Role of Crude Oil, Natural Gas and Wheat Prices and the Impact of the ‎Russian-Ukrainian War on the Investor Social Network Sentiment; Evidence ‎from the US Stock Market

NEIFAR, MALIKA and HDIDER, ANIS (2024): Role of Crude Oil, Natural Gas and Wheat Prices and the Impact of the ‎Russian-Ukrainian War on the Investor Social Network Sentiment; Evidence ‎from the US Stock Market. Published in:

Abstract

Through an empirical analysis of the impact of fluctuations in the international prices of crude oil, natural gas ‎and wheat on the US stock market performance, the study seeks to show evidence of the investor social ‎network sentiment effects post the Ukraine war declaration on February 24, 2022. A ‎comparative approach was used for Ukraine's pre- vs post-war declaration period. The ‎considered models are of the GARCH-X type. Founding show that only post-war declaration; ‎investor sentiment as well as the economic factors such as the prices of raw materials ‎‎(including crude oil and natural gas) and food (wheat) have caused the volatility of the S&P ‎‎500 index return, while market volatility (VIX) affect negatively the stock market return pre- ‎and post-war declaration.‎

• Item Type: MPRA Paper
• Language: English
• Keywords: S&P500 stock index; American market volatility (the VIX index); American investor ‎sentiment on tweeter, Ukraine War; commodity prices (crude oil, natural gas and wheat); ‎GARCH-X model.
• Subjects: E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

A. Christakis, N., & H. Fowler, J. (2009). Connected: The Surprising Power of our Social ‎Networks and How they Shape our Lives. Journal of Cognition and Culture, 353 pages. ‎doi:DOI:10.1163/156853710X531267‎

A. Samuelson, P. (1947). Foundations of Economic Analysis. Havard: Harvard University Press.‎

Akerlof, G. A., & Shiller, R. J. (2015). Phishing for Phools: The Economics of Manipulation ‎and Deception. United States: Princeton University Press. ‎doi:https://doi.org/10.2307/j.ctvc777w8‎

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting ‎realized volatility. Econometrica, 71(2), 579–625.‎

Aral, S., & Dylan, W. (2012). Identifying Influential and Susceptible Members of Social ‎Networks. Science AAAS, 1-32. doi:DOI: 10.1126/science.1215842 ‎

Aral, S., Muchnik, L., & Taylor, S. J. (2013). Social Influence Bias: A Randomized Experiment. ‎Science AAAS, 1-6. doi:DOI: 10.1126/science.1240466‎

Arnd, C., Diao, X., Dorosh, P., Paw, K., & Thurlow, J. (2023, March). The Ukraine war and ‎rising commodity prices: Implications for developing countries. Global Food Security, 1-‎‎9. doi:https://doi.org/10.1016/j.gfs.2023.100680 ‎

Audrino, F., Sigrist, F., & Ballinari, D. (2019). The impact of sentiment and attention measures ‎on stock market volatility. International Journal of Forecasting, 1-24. ‎doi:https://doi.org/10.1016/j.ijforecast.2019.05.010‎

Bapat, S. R., Kothari, S., & Bansal, R. (2022, October). Sentiment Analysis of ESG disclosures ‎on Stock Market. arXiv:2210.00731v1 [q-fin.CP], 1-9.‎

Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial ‎connectedness and systemic risk. J. Financial Econom. 16 (2), 271–296.‎

Bekaert, G., & R. Harvey, C. (2000). Foreign Speculators and Emerging Equity Markets. The ‎Journal of Finance , Vol. 55, No. 2, 565-613.‎

Berkman, H., Jacobsen, B., & Lee, J. B. (2011). Time-varying rare disaster risk and stock ‎returns. Journal of Financial Economics, 101(2), 313–332‎. ‎ Black, F., Scholes, M., & Merton, R. c. (1973). The Pricing of Options and Corporate Liabilities. ‎Journal of Political Economy, Vol. 81, No. 3, 637-654 (18 pages).‎

Bollen, J., Mao, H., & Pepe, A. (2009). Modeling Public Mood and Emotion: Twitter Sentiment ‎and Socio-Economic Phenomena. (pp. 1-10). Bloomington-Los Angelos: Proceedings of ‎the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011), ‎‎17-21 July 2011, Barcelona, Spain. doi:https://doi.org/10.1609/icwsm.v5i1.14171‎

