Stock Market Predictions through Deep Belief Neural Network

Abstract

Abstract: Owing to the huge potential returns, the proportionate degree of exposure, and the adaptability of the transaction, a big number of people choose to invest their money in stocks. This is due to the fact that stocks come with both of these factors. If investors are able to successfully foresee changes in stock values, they have the opportunity to generate considerable returns from their financial investments. The value of a stock may be affected by a wide variety of variables, including the current state of the market and the macroeconomic environment as a whole, significant events in the economic and social spheres, investor attitude, and management actions made by the firm. Predicting the value of a company's shares has always been considered as the most challenging and essential component of academic research. This view continues to hold true today. Utilizing mathematical and statistical models is a common component of the traditional approaches used for predicting stock values. These methods have been around for quite some time. Despite this, these strategies are not enough for dealing with the intricate dynamics of the stock market, which are always evolving. The fast development of computer technology over the last several decades has led to an increase in the prevalence of the use of machine learning among academics. Therefore, this paper outlines an effective strategy for stock market prediction that utilizes K Nearest Neighbors, Linear Regression, along with Deep Belief Network and Decision making.