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Russia-Ukraine conflict: The effect on European banks’ stock market returns

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  • Martins, António Miguel
  • Correia, Pedro
  • Gouveia, Ricardo

Abstract

We examine the short-term market impact of the beginning of the military conflict between Russia and Ukraine (February 24, 2022) on the largest European listed banks. Using an event study, for the 100 largest European listed banks, we observe a negative and statistically significant stock price reaction at and around the beginning of the military conflict. These results are consistent with investor sentiment, investment portfolio rebalancing and the asset pricing perspective. Furthermore, we use a cross-sectional analysis against a set of country- and bank-specific variables. The results show a higher negative stock market reaction for Russian listed banks and for foreign banks with a high exposure to Russia. The magnitude of the stock market reaction to the military conflict is reinforced or mitigated by bank-specific determinants such as size, profitability, risk aversion, operational efficiency level, institutional ownership and exposure to Russia.

Suggested Citation

  • Martins, António Miguel & Correia, Pedro & Gouveia, Ricardo, 2023. "Russia-Ukraine conflict: The effect on European banks’ stock market returns," Journal of Multinational Financial Management, Elsevier, vol. 67(C).
  • Handle: RePEc:eee:mulfin:v:67:y:2023:i:c:s1042444x23000051
    DOI: 10.1016/j.mulfin.2023.100786
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    1. Furdui Călin & Șfabu Dorina Teodora, 2023. "The European Banks Under the Shock of the Russian Invasion of 2022: An Event Study Approach," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 68(1), pages 62-77, April.
    2. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).
    3. Evrim Mandaci, Pınar & Azimli, Asil & Mandaci, Nazif, 2023. "The impact of geopolitical risks on connectedness among natural resource commodities: A quantile vector autoregressive approach," Resources Policy, Elsevier, vol. 85(PA).

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