News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models
This is an incremental study for traders and analysts in Indian markets, applying existing methods to new data without claiming broad breakthroughs.
The paper tackled stock price forecasting in Indian markets by comparing advanced deep learning models like LSTM, Facebook Prophet with LightGBM, and SARIMA, integrated with sentiment analysis from tweets and financial sources, but did not report specific accuracy numbers or results in the abstract.
Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations.