IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v66y2023ics0275531923001708.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531923001708
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2023.102044?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    2. Zouaoui, Haykel & Zoghlami, Feten, 2020. "On the income diversification and bank market power nexus in the MENA countries: Evidence from a GMM panel-VAR approach," Research in International Business and Finance, Elsevier, vol. 52(C).
    3. Boubaker, Sabri & Farag, Hisham & Nguyen, Duc Khuong, 2015. "Short-term overreaction to specific events: Evidence from an emerging market," Research in International Business and Finance, Elsevier, vol. 35(C), pages 153-165.
    4. Austmann, Leonhard M. & Vigne, Samuel A., 2021. "Does environmental awareness fuel the electric vehicle market? A Twitter keyword analysis," Energy Economics, Elsevier, vol. 101(C).
    5. Kim, Dai-Won & Yu, Jung-Suk & Hassan, M. Kabir, 2018. "Financial inclusion and economic growth in OIC countries," Research in International Business and Finance, Elsevier, vol. 43(C), pages 1-14.
    6. Behrendt, Simon & Schmidt, Alexander, 2018. "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 355-367.
    7. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Polyzos, Efstathios & Wang, Fang, 2022. "Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction," Energy Economics, Elsevier, vol. 114(C).
    9. Wu, You & Ren, Wenting & Wan, Jieru & Liu, Xiaoxue, 2023. "Time-frequency volatility connectedness between fossil energy and agricultural commodities: Comparing the COVID-19 pandemic with the Russia-Ukraine conflict," Finance Research Letters, Elsevier, vol. 55(PA).
    10. Kinateder, Harald & Campbell, Ross & Choudhury, Tonmoy, 2021. "Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets," Finance Research Letters, Elsevier, vol. 43(C).
    11. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).
    12. Beechey, Meredith J. & Wright, Jonathan H., 2009. "The high-frequency impact of news on long-term yields and forward rates: Is it real?," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 535-544, May.
    13. Singh, Sanjeet & Bansal, Pooja & Bhardwaj, Nav, 2022. "Correlation between geopolitical risk, economic policy uncertainty, and Bitcoin using partial and multiple wavelet coherence in P5 + 1 nations," Research in International Business and Finance, Elsevier, vol. 63(C).
    14. Boubaker, Sabri & Goodell, John W. & Pandey, Dharen Kumar & Kumari, Vineeta, 2022. "Heterogeneous impacts of wars on global equity markets: Evidence from the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 48(C).
    15. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    16. Daly, Kevin & Batten, Jonathan A. & Mishra, Anil V. & Choudhury, Tonmoy, 2019. "Contagion risk in global banking sector," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    17. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    18. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    19. Lee, Kiryoung & Choi, Eunseon & Kim, Minki, 2023. "Twitter-based Chinese economic policy uncertainty," Finance Research Letters, Elsevier, vol. 53(C).
    20. Polyzos, Stathis & Samitas, Aristeidis & Katsaiti, Marina-Selini, 2020. "Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability," International Review of Financial Analysis, Elsevier, vol. 72(C).
    21. Bollen, Bernard & Inder, Brett, 2002. "Estimating daily volatility in financial markets utilizing intraday data," Journal of Empirical Finance, Elsevier, vol. 9(5), pages 551-562, December.
    22. Olson, Eric & J. Vivian, Andrew & Wohar, Mark E., 2014. "The relationship between energy and equity markets: Evidence from volatility impulse response functions," Energy Economics, Elsevier, vol. 43(C), pages 297-305.
    23. Ali-Rind, Asad & Boubaker, Sabri & Jarjir, Souad Lajili, 2023. "Peer effects in financial economics: A literature survey," Research in International Business and Finance, Elsevier, vol. 64(C).
    24. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    25. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    26. Klaus, Jürgen & Koser, Christoph, 2021. "Measuring Trump: The Volfefe Index and its impact on European financial markets," Finance Research Letters, Elsevier, vol. 38(C).
    27. Lin, Hai & Lo, Ingrid & Qiao, Rui, 2021. "Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market," Journal of Banking & Finance, Elsevier, vol. 133(C).
    28. Roma, Paolo & Aloini, Davide, 2019. "How does brand-related user-generated content differ across social media? Evidence reloaded," Journal of Business Research, Elsevier, vol. 96(C), pages 322-339.
    29. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
    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. 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. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    3. 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).

    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. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    2. Kumari, Vineeta & Kumar, Gaurav & Pandey, Dharen Kumar, 2023. "Are the European Union stock markets vulnerable to the Russia–Ukraine war?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    4. Pandey, Dharen Kumar & Assaf, Rima & Rai, Varun Kumar, 2023. "Did the Indian stock market sail the Russia-Ukraine storm safely?," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    5. Shahzad, Syed Jawad Hussain & Anas, Muhammad & Bouri, Elie, 2022. "Price explosiveness in cryptocurrencies and Elon Musk's tweets," Finance Research Letters, Elsevier, vol. 47(PB).
    6. Seok-Jun Yun & Sun-Yong Choi & Young Sung Kim, 2023. "Examining the hedge performance of US dollar, VIX, and gold during the coronavirus pandemic: Is US dollar a better hedge asset?," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-19, October.
    7. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    8. Assaf, Rima & Gupta, Deeksha & Kumar, Rahul, 2023. "The price of war: Effect of the Russia-Ukraine war on the global financial market," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    9. Umar, Zaghum & Gubareva, Mariya & Yousaf, Imran & Ali, Shoaib, 2021. "A tale of company fundamentals vs sentiment driven pricing: The case of GameStop," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    10. Loutfi, Ahmad Amine, 2024. "Renewable energy stock prices forecast using environmental television newscasts investors’ sentiment," Renewable Energy, Elsevier, vol. 230(C).
    11. Chortane, Sana Gaied & Pandey, Dharen Kumar, 2022. "Does the Russia-Ukraine war lead to currency asymmetries? A US dollar tale," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    12. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
    13. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    14. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    15. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    16. Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    17. Jin Chen & Yue Chen & Wei Zhou, 2024. "Relation exploration between clean and fossil energy markets when experiencing climate change uncertainties: substitutes or complements?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    18. Yan-Hong Yang & Ying-Lin Liu & Ying-Hui Shao, 2023. "Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," Papers 2310.18903, arXiv.org, revised Jun 2024.
    19. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    20. Pandey, Dharen Kumar & Hassan, M.Kabir & Kumari, Vineeta & Hasan, Rashedul, 2023. "Repercussions of the Silicon Valley Bank collapse on global stock markets," Finance Research Letters, Elsevier, vol. 55(PB).

    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-

    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:riibaf:v:66:y:2023:i:c:s0275531923001708. 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: http://www.elsevier.com/locate/ribaf .

    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.