Machine Learning Approach for Stock Market Prediction

Abstract

In a rapidly innovating environment, the evolution of technology has become fast-paced and more complex. One of these innovations is artificial intelligence (AI), enabling machines to think and act like humans. The machine learning (ML) algorithms are one group of AI that trains machines to learn specific processes and use this knowledge to deliver output. As is the case in ordinary life, billions of dollars are traded daily, and behind each dollar is a speculator looking to profit somehow. Daily, whole organizations rise and fall in response to market behavior. Since its inception, financial experts have sought to forecast the stock market. Thus, machine learning techniques are being used and evaluated in financial markets. If a financial expert can predict market trends accurately, this presents an alluring promise of wealth and influence. Therefore, it is unsurprising that the Stock Market and the problems that accompany it find their way into the open creative mind whenever they spiral out of control. The paper's primary objective is to determine the optimal model for forecasting financial exchange rates. While considering various methods and variables, we found that processes such as random forest and support vector machines had not been fully explored in light of the growing interest in artificial intelligence. This research uses ML methods, the Artificial Neural Networks (ANN), and Support Vector Machines (SVM) to predict stock indices with the aid of their correlated index. These ML approaches are compared with the traditional forecasting methods for predicting the stock value.