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of ‎Computational Science, Volume 2( Issue), 1-8. ‎doi:https://doi.org/10.1016/j.jocs.2010.12.007‎

Boungou, W., & Yatié, A. (2022, April). The impact of the Ukraine–Russia war on world stock ‎market returns. Economics Letters, 1-3. ‎doi:https://doi.org/10.1016/j.econlet.2022.110516‎

Brown, W. O., & Burdekin, R. C. (2002). German debt traded in London during the second ‎world war: A British perspective on Hitler. Economica, 69(276), 655–669‎.‎

Cabarcos, L. M., Ada, M. P.-P., & López-Pérez, M. L. (2019). Does Social Network Sentiment ‎Influence S&P 500 Environmental & Socially Responsible Index? Sustainability, ‎‎11(320). doi:10.3390/su11020320‎

Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. Am. Econ. Rev., 112 (4), ‎‎1194–1225.‎

Cortes, G., Vossmeyer, A., & Weidenmier, M. (2022). Stock Volatility and the War Puzzle. ‎National Bureau of Economic Research: NBER Working Paper (No. 29837).‎

Diebold, F., & Yılmaz, K. (2014). On the network topology of variance decompositions: ‎Measuring the connectedness of financial firms. J. Econom. 182 (1).‎

Ding, Z., Engle, R., & Granger, C. (1993). Long Memory Properties of Stock Market Returns and ‎a New Model. Journal of Empirical Finance, 1, 83-106.‎

Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating Time Varying Risk Premia in the ‎Term Structure: The ARCH-M Model. Econometrica, 55(2), 391-407.‎

Engle, R., & Ng, V. (1993). Measuring and Testing the Impact of News on Volatility. Journal of ‎Finance, 48, 1749–1778.‎

Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969). The adjustment of stock prices to new ‎information. Int. Econ. Rev. 10 (1), 1–21. ‎ Frey, B. S., & Kucher, M. (2000). World war II as reflected on capital markets. Economics ‎Letters, 69(2), 187–191.‎

Frey, B. S., & Kucher, M. (2001). Wars and markets: How bond values reflect the second world ‎war. Economica, 68 (271), 317–333‎.‎

Frey, B. S., & Waldenström, D. (2004). Markets work in war: World war II reflected in the ‎Zurich and Stockholm bond markets. Financial History Review, 11(1), 51–67.‎

Garcia, D., & Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal ‎Society Open Science, 1-19.‎

Hodorog, A., Petri, I., & Rezgui, Y. (2022, july). Machine learning and Natural Language ‎Processing of social media data for event detection in smart cities. Sustainable Cities and ‎Society, 1-20. doi:https://doi.org/10.1016/j.scs.2022.104026‎

Hong, H., & Stein, J. C. (1999). A Unified Theory of Underreaction, Momentum Trading, and ‎Overreaction in Asset. The Journal of Finance , 1-43.‎

Hudson, R., & Urquhart, A. (2015). War and stock markets: The effect of world war two on the ‎British stock market. International Review of Financial Analysis, 40, 166–177.‎

Izzeldin, M., Muradoglu, Y. G., Pappas, V., Petropoulou, A., & Sivaprasad, S. (2023, February). ‎The impact of the Russian-Ukrainian war on global financial markets. International ‎Review of Financial Analysis, 1-13. doi:https://doi.org/10.1016/j.irfa.2023.102598 ‎

J. Shiller, R. (2000). The American Journal of Economics and Sociology (Vol. Vol. 59, No. 3). ‎Princeton: The American Journal of Economics and Sociology.‎

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. ‎Econometrica , Vol. 47, No. 2, 263-292 (29 pages). doi:https://doi.org/10.2307/1914185‎

Kamal, M.-R., Ahmed, S., & Hasan, M. M. (2023, April). The impact of the Russia-Ukraine ‎crisis on the stock market. Pacific-Basin Finance Journal, 1-27. ‎doi:https://doi.org/10.1016/j.pacfin.2023.102036 ‎ Kevin, H., D. G., & Eziz, D. (2021). Twitter Sentiment Analysis for Predicting Stock Price ‎Movements. Stanford University, Department of Computer Science, USA. doi:CS230: ‎Deep Learning, Winter 2018, Stanford University, CA. (LateX template borrowed from ‎NIPS 2017.)‎

Liadze, I., Macchiarelli, C., Mortimer-Lee, P., & Juanino, P. (2022). The economic costs of the ‎Russia-Ukraine conflict. NIESR Policy Pap(32).‎

Liao, S. (2023, February). The Russia-Ukraine Outbreak and the Value of Renewable Energy. ‎Economics Letters, 1-20. doi:DOI: https://doi.org/10.1016/j.econlet.2023.111045‎

Liu, Q., Wang, X., & Du, Y. (2022, December). The weekly cycle of investor sentiment and the ‎holiday effect- An empirical study of Chinese stock market based on natural language ‎processing.

