In the context of accelerated globalization, the volatility and complexity of financial markets are increasing, which is closely related to political and economic factors. In order to better understand this complex relationship, this study uses a Bayesian network model to construct a quantitative indicator system that includes factors such as political stability, economic policies, and international relations, and simulates and predicts the dynamics of financial markets. The model training utilized global financial market data and related political and economic event data from the past decade, and optimized model parameters through machine learning algorithms to improve prediction accuracy. The study not only reveals the impact mechanism of political and economic changes on financial market volatility, but also provides new perspectives and tools for market participants to make wiser decisions in complex and ever-changing market environments. In addition, the model can also provide decision support for policymakers, helping them evaluate their long-term impact on financial markets when formulating economic policies. The Economic Policy Change Index fluctuates between 1.0 and 3.0, indicating that the government is constantly adjusting its economic policies to cope with different situations. This study not only expands the theoretical and methodological framework of political economy and financial market dynamics research, but also provides specific analytical tools and decision support for practical operations, which has important academic value and practical application significance.

Gao Luwen. Financial Market Dynamics from the Perspective of Political Economy: Using Bayesian Network Models. Academic Journal of Business & Management (2024) Vol. 6, Issue 7: 77-84. https://doi.org/10.25236/AJBM.2024.060711.

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