Comparative Study: Stock Prediction Using Fundamental and Technical Analysis

Abstract

The stock market, which is also called the equity or the share market, is a place where the shares of publicly listed companies are traded. The price of a particular stock can be approximated using two types of analysis, technical and fundamental analysis. The growing applications of machine learning have made it possible to be applied to the task of prediction using historical stock data, namely OHLC (open, high, low, and close) data and publicly listed companies’ annual and quarterly financial reports. In this survey paper, we explore the current advancements in this field using different Artificial Neural Network architectures such as Long Short Term Memory Networks, Recurrent Neural Networks, Support Vector Machines, Deep Learning, and Machine Learning techniques. These different methodologies and architectures are compared on how effective the existing systems are for the task of stock price prediction in the view of long-term investing. This paper also discusses how these techniques could be used to develop a system that would help investors decide to invest.