Stock Market Price Prediction Using Attention-Based Mechanism

The ability to foresee stock market values is extremely valuable because it allows traders and investors to strategically buy and sell equities at the best periods, which can result in significant financial rewards. Thus the proposed work aims to use a machine learning algorithm to predict the stock market values. Although there are several studies that focus on stock price prediction, the models used only used news or numerical data, and are not efficient because of the market's complexity, volatility, and rapid swings. Because of the nonlinear interactions between market elements, the stock market is inherently complicated, making it difficult for classical machine learning models to address such complexities. Thus the proposed work adopts attention-based mechanisms to address the issue of handling complex market dynamics and long-term dependencies. As real-time news and stock market data become more widely available, attention-based models have become effective tools for analyzing and locating important components in such enormous datasets, leading to more precise forecasts. The proposed work uses attention-based mechanism towards stock market prediction and demonstrates the performance with a case study with RMSE value of 0.0133 and r2 score of 0.9892