Heliyon, 1-14. doi:https://doi.org/10.1016/j.heliyon.2022.e12646‎ M. Barber, B., & Odeon, T. (2001). BOYS WILL BE BOYS: GENDER, OVERCONFIDENCE, ‎AND COMMON STOCK INVESTMENT. QUARTERLY JOURNAL OF ECONOMICS, 1-‎‎32.‎

Moat, H., Curne, C., Avakian, A., Kenett, D., Stanley, E., & Preis, T. (2013). Quantifying ‎Wikipedia usage patterns before stock market moves. Sci. Rep, 3 (1801).‎

Neifar, M. (2020, March 17). Stock Market Volatility Analysis: A Case. Munich, Germany. ‎Récupéré sur https://mpra.ub.uni-muenchen.de/99140/‎

Neifar, M., Charfeddine, S., & Kammoun, A. (2022). Financial Performance of Islamic ‎Versus ‎Conventional Banks a Comparative Analysis for Jordan. International Journal of ‎‎Economics and Financial Issues, 12(6), 65-74. doi:DOI: https://doi.org/10.32479/ij

Nguyen, D. Q., Vu, T., & Nguyen, A. T. (2020, November). BERTweet: A pre-trained language ‎model for English Tweets. Association for Computational Linguistics, 1-6. ‎doi:Proceedings of the 2020 EMNLP (Systems Demonstrations), pages 9–14‎

Nyakurukwa, K., & Seetharam, Y. (2023, February ). The evolution of studies on social media ‎sentiment in the stock market: Insights from bibliometric analysis. Scientific African, 1-‎‎11. doi:https://doi.org/10.1016/j.sciaf.2023.e01596‎

Oh, C., & Sheng, O. (December, 2011). Investigating predictive power of stock micro blog ‎sentiment in forecasting future stock price directional movement. In Proceedings of the ‎ICIS, Association for Information Systems, (pp. 1-19). Shanghai, China.‎

Paes Herrera, G., Constantino, M., Su, J.-J., & Naranpanawa, A. (2022, September). Renewable ‎energy stocks forecast using Twitter investor sentiment and deep learning. Energy ‎Economics, 1-11. doi:https://doi.org/10.1016/j.eneco.2022.106285‎

Piñeiro-Chousa, J., López-Cabarcos, M., Pérez-Pico, A., & Ribeiro-Navarrete, B. (2018). Does ‎social network sentiment influence the relationship between the S&P 500 and gold ‎returns? Int. Rev. Financ. Anal., 57, 57-64.‎

Poterba, J. M., & Summers, L. H. (1984, September ). THE PERSISTENCE OF VOLATILITY ‎AND STOCK MARKET FLUCTUATIONS. National Bureau of Economic Research , ‎pp. 1-34. doi:DOI 10.3386/w1462‎

Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015). Studying User ‎Income through Language, Behaviour and Affect in Social Media. PLOS ONE. doi:DOI: ‎‎10.1371/journal.pone.0138717‎

Qing, L., Woon-Seek, L., Minghao, H., & Qingjun, W. (2022, September). Synergy between ‎stock prices and investor sentiment in social media. Borsa Istanbul Review, 1-17. ‎doi:https://doi.org/10.1016/j.bir.2022.09.006‎

R.H. Thaler, C. S. (2008). Nudge: Improving Decisions About Health, Wealth and Happiness. ‎New Haven: Yale University Press.‎

Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015, September). The ‎Effects of Twitter Sentiment on Stock Price returns. Plos One, 1-21. doi:PLOS ONE | ‎DOI:10.1371/journal.pone.0138441‎

Ranjan, J. P., & Majhi, R. (2022, November). Are Twitter sentiments during COVID-19 ‎pandemic a critical determinant to predict stock market movements? A machine learning ‎approach. Scientific African, 1-11. doi:https://doi.org/10.1016/j.sciaf.2022.e01480‎

Reinhart, C., & Rogoff, K. (2009). This Time Is Different: Eight Centuries of Financial Folly. ‎Maryland-Havard: Princeton University Press.‎

Renault, T. (2019, September). Sentiment analysis and machine learning in fnance: ‎a comparison of methods and models on one million messages. Digital Finance, 1-13. ‎doi:https://doi.org/10.1007/s42521-019-00014-x

S. Nye, J. (2002). The Paradox of American Power: Why the World’s Only Superpower Can’t ‎Go It Alone. Oxford: Oxford University Press.‎

Sabherwal, S., Sarkar, S., & Zhang, Y. (2011). Do Internet stock message boards in- fluence ‎trading? Evidence from heavily discussed stocks with no fundamental news. J. Bus. ‎Financ. Account., 38, 1209–1237.‎

Schaeffer, R., Borba, B., Rathmann, R., Szklo, A., & Branco, D. (2012). Dow Jones ‎sustainability index transmission to oil stock market returns: A GARCH approach. ‎Energy, 45, 933–943.‎

Shleifer, A., & W. Vishny, R. (1997). The Limits of Arbitrage. The Journal of Finance, Vol. 52, ‎No. 1 , 35-55 (21 pages). doi:https://doi.org/10.2307/2329555‎

Sinha, A., Kedas, S., Kumar, R., & Malo, P. (2022, February India-Finland). SEntFiN 1.0: ‎Entity-aware sentiment analysis for financial news. Association for Information Science ‎and Technology, 1-22. doi:DOI: 10.1002/asi.24634‎

Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. (2014). Tweets and Trades: the ‎Information Content of Stock Microblogs. European Financial Management. ‎doi:DOI:10.1111/j.1468-036X.2013.12007.x‎

Tuckett, D., & J., T. (2011). Emotional Finance: The Role of the Unconscious in Financial ‎Decisions. London: The Research Foundation of CFA Institute. ‎doi:DOI:10.1002/9781118258415.ch6‎

Umar, Z., Bossman, A., Choi, S.-Y., & Vo, X. V. (2022, October). Are short stocks susceptible ‎to geopolitical shocks? Time-Frequency evidence from the Russian-Ukrainian conflict. ‎Finance Research Letters, 1-9. doi:https://doi.org/10.1016/j.frl.2022.103388 ‎

Waldenström, D. &. (2008). Did Nordic countries recognize the gathering storm of world war II? ‎Evidence from the bond markets. Explorations in Economic History, 45(2), 107–126‎.‎

Wisniewski, T. P. (2016). Is there a link between politics and stock returns? A literature survey. ‎International Review of Financial Analysis, 47, 15–23.‎

WorldBank. (2022b). Developing economies must act now to dampen the shocks from the ‎Ukraine conflict. WorldBank.Org. Retrieved from. Récupéré sur ‎https://blogs.worldbank.org/voices/developing-economies-must-act-now-dampen-shocks-‎ukraine-conflict? cid=ECR_TT_worldbank_EN_EXT

Wu, F.-l., Zhan, X.-d., Zhu, J.-q., & Wang, M.-h. (2023, April). Stock market volatility and ‎Russia–Ukraine conflict. Finance Research Letters, 1-14. ‎doi:https://doi.org/10.1016/j.frl.2023.103919‎

Xiang, C., Zhang, J., Li, F., Fei, H., & Ji, D. (2022, May). A semantic and syntactic enhanced ‎neural model for financial sentiment analysis. Information Processing and Management, ‎‎1-15. doi:https://doi.org/10.1016/j.ipm.2022.102943‎

Yousaf, I., Patel, R., & Yarovaya, L. (2022). The reaction of G20+ stock markets to the Russia-‎Ukraine conflict ‘black-swan’ event: evidence from event study approach. SSRN ‎Electronic J. doi: https://doi.org/10.2139/ssrn.4069555‎

Zeitun, R., Ur Rehman, M., Ahmad, N., & Vinh Vo, X. (2022, November ). The impact of ‎Twitter-based sentiment on US sectoral returns. North American Journal of Economics ‎and Finance, 1-16. doi:https://doi.org/10.1016/j.najef.2022.101847 ‎ Zvonarev, A., & Bilyi, A. (2019). A Comparison of Machine Learning Methods of Sentiment ‎Analysis Based on Russian Language Twitter Data. CEUR-WS.org/Vol-2590/short35.pdf, ‎‎1-7.‎