Addressing Market Volatility and Economic Factors in Long-Term Stock Price Prediction with Deep Learning

This paper explores the possibility of LSTM networks to improve predictability in the long-term stock prices during economic volatility. Conventional machine learning models fail to handle the nonlinear complexities associated with financial markets, especially when external economic factors are considered. We add econometrics factors like inflation rate, interest rate, and the rate of growth of the Gross Domestic Product for LSTM-based models. Those further enhanced the accuracy of such models than traditional models alone. It was observed during our experimentation that the application of LSTM models, especially including epoch optimization, is able to generate key temporal dynamics and significant implications that economic variables would have an influence on. This observation comes as a result of increased values in metrics as MAE and RMSE. It successfully balances the accuracy with computational efficiency and demonstrates a reliable alignment of actual and predicted stock prices using some varieties of testing intervals. Our findings indicate the ability of LSTM in tracking long-term trends of stock and underscore the economic factor inclusion for more robust applications in financial forecasting. Future work would aim at including further economic variables or exploring hybrid architectures further in order to refine predictive performance.