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Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentiment

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  • Polyzos, Efstathios

Abstract

In this paper, we propose the use of social media information as a real-time decision-making tool for significant war events, using the war in Ukraine as a case study. We proxy the public’s perception of the progression of events using sentiment analysis on 42 million tweets and calculate impulse response functions on 5-min data for 15 economic and financial indicators. European indices (currencies and markets) experience an immediate negative response to conflict escalation “shocks”, while crude oil registers a delayed negative response. US stock markets seem unaffected, while the US Dollar responds positively to negative events of the war. Our findings suggest that user generated content can be used as a decision-making tool when important war events unfold. This approach can monitor the public’s perception of such events as well as capture their potential economic impact, which carries increased importance in times of increasing prices.

Suggested Citation

  • Polyzos, Efstathios, 2023. "Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentiment," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001708
    DOI: 10.1016/j.ribaf.2023.102044
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    References listed on IDEAS

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    Cited by:

    1. Cao, Fangzhi & Su, Chi-Wei & Sun, Dian & Qin, Meng & Umar, Muhammad, 2024. "U.S. monetary policy: The pushing hands of crude oil price?," Energy Economics, Elsevier, vol. 134(C).
    2. Cao, Fangzhi & Su, Chi-Wei & Qin, Meng & Moldovan, Nicoleta-Claudia, 2024. "The investment of renewable energy: Is green bond a safe-haven to hedge U.S. monetary policy uncertainty?," Energy, Elsevier, vol. 307(C).

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    More about this item

    Keywords

    Ukrainian war; Financial markets; Economic indicators; Twitter sentiment; Impulse response functions;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • 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
    • N44 - Economic History - - Government, War, Law, International Relations, and Regulation - - - Europe: 1913-

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