Market Dynamic

Market dynamics research aims to understand and predict the complex behaviors of economic systems, focusing on price formation, agent interactions, and overall market stability. Current research employs diverse computational models, including agent-based models, neural networks (like CNNs, LSTMs, and hybrid architectures), and machine learning algorithms (such as XGBoost), to simulate and analyze market behavior across various asset classes (commodities, cryptocurrencies, stocks). These models are applied to optimize trading strategies, forecast prices, and improve understanding of market responses to crises or policy changes. The insights gained have significant implications for financial modeling, risk management, and the development of more effective economic policies.