Intelligent Financial Forecasting with Granger Causality and Correlation Analysis Using Bayesian Optimization and LongShort-Term Memory

Abstract

Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market trend and asset price predictions while improving the robustness of forecasts for economic indicators, which are essential for strategic positioning, risk management, and policy formulation. The methodology involves leveraging the strengths of both Bayesian optimization and LSTM networks, allowing for more effective pattern recognition and forecasting in volatile market conditions. Key contributions of this work include the development of a novel hybrid framework that demonstrates superior performance with significantly reduced forecasting errors compared to traditional methods. Experimental results highlight the model's potential to support informed decision-making amidst market uncertainty, ultimately contributing to improved market efficiency and stability.