Quantitative analysis of stock market prediction for accurate investment decisions in future

Abstract

Stock market is considered the primary indicator of a country's economic strength and development. Stock Market prices are volatile in nature and are affected by factors like inflation, economic growth, etc. Prices of a share market depend heavily on demand and supply. High demanded stocks will increase in price whereas heavily sold stocks will decrease in price. Fluctuating stock prices affects the investor's belief and thus there is a need to predict the future stock value. The objective of this review is to predict the stock market prices in order to make more informed and accurate investment decisions. Recent trends in stock market prediction are surveyed. Different types of machine learning classifiers and their respective variants. Various approaches and the results of past years are compared based on methodologies, datasets and efficiency and then it is