Commodities Exchange Utilizing Diverse Deep Learning Algorithm

The equity market constitutes a substantial arena with a profound influence on a company's financial standing, commonly known as the stock exchange, serves as a hub for disseminating the latest global business news and developments. Professionals in the field of finance and investors meticulously scrutinize its multifaceted components. Given the sheer volume of information available concerning stocks and investments, customers often find it challenging to make predictions regarding future stock market movements. To address this challenge, customers can leverage deep learning algorithms for stock valuation. This study employs a stock price ensemble model to predict forthcoming stock market events. It harnesses time series data and historical company records to train the algorithm within the stock price ensemble model, enhancing the accuracy of future predictions. Specifically, this work focuses on refining the accuracy of the Long Short-Term Memory (LSTM) algorithm and conducting an in-depth analysis of the dataset provided.