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

被引:13
|
作者
Plakandaras, Vasilios [1 ]
Gogas, Periklis [1 ]
Papadimitriou, Theophilos [1 ]
机构
[1] Democritus Univ Thrace, Dept Econ, Komotini 69100, Greece
关键词
machine learning; Support Vector Regression; Geopolitical Uncertainty; POLITICAL RISK; STOCK MARKETS; TERRORISM; RETURNS;
D O I
10.3390/a12010001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Practical machine learning: Forecasting daily financial markets directions
    Henrique, Bruno Miranda
    Sobreiro, Vinicius Amorim
    Kimura, Herbert
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 233
  • [2] A machine learning approach to classification for traders in financial markets
    Wright, Isaac D.
    Reimherr, Matthew
    Liechty, John
    STAT, 2022, 11 (01):
  • [3] The spillover effects of the "Binance Incident" on financial markets: A study based on machine learning approach
    Feng, Lingbing
    Qi, Jiajun
    Liu, Ye
    Wang, Wei
    FINANCE RESEARCH LETTERS, 2025, 71
  • [4] Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
    Wang, Bin
    Lu, Jie
    Yan, Zheng
    Luo, Huaishao
    Li, Tianrui
    Zheng, Yu
    Zhang, Guangquan
    KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, : 2087 - 2095
  • [5] The effects of geopolitical uncertainty on cryptocurrencies and other financial assets
    Νikolaos A. Kyriazis
    SN Business & Economics, 1 (1):
  • [6] Geopolitical risk, economic policy uncertainty and asset returns in Chinese financial markets
    Chiang, Thomas C.
    CHINA FINANCE REVIEW INTERNATIONAL, 2021, 11 (04) : 474 - 501
  • [7] Forecasting Financial Markets using Deep Learning
    Zanc, Razvan
    Cioara, Tudor
    Anghel, Ionut
    2019 IEEE 15TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2019), 2019, : 459 - 466
  • [8] Multitask machine learning for financial forecasting
    Di Persio, Luca
    Honchar, Oleksandr
    International Journal of Circuits, Systems and Signal Processing, 2018, 12 : 444 - 451
  • [9] Advanced Machine Learning for Financial Markets: A PCA-GRU-LSTM Approach
    Liu, Bingchun
    Lai, Mingzhao
    JOURNAL OF THE KNOWLEDGE ECONOMY, 2024,
  • [10] Machine Learning Ensemble Approach for Ionosphere and Space Weather Forecasting with Uncertainty Quantification
    Natras, Randa
    Soja, Benedikt
    Schmidt, Michael
    2022 3RD URSI ATLANTIC AND ASIA PACIFIC RADIO SCIENCE MEETING (AT-AP-RASC), 2022,