A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

The prediction of stock market prices represents a significant challenge due to its volatile nature, influenced by unpredictable economic factors, company performance, and market sentiment. The assurance of these forecasts or the associated risk with these price estimations plays a pivotal role in the decision-making process. Existing models have either focused on stock price prediction or risk analysis but rarely integrate both, leaving a gap in providing a comprehensive tool for investors. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model uses a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles as input. The stock price is predicted by LSTMs on individual stock prices, while the confidence is represented by a risk value calculated using XGBoost and LSTM output. We performed sentiment analysis and subjectivity analysis to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. Our study demonstrates the effective integration of risk analysis with stock price forecasting, illustrating that deep learning techniques are suitable for melding risk assessment with the prediction of stock prices.