conferences.

Agnieszka Choczyńska

Research and teaching assistant at the Department of Management, AGH University of Krakow. Her research interests include the impact of economic and media sentiment on financial markets and the construction of sentiment indicators.

Artur Machno

Assistant professor at the Department of Management, AGH University of Krakow. His research interests include financial econometrics; stock market dependencies and international market dynamics. Experienced data scientist with industry background in AI, credit risk modelling, and CRM analytics.

References

  1. Aloy, M., De Truchis, G., Dufrénot, G., & Keddad, B. (2014). Shift-Volatility Transmission in East Asian Equity Markets: New Indicators. In Dufrénot, G., Jawadi, F., & Louhichi, W. (Eds.), Market Microstructure and Nonlinear Dynamics (pp. 273-291), Springer, Cham https://doi.org/10.1007/978-3-319-05212-0_10
  2. Baker, M., & Stein, J.C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271-99. https://doi.org/10.1016/j.finmar.2003.11.005
  3. Baker, M., & Wurgler, J. (2006). Investor Sentiment and the Cross‐Section of Stock Returns. Journal of Finance, 61(4), 1645-1680. https://doi.org/10.1111/j.1540-6261.2006.00885.x
  4. Baker, M., & Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic Perspectives, 21(2), 129-152. https://doi.org/10.1257/jep.21.2.129
  5. Baker, M., Wurgler, J., & Yuan Y. (2012). Global, Local, and Contagious Investor Sentiment. Journal of Financial Economics, 104(2), 272-287. https://doi.org/10.1016/j.jfineco.2011.11.002
  6. Ballinari, D., Audrino, F., & Sigrist, F. (2022). When does attention matter? The effect of investor attention on stock market volatility around news releases. International Review of Financial Analysis, 82, 102-185. https://doi.org/10.1016/j.irfa.2022.102185
  7. Bossman A., Gubareva M., & Teplova T. (2023). EU sectoral stocks amid geopolitical risk, market sentiment, and crude oil implied volatility: An asymmetric analysis of the Russia-Ukraine tensions. Resources Policy, 82, 103515. https://doi.org/10.1016/j.resourpol.2023.103515.
  8. Chang, K-L. (2009). Do macroeconomic variables have regime-dependent effects on stock return dynamics? Evidence from the Markov regime switching model. Economic Modelling, 26(6), 1283-1299. https://doi.org/10.1016/j.econmod.2009.06.003
  9. Chen, S-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211-223. https://doi.org/10.1016/j.jbankfin.2008.07.013
  10. Chung, S-L., Hung, C-H. & Yeh, C-Y. (2012). When does investor sentiment predict stock returns?. Journal of Empirical Finance, 19(2), 217-240. https://doi.org/10.1016/j.jempfin.2012.01.002
  11. Cieślik, A., & Ghodsi, M. (2021). Economic sentiment indicators and foreign direct investment: Empirical evidence from European Union countries. International Economics, 168, 56-75. https://doi.org/10.1016/j.inteco.2021.07.001
  12. Corredor, P., Ferrer, E., & Santamaria, R. (2015). The Impact of Investor Sentiment on Stock Returns in Emerging Markets: The Case of Central European Markets. Eastern European Economics, 53(4), 328-355. https://doi.org/10.1080/00128775.2015.1079139
  13. Czapkiewicz, A. (2018). Determinanty zmian współzależności wybranych giełd papierów wartościowych: analiza relacji GPW w Warszawie z giełdami na świecie — [Determinants of changes in the structure of links between stock exchanges: analysis of relations with the Warsaw Stock Exchange]. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
  14. Davies, N. (2022). FDI in Europe in 2021: the state of play. Retrieved from https://www.investmentmonitor.ai/fdi-data/fdi-in-europe-in-2021-the-state-of-play on November 4, 2023.
  15. De Long, J.B., Shleifer, A., Summers, L.H., & Waldmann, R.J. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703-738. https://doi.org/10.1086/261703
  16. Filardo, A.J. (1994). Business-Cycle Phases and Their Transitional Dynamics. Journal of Business & Economic Statistics, 12(3), 299-308. https://doi.org/10.2307/1392086
  17. García, D. (2013). Sentiment during Recessions. The Journal of Finance, 68(3), 1267-1300. https://doi.org/10.1111/jofi.12027
  18. Gervais, S., & Odean, T. (2001). Learning to Be Overconfident. The Review of Financial Studies, 14(1), 1-27. https://doi.org/10.1093/rfs/14.1.1
  19. Hamilton , J.D. (1990). Analysis of time series subject to change in regime. Journal of Econometrics, 45(1-2), 39-70. https://doi.org/10.1016/0304-4076(90)90093-9
  20. Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press. https://doi.org/10.1515/9780691218632
  21. Hansson, J., Jansson, P., & Löf, M. (2005). Business survey data: Do they help in forecasting GDP growth?. International Journal of Forecasting, 21(2), 377-389. https://doi.org/10.1016/j.ijforecast.2004.11.003
  22. Homolka, L., & Pavelková, D. (2018). Predictive Power of the ZEW Sentiment Indicator: Case of the German Automotive Industry. Acta Polytechnika Hungarica, 15(4), 161-178. https://doi.org/10.12700/APH.15.4.2018.4.9
  23. Humpe, A., & Macmillan, P. (2009). Can macroeconomic variables explain long-term stock market movements? A comparison of the US and Japan. Applied Financial Economics, 19(2), 111-119. https://doi.org/10.1080/09603100701748956
  24. Karlsson, N., Loewenstein, G., & Seppi, D. (2009). The ostrich effect: Selective attention to information. Journal of Risk and Uncertainty, 38(2), 95-115. Retrieved from http://www.jstor.org/stable/41761376 on July 2, 2024.
  25. Kim, C-J., Piger, J., & Startz, R. (2008). Estimation of Markov regime-switching regression models with endogenous switching. Journal of Econometrics, 143(2), 263-273. https://doi.org/10.1016/j.jeconom.2007.10.002
  26. Larsson, R., & Haq, S. (2016). The dynamics of stock market returns and macroeconomic indicators: An ARDL approach with cointegration. KTH Industrial Engineering and Management. Retrieved form FULLTEXT01.pdf on July 1, 2025.
  27. Lee, W.Y., Jiang, C.X., & Indro, D.C. (2002). Stock market volatility, excess returns, and the role of investor sentiment. Journal of Banking & Finance, 26(12), 2277-2299. https://doi.org/10.1016/S0378-4266(01)00202-3
  28. Lemmon, M., & Portniaguina, E.(2006). Consumer Confidence and Asset Prices: Some Empirical Evidence. The Review of Financial Studies, 19(4), 1499-1529. https://doi.org/10.1093/rfs/hhj038
  29. Li, X., & Luo, D. (2017). Investor Sentiment, Limited Arbitrage, and the Cash Holding Effect. Review of Finance, 21(6), 2141-2168. https://doi.org/10.1093/rof/rfw031
  30. Lischka, J.A. (2015). What Follows What? Relations between Economic Indicators, Economic Expectations of the Public, and News on the General Economy and Unemployment in Germany, 2002-2011. Journalism & Mass Communication Quarterly, 92(2), 374-398. https://doi.org/10.1177/1077699015574098
  31. Mahmood, W.M., & Dinniah, M.N. (2009). Stock Returns and Macroeconomics Variables: Evidence from the Six Asian-Pacific Countries. International Research Journal of Finance and Economics, 5(30).
  32. Nofsinger, J.R. (2005). Social Mood and Financial Economics. Journal of Behavioral Finance, 6(3), 144-160. https://doi.org/10.1207/s15427579jpfm0603_4
  33. Petit, J.J.G., Lafuente, E.V., & Vieites, A.R. (2019). How information technologies shape investor sentiment: A web-based investor sentiment index. Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, 19(2), 95-105. https://doi.org/10.1016/j.bir.2019.01.001
  34. Pilinkus, D. (2010). Macroeconomic indicators and their impact on stock market performance in the short and long run: The case of the baltic states. Technological and Economic Development of Economy, 16(2), 291-304. https://doi.org/10.3846/tede.2010.19
  35. Rakovská, Z. (2021). Composite survey sentiment as a predictor of future market returns: Evidence for German equity indices. International Review of Economics & Finance, 73, 473-495. https://doi.org/10.1016/j.iref.2020.12.022
  36. Rapach, D.E., Mark E., Wohar, M., & Rangvid, J. (2005). Macro variables and international stock return predictability. International Journal of Forecasting, 21(1), 137-166. https://doi.org/10.1016/j.ijforecast.2004.05.004
  37. Schmeling, M. (2007). Institutional and individual sentiment: Smart money and noise trader risk?. International Journal of Forecasting, 23(1), 127-145. https://doi.org/10.1016/j.ijforecast.2006.09.002
  38. Schröder, M., & Hüfner, F.P. (2002). Forecasting economic activity in Germany: how useful are sentiment indicators?. ZEW Discussion Papers, 02-56, Zentrum für Europäische Wirtschaftsforschung (ZEW). Mannheim.
  39. Shi, Y., Tang, Y., & Long, W. (2019). Sentiment contagion analysis of interacting investors: Evidence from China’s stock forum, Physica A. Statistical Mechanics and its Applications, 523, 246-259. https://doi.org/10.1016/j.physa.2019.02.025
  40. Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior & Organization, 107, 730-743. https://doi.org/10.1016/j.jebo.2014.06.004
  41. Trabelsi, M.A., & Hmida, S. (2018). A Dynamic Correlation Analysis of Financial Contagion: Evidence from the Eurozone Stock Markets. Entrepreneurial Business and Economics Review, 6(3), 129-141. https://doi.org/10.15678/EBER.2018.060308
  42. Ung, S.N., Gebka, B., & Anderson, R.D.J. (2023) Is sentiment the solution to the risk–return puzzle? A (cautionary) note. Journal of Behavioral and Experimental Finance, 37, 100787, https://doi.org/10.1016/j.jbef.2023.100787
  43. Vuong, Q.H. (1989). Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses. Econometrica, 57(2), 307-333. https://doi.org/10.2307/1912557
  44. Wang, W., Su, C., & Duxbury, D. (2022). The conditional impact of investor sentiment in global stock markets: A two-channel examination. Journal of Banking & Finance, 138, 106458. https://doi.org/10.1016/j.jbankfin.2022.106458
  45. Yu, J., & Yuan, Y. (2011). Investor sentiment and the mean–variance relation. Journal of Financial Economics, 100(2) 367-381. https://doi.org/10.1016/j.jfineco.2010.10.011

Downloads

Download data is not yet available.