Published Paper Details:

Forecasting share prices is a complex mission given the dynamic and non-stationary nature of capital markets. Accurate predictions are challenging due to the inherent volatility and non-linear patterns in stock market behavior. This involves expecting the future value of a company's stock or other financial instruments traded on exchanges, aiming to enhance shareholder's gains. The latest success of applying Artificial Intelligence in finance has led to increased reliance on stochastic models for market predictions. Stock market prediction, a longstanding research focus, incorporates various machine learning techniques and diverse datasets. While many studies utilize historical stock statistics and relevant real-time data (e.g., oil and gold prices), few investigate the integration of Economic news in forecasting stock price trends. To address this gap, A system is proposed that leverages deep learning techniques and the LSTM (Long Short-Term Memory) algorithm for stock price prediction based on historical data of previous stock prices.