Machine Learning-Based Early Detection of Financial Crashes Using Multi-GRU Networks

Abstract

Financial crises and stock market disasters are major causes of economic instability that affect governments, companies, and investors worldwide. Traditional statistical forecasting techniques often fail to capture the complex and dynamic behaviour of financial markets because of the high volatility, nonlinear patterns, and temporal correlations of financial data. To overcome these limitations, this study proposes a Machine Learning-Based Early Detection System for Financial Crashes using Multi-Gated Recurrent Unit (Multi-GRU) Networks. The proposed approach analyses past financial data, market indicators, trade volumes, and economic factors using deep learning techniques to identify early warning signs of financial instability. The Multi-GRU architecture aims to improve forecast accuracy by finding long-term connections and sequential patterns in time-series financial data. Lightweight optimisation approaches and residual learning are used to reduce computer complexity and enhance model performance. The system employs preprocessing, feature extraction, normalisation, and sequential learning to classify market conditions into stable and crisis stages. Experiments show that the proposed Multi-GRU model performs better in terms of prediction accuracy, loss rate, and convergence speed than traditional machine learning methods such as Support Vector Machine (SVM), Random Forest, and conventional Recurrent Neural Networks (RNN). The proposed framework provides an advanced financial early warning system that can assist legislators, financial institutions, and investors in lowering economic risks and taking preventative measures. The study shows the effectiveness of deep learning approaches in financial crisis prediction and promotes the development of reliable and scalable financial forecasting systems.