Deep Learning-Based Stock Price Forecasting for Business Management: A Hybrid Approach Using LSTM and Sentiment Analysis

Abstract

In the dynamic environment of today's financial markets, genuine stock price prediction is important for efficient corporate management and strategic planning. This paper presents a hybrid deep learning model combining LSTM with sentimental analysis to improve the prediction of stocks prices. Standard time series models frequently fail to account for intricate temporal correlations and external market dynamics, e.g., investor mood. We address this problem by using historical price data along with sentiment indicators from live financial news headlines and social media posts captured by natural language processing algorithms and supplied with pre-trained language models. The LSTM subcomponent monitors long-range temporal dynamics and short-run fluctuations, and the sentiment analysis unit helps to set a trading mood of the market, making the model sensitive to outside jolts. Experimental evaluations on benchmark data sets show that the hybrid model gains a big improvement over the single model LSTM and traditional machine learning models, such as RMSE and MAPE. It provides an integrated framework to investors and business management for decision making using both quantitative and qualitative indication in market play. The research provides evidence of the promise of hybrid deep learning architectures in financial prediction, and paves the way for more intelligent and adaptive decision-support systems.