Hybrid Deep Learning and Econometric Models for Financial Market Forecasting: A Review of Emerging Approaches and Their Impact on Trend Prediction

Financial markets forecasting has been a challenge since it is difficult, due to nonlinearity and dynamism of financial data. Traditional econometric frameworks like ARIMA and GARCH are often widely used and regularly fail to capture complicated trends. Combination of deep learning technology with the econometric models which brings about hybrid models have also emerged as an effective approach to enhance forecast accuracy and strength. This literature review provides a very comprehensive analysis of the hybrid deep learning and econometric models to predict in the financial markets. We refer to various hybrid approaches that can be used, their applications, benefits, and challenges through this far-ranging study of recent studies. The findings prove that hybrid models can significantly improve predictive power because they can find both nonlinear and linear patterns. However, there are remaining opportunities to work on approaches to address the balancing of the concepts of model flexibility and interpretability, solving the causal inference and causal endogeneity problem, and effective combination of domain knowledge as the avenues of further research.