Forecasting Stock Prices with Long Short-Term Memory (LSTM) Networks : A Deep Learning Approach

Abstract

Stock price prediction is essential yet not easy because of the high volatility of the stock prices, non-linearity, and non-stationarity of the financial markets. In this case, the current research examines a robust architecture developed by LSTM networks, a type of deep learning architecture renowned for its effectiveness in analyzing sequence data and its resistance to the gradient vanishing problem. The overall goal is to improve predictive performance in given settings by overcoming the known contemporary issues, which include the inability of positive models to accommodate random variance and other complex market dynamics. The proposed model improves the results further than prior research in terms of skillful noise reduction, feature normalization, and dynamic walk-forward validation, achieving better and more accurate stock price prediction. The historical price information of stocks forms the core of model training and evaluation. Outcomes are measured based on RMSE and MAE, and through these measures, LSTM proves to be superior to conventional approaches. As an entirely original concept, this method serves as a verifiable and practical asset, providing a valuable lens for examining market trends more closely and making informed decisions.