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Authors

Masaaki Yoshimori

Abstract

This study develops an interdisciplinary framework linking geopolitical shocks to financial markets through macroeconomic fundamentals, perceived risk, and collective sentiment. Perceived risk shifts investors between optimistic and pessimistic states, while imitation can generate bistable sentiment. Energy prices affect fundamental valuations differently in net energy-importing and exporting economies. When markets share the same sensitivities to common risk and sentiment, cross-market comparisons remove common global components and reveal differences in energy exposure. The empirical analysis examines escalating tensions involving Iran in early March 2026. The daily Geopolitical Risk index exceeded four times its February average and Brent crude rose about 34% within a week, yet an event study finds no statistically significant abnormal return of the S&P 500, although the confidence interval does not rule out a sizeable decline; the index fell about 2% in the first week and about 8% by the end of March. A synthetic control constructed from five non-US equity markets shows that US equities outperformed by about 3.5 percentage points over the first two trading days, mainly against oil-importing markets; this gap is stable across fitting windows of 20 to 120 days but becomes small when a 250-day window places almost all weight on Canada, another net oil exporter, and the advantage narrowed sharply at the end of March. A vector autoregression (VAR) indicates that the volatility–return relationship is predominantly contemporaneous. The findings are consistent with, but do not establish, a weak-herding regime and an energy channel moderated by the US net energy position, because collective sentiment and herding are not measured directly. Drawing on feedback dynamics, state transitions, and equilibrium adjustments in physics and chemistry, the framework connects investor behavior with macroeconomic exposure to explain heterogeneous financial responses to geopolitical uncertainty.

Keywords:
behavioral finance, complex systems, econophysics, energy exposure, geopolitical risk, investor sentiment, synthetic control

Article Details

References

[1]Drakos, K. (2010). Terrorism activity, investor sentiment, and stock returns. Review of Financial Economics, 19(3), 128–135. https://doi.org/10.1016/j.rfe.2010.01.001

[2]Kollias, C., Papadamou, S., & Stagiannis, A. (2011). Terrorism and capital markets: The effects of the Madrid and London bomb attacks. International Review of Economics & Finance, 20(4), 532–541. https://doi.org/10.1016/j.iref.2010.09.004

[3]Kilian, L., & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267–1287. https://doi.org/10.1111/j.1468-2354.2009.00568.x

[4]Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225. https://doi.org/10.1257/aer.20191823

[5]Jamel, L., & Derbali, A. (2019). Damascus securities exchange weighted index volatilities and terrorist attacks in Syria. International Journal of Business and Risk Management, 2(1), 9–15.

[6]De Bondt, W. F. M., & Thaler, R. (1985). Does the stock market overreact? Journal of Finance, 40(3), 793–805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x

[7]Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

[8]Shefrin, H. (2007). Behavioral corporate finance: Decisions that create value. McGraw-Hill.

[9]Hirshleifer, D. (2020). Presidential address: Social transmission bias in economics and finance. Journal of Finance, 75(4), 1779–1831. https://doi.org/10.1111/jofi.12906

[10]Stanley, H. E., Amaral, L. A. N., Canning, D., Gopikrishnan, P., Lee, Y., & Liu, Y. (1999). Econophysics: Can physicists contribute to the science of economics? Physica A: Statistical Mechanics and Its Applications, 269(1), 156–169. https://doi.org/10.1016/S0378-4371(99)00185-5

[11]Bay, T., & Schinckus, C. (2012). Critical finance studies: An interdisciplinary manifesto. Journal of Interdisciplinary Economics, 24(1), 1–6. https://doi.org/10.1177/0260107912471454

[12]Yoshimori, M., Takada, H., & Matsugi, T. (2003). A dynamical system applied to foreign currency exchange determination. Forma, 18(3), 149–163. https://forma.katachi-jp.com/abstract/1803/18030149.html

[13]Yoshimori, M. (2024). The impact of the monetary policy of the U.S. Federal Reserve on the ratio of the U.S. dollar to the Japanese yen in the foreign exchange market during 2015–2023. SocioEconomic Challenges, 8(1), 16–30. https://doi.org/10.61093/sec.8(1).16-30.2024

[14]Yoshimori, M., & Kimball, C. T. (2025). From evolution to economics: How gradualism, punctuated equilibrium and bifurcation shape adaptive systems. Journal of Interdisciplinary Economics. https://doi.org/10.1177/02601079251352253

