Prediction of Stock Price Using Machine Learning Techniques

The paper presents a study on stock price prediction using a Long Short-Term Memory (LSTM) model and other machine learning techniques like Random Forest (RF). The LSTM model was trained on historical stock price data and technical indicators to forecast future stock prices accurately. Evaluation metrics demonstrate the LSTM model's superior predictive accuracy compared to benchmark models. The findings highlight the potential of LSTM and machine learning for stock price prediction, offering implications for financial analysts and traders. The results show that both RF and LSTM can predict stock prices reasonably, with LSTM performing slightly better than RF. Through LSTM, R2=0.99, MSE=0.029, RMSE=0.49 has been calculated. The work suggests that deep learning methods such as LSTM can be a promising approach for stock price prediction, especially when dealing with complex and noisy data.