Proposing a Novel Framework for Prediction of Stock using Machine Learning

Stock prices are highly volatile, dynamic, and non-linear, making it very difficult to predict the exact price at any given time. In addition, stock prices are influenced by several factors, such as political conditions, the global economy, unexpected events, company financial performance, and more. Up to this point, various machine learning techniques have been employed for stock prediction; however, none of these techniques can accurately predict stock prices due to the high volatility in stock prices. Machine learning approaches, such as random forest, SVM, KNN, and logistic regression, represent some of the algorithms used for stock prediction. This paper aims to propose a new framework based on machine learning and deep learning for stock prediction. The prediction relies on the company’s stock fundamentals, past performance, related stocks in the same sector, and other relevant factors.