Stock Predator: ML-driven Stock Prediction
Authors:-Anushka Sakure, Shrishti Mishra, Riya Das, Reetika Roy
Abstract-:Stock price prediction remains a challenging task due to the inherent volatility and non-linear nature of financial markets. This study proposes a deep learning approach using Long Short-Term Memory (LSTM) networks to forecast stock prices, leveraging their ability to model temporal dependencies. Historical data from the S&P 500 index (2010–2023) was pre-processed, normalized, and used to train an LSTM model. The model’s performance was evaluated against ARIMA and SVM using RMSE, MAE, and directional accuracy. Results indicate that the LSTM model outperforms traditional methods, achieving an RMSE of 1.82 and 87% directional accuracy. This work highlights the potential of LSTM in financial forecasting and algorithmic trading strategies.
