The effect of conflicts on local markets

A deeper look into the effect of the number of conflicts on local financial markets

Todays writer:

My name is Chiel Evertz, I am an Econometrics student at the University of Groningen. I finished my bachelor's in Econometrics and Operations Research, and I will start with the master in Econometrics in september. Within this master I follow the track actuarial science and financial econometrics. I will combine this master with a master finance with focus area in quantitative finance at the University of Groningen, which I will start next year. My studies focus on analyzing financial data, modelling processes, and forecasting. I have have previous financial knowlegde from Groningen Investment Team and personal interest in financial markets.

Conflicts seem to be everywhere as of now. Assuming these conflicts quickly affect the economy and financial markets seems rather straightforward, however, is this truly the case? Since data is increasingly more readily available, the effect of these conflicts can be evaluated using public data. By inspecting two regions subject to much conflicts in recent years, we try to find out more. Do changes in the amount of daily conflicts directly result in downward movements in local financial markets?

The data

Armed Conflict Location & Event Data Project (ACLED) (2025) is an organization that collects information on all recorded conflicts in the world. They have an extensive database with an easy-to-use export tool, which is updated weekly. Using this data we try to estimate the short-term effects of conflicts in a country on its local financial markets. In this article we focus on two specific regions in the world. First we look at the conflicts in Ukraine and its effect on neighboring countries. Then we look at the conflicts in Israel and the effect on the middle east. To look at the impact of conflicts in Ukraine on neighboring financial markets we use ETFs of neighboring countries as proxies for the financial markets. Since three neighboring European countries have publicly listed ETfs we use a weighted ETFs based on market capitalization of these countries as a proxy for local financial markets near Ukraine. The countries used for Ukraine are Poland, Hungary, and Romania. To look at the effect of conflict in Israel on the local financial markets we create another weighted ETF. We use countries in the middle east that have publicly traded ETFs as a proxy for the performance of those markets. The selected countries are Saudi Arabia, United Arabic Emirates, Qatar, and Egypt. The weighted ETFs are created by weighing the ETFs returns by the respective countries market capitalization. More information on the exact construction of these weighted ETFs is in the Appendix.

Now that we have created the weighted ETFs, we will download the conflict data from Armed Conflict Location & Event Data Project (ACLED) (2025). They have recorded all available conflicts starting from 01-01-2018. There is a large quantity of recorded conflicts, and their code book and user guide contains specific definitions. To summarize, the conflicts contain these main event types: battles, remote violence, explosions, riots, demonstrations, and violence against civilians. Further specifications and sub-events can be found in their said guidebook.

What does the data imply?

We have made a graph of the weighted ETF and the recorded conflicts in the selected countries in Figure 1 and Figure 2. The conflict data is represented by the weighted average over the past 5 days of the number of daily conflicts. We only show the part of the graph for which we have both conflict data and all off the ETF data. It would be intuitive to assume a big increase in the number of conflicts leads to lower ETF prices. If we look at both figures, this does not always happen. In Figure 1 we see the conflicts in Israel in orange, and the weighted middle east ETF in blue. A big increase in conflicts was followed by a drop in the ETF price in October 2023. In other moments a big increase of conflicts did not change the market or there was an increase. A similar pattern can be seen in Figure 2 for conflicts in Ukraine. The increase of conflicts at the end of 2024 are followed by some downward movement. But the increased conflicts in 2025 is followed by upwards movement. Visual inspection is no strong evidence, so we will perform some basic analysis to inspect the correlation.

Figure 1

Figure 2

Does a simple analysis show the same?

We want to dive deeper into the effect of conflicts on the ETF returns. We choose to use a regression model to analyse the time-series data. The model estimates the effect of the explanatory variables on the daily ETF returns. We have chosen two additionally explanatory variables besides the amount of conflicts recorded, based on existing literature. The price of oil influences stock prices and is correlated with conflict; therefore, we include the oil price as explanatory variable (Osah and Mollick, 2023). And gold returns are common to include since gold is correlated with uncertainty, like Yilmaz and İlhan (2022) who also use gold returns in their regression. We use time series data to run a regression of the daily change of the moving average of conflicts, and the daily oil and gold log return on the log returns of the weighted ETFs. The regression equation is given below:

∆ log(ETFt) = β0 + β1 conflictst + β2 ∆ log(oilt) + β3 ∆ log(goldt) + ϵt (1)

Note: ∆ denotes first differences (day-to-day changes), and log denotes natural logarithms. Newey-West standard errors are applied to correct for autocorrelation and heteroskedasticity, with maximum lags set to 5.

Results of the analysis

Results of the regression are given in table 1. Surprisingly, the effect is notably different for both regions. The change in the amount of conflicts has a significant negative effect on the local financial markets around Ukraine. However, conflicts in Israel have no significant effect on local markets. The effect is centered around zero and even slightly biased towards a positive effect. This might say something about the heterogeneity of the effect of conflicts on the financial markets, in each region the effects could be different. Israel has endured many conflicts in the past, this could have influenced the effects of conflicts on financial markets today. Yilmazkuday (2024) found a increase in global geopolitical risk reduces stock prices in Israel a year after the shock. It could be that the effect of the daily conflicts in Israel takes longer to reach the financial markets and do not influence markets the same day.

Conclusion

We conclude that variation in the number of daily conflicts in Ukraine has a significant negative effect on neighboring country's financial markets. While, variation in the daily conflicts in Israel have no significant direct effect on local financial markets. For investors, these results suggest that markets near Ukraine might require more monitoring after days of heightened conflicts, which occurs often in recent years. And assets in the middle east might have some short-term resilience to changes in the amount of daily conflicts, panic-selling after an increase in conflicts might not be necessary. The effects of conflicts can differ per region as we have shown. If assets in your portfolio are subject to conflicts in their respective region, each asset's situation should be inspected carefully.

Appendix

Weighted ETFs

The weight wi of country i in the market-capitalization-weighted index is calculated as:

wi = MCi / ∑(j=1 to N) MCj

where:

• MCi = market capitalization of country i,

• N = total number of countries in the index,

• ∑(j=1 to N) MCj = total market capitalization of all countries.

The market capitalization per country are pulled from Wikipedia contributors (2025). Note these data are not all coming from the same year, but acquiring all data from all countries is quite difficuly. This will serve as a good approximation. The specific ETFs chosen for each country are listed in the table below. The selected ETFs had the highest volume out of the available ETFs. The data was downloaded from Investing.com (2025).

References

Armed Conflict Location & Event Data Project (ACLED) (2025). Acled data export tool. https://acleddata.com/data-export-tool/. Accessed on June 27, 2025. Data range: January 1, 2021 – June 20, 2025.

Investing.com (2025). Historical data for middle east etfs. Accessed: 2025-07-07.

Osah, Theophilus Teye and Andre Varella Mollick (2023). Stock and oil price returns in international markets: Identifying short and long-run effects. Journal of Economics and Finance 47 (1), 116–141.

Wikipedia contributors (2025). List of countries by stock market capitalization — Wikipedia, the free encyclopedia. [Online; accessed 7-July-2025].

Yilmaz, Hülya and Bülent İlhan (2022, 08). Determinants of stock market indices: An analysis of emerging markets of brazil, mexico, russia, and turkey. EMAJ: Emerging Markets Journal 12, 26–38.

Yilmazkuday, Hakan (2024). Geopolitical risk and stock prices. European Journal of Political Economy 83, 102553.