Integrated Stock Forecasting Harnessing Technical Indicators Sentiment Analysis Including Inflationary Patterns

Integrated stock forecasting is an advanced method for predicting stock prices by combining multiple data sources and analytical techniques. It integrates fundamental and technical analysis with sentiment data and machine learning for more precise forecasts. A key challenge is handling vast, high-dimensional data from diverse sources, making it difficult to develop integrated predictive models. Previous research has used statistical methods and machine learning, including ARIMA, SVM, and LSTM, combined with sentiment analysis from social media and financial news. This paper employs an integrated approach incorporating technical indicators and sentiment analysis within inflation data. Using historical data from the Bombay Stock Exchange (BSE), a Random Forest model achieved an RMSE of 0.0244, MAE of 0.018, and an R-squared value of 0.988, indicating strong predictive accuracy. The study found that combining economic and sentiment factors improved stock price projections, demonstrating the effectiveness of an integrated approach in financial forecasting.