[15]Eyring, H. (1935). The activated complex in chemical reactions. The Journal of Chemical Physics, 3(2), 107–115. https://doi.org/10.1063/1.1749604

[16]Hänggi, P., Talkner, P., & Borkovec, M. (1990). Reaction-rate theory: Fifty years after Kramers. Reviews of Modern Physics, 62(2), 251–341. https://doi.org/10.1103/RevModPhys.62.251

[17]Yoshimori, M. (2019). A phase transition of the USD/JPY exchange rate with monetary policy. Hyperion International Journal of Econophysics & New Economy, 12(1). https://www.journal-hyperion.ro/journal-archive/category/39-volume-12-issue-1-2019?download=130:02-yoshimori

[18]Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623–685. https://doi.org/10.3982/ECTA6248

[19]Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024

[20]Pastor, Ľ., & Veronesi, P. (2013). Political uncertainty and risk premia. Journal of Financial Economics, 110(3), 520–545. https://doi.org/10.1016/j.jfineco.2013.08.007

[21]Baur, D. G., & Smales, L. A. (2020). Hedging geopolitical risk with precious metals. Journal of Banking & Finance, 117, 105823. https://doi.org/10.1016/j.jbankfin.2020.105823

[22]Yilmazkuday, H. (2024). Geopolitical risk and stock prices. European Journal of Political Economy, 83, 102553. https://doi.org/10.1016/j.ejpoleco.2024.102553

[23]Federle, J., Meier, A., Müller, G. J., & Sehn, V. (2026). Proximity to war: The stock market response to the Russian invasion of Ukraine. Journal of Money, Credit and Banking, 58(3), 681–703. https://doi.org/10.1111/jmcb.13226

[24]Shiller, R. J. (2000). Irrational exuberance. Princeton University Press.

[25]Shiller, R. J. (2017). Narrative economics. American Economic Review, 107(4), 967–1004. https://doi.org/10.1257/aer.107.4.967

[26]Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307–343. https://doi.org/10.1016/S0304-405X(98)00027-0

[27]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

[28]Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129

[29]Guo, X., Gu, C., Zhang, C., & Li, S. (2024). Institutional herding and investor sentiment. Journal of Financial Markets, 68, 100891. https://doi.org/10.1016/j.finmar.2024.100891

[30]Cookson, J. A., Lu, R., Mullins, W., & Niessner, M. (2024). The social signal. Journal of Financial Economics, 158, 103870. https://doi.org/10.1016/j.jfineco.2024.103870

[31]Bernanke, B. S., & Kuttner, K. N. (2005). What explains the stock market’s reaction to Federal Reserve policy? Journal of Finance, 60(3), 1221–1257. https://doi.org/10.1111/j.1540-6261.2005.00760.x

[32]Hamilton, J. D. (2009). Causes and consequences of the oil shock of 2007–08. Brookings Papers on Economic Activity. https://doi.org/10.3386/w15002

[33]Kilian, L. (2008). The economic effects of energy price shocks. Journal of Economic Literature, 46(4), 871–909. https://doi.org/10.1257/jel.46.4.871

[34]Aizenman, J., Lindahl, R., Stenvall, D., & Uddin, G. S. (2024). Geopolitical shocks and commodity market dynamics: New evidence from the Russia-Ukraine conflict. European Journal of Political Economy, 85, 102574. https://doi.org/10.1016/j.ejpoleco.2024.102574

[35]Bernanke, B. S., Gertler, M., & Watson, M. (1997). Systematic monetary policy and the effects of oil price shocks. Brookings Papers on Economic Activity, 1997(1), 91–157. https://doi.org/10.2307/2534702

[36]U.S. Energy Information Administration. (n.d.). Oil and petroleum products explained: Oil imports and exports. https://www.eia.gov/energyexplained/oil-and-petroleum-products/imports-and-exports.php

[37]Mantegna, R. N., & Stanley, H. E. (1999). An introduction to econophysics: Correlations and complexity in finance. Cambridge University Press. https://doi.org/10.1017/CBO9780511755767

[38]Bouchaud, J.-P., & Potters, M. (2003). Theory of financial risk and derivative pricing. Cambridge University Press. https://doi.org/10.1017/CBO9780511753893

[39]Lux, T., & Marchesi, M. (1999). Scaling and criticality in a stochastic multi-agent model of financial markets. Nature, 397, 498–500. https://doi.org/10.1038/17290

[40]Sornette, D. (2003). Why stock markets crash: Critical events in complex financial systems. Princeton University Press.

[41]Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460, 685–686. https://doi.org/10.1038/460685a

[42]Kirman, A. (1993). Ants, rationality, and recruitment. Quarterly Journal of Economics, 108(1), 137–156. https://doi.org/10.2307/2118498

[43]Lux, T. (1995). Herd behaviour, bubbles and crashes. Economic Journal, 105(431), 881–896. https://doi.org/10.2307/2235156

[44]Einstein, A. (1905). On the motion of small particles suspended in liquids at rest required by the molecular-kinetic theory of heat. Annalen der Physik, 17, 549–560. https://doi.org/10.1002/andp.19053220806

[45]Bachelier, L. (1900). Théorie de la spéculation. Annales Scientifiques de l’École Normale Supérieure, 17, 21–86. https://doi.org/10.24033/asens.476

[46]Merton, R. C. (1973). Theory of rational option pricing. Bell Journal of Economics, 4(1), 141–183. https://doi.org/10.2307/3003143

[47]Haken, H. (1983). Synergetics: An introduction. Springer. https://doi.org/10.1007/978-3-642-88338-5

[48]Gillespie, D. T. (1977). Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81(25), 2340–2361. https://doi.org/10.1021/j100540a008

[49]van Kampen, N. G. (2007). Stochastic processes in physics and chemistry (3rd ed.). Elsevier. https://doi.org/10.1016/B978-044452965-7/50006-4

[50]Schlögl, F. (1972). Chemical reaction models for non-equilibrium phase transitions. Zeitschrift für Physik, 253(2), 147–161. https://doi.org/10.1007/BF01379769

[51]Bornholdt, S. (2001). Expectation bubbles in a spin model of markets: Intermittency from frustration across scales. International Journal of Modern Physics C, 12(5), 667–674. https://doi.org/10.48550/arXiv.cond-mat/0105224

[52]MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of Economic Literature, 35(1), 13–39.

[53]Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of Financial Economics, 14(1), 3–31. https://doi.org/10.1016/0304-405X(85)90042-X

[54]Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review, 93(1), 113–132. https://doi.org/10.1257/000282803321455188

[55]Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. https://doi.org/10.1198/jasa.2009.ap08746

[56]Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495–510. https://doi.org/10.1111/ajps.12116

[57]Doudchenko, N., & Imbens, G. W. (2016). Balancing, regression, difference-in-differences and synthetic control methods: A synthesis (NBER Working Paper No. 22791). National Bureau of Economic Research. https://doi.org/10.3386/w22791

[58]Ferman, B., & Pinto, C. (2021). Synthetic controls with imperfect pretreatment fit. Quantitative Economics, 12(4), 1197–1221. https://doi.org/10.3982/QE1596

[59]Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017

[60]Jordà, Ò. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161–182. https://doi.org/10.1257/0002828053828518

[61]Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. https://doi.org/10.2307/1913610

[62]Rey, H. (2015). Dilemma not trilemma: The global financial cycle and monetary policy independence (NBER Working Paper No. 21162). National Bureau of Economic Research. https://doi.org/10.3386/w21162

[63]Cornevin, A. (2025). Geopolitical risks and economic expectations: The role of trade linkages (Working Paper No. 11). Graduate Institute of International and Development Studies Working Paper Series, 1–35. https://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP11-2025.pdf

[64]Salachas, E., Kouretas, G. P., & Laopodis, N. T. (2025). Understanding central bank responses to geopolitical risks: Evidence from the Fed and ECB. Journal of Financial Stability, 80. Article 101452. https://doi.org/10.1016/j.jfs.2025.101452

[65]Christie, W. G., & Huang, R. D. (1995). Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal, 51(4), 31–37. https://doi.org/10.2469/faj.v51.n4.1918

[66]Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking & Finance, 24(10), 1651–1679. https://doi.org/10.1016/S0378-4266(99)00096-5

[67]Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559

[68]Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009). Early-warning signals for critical transitions. Nature, 461(7260), 53–59. https://doi.org/10.1038/nature08227