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Algorithms 2019, 12(1), 1; https://doi.org/10.3390/a12010001

The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
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Received: 16 November 2018 / Revised: 8 December 2018 / Accepted: 17 December 2018 / Published: 20 December 2018
(This article belongs to the Special Issue Algorithms in Computational Finance)
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Abstract

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk. View Full-Text
Keywords: machine learning; Support Vector Regression; Geopolitical Uncertainty machine learning; Support Vector Regression; Geopolitical Uncertainty
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Plakandaras, V.; Gogas, P.; Papadimitriou, T. The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach. Algorithms 2019, 12, 1.

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