Risk Prediction in Financial Markets Using Hybrid AI and Time Series Forecasting Models

The ability to properly anticipate potential risk is essential when making investment decisions since financial markets are typically hard to predict. This work presents a new method for risk prediction improvement that combines traditional time series forecasting models with artificial intelligence. The suggested model combines Long Short-Term Memory (LSTM) networks with Autoregressive Integrated Moving Average (ARIMA) to detect nonlinear relationships and statistical patterns in financial data. Feature selection methods are utilized to reduce the input data, hence the precision in making predictions. The hybrid model is trained and cross-validated on past market data, and its performance is tracked using assessment metrics that quantify key parameters, including Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The experimental findings show that our hybrid model produces more accurate risk predictions than the LSTM or ARIMA models alone. The results of this study, providing evidence of AI-based models' effectiveness in the management of financial risks, have considerable importance to investors, legislators, and analysts. As the suggested strategy enables proactive management of risks as well as judicious investment-making, it facilitates stability in markets. The hybrid model achieved a predicted accuracy of 92.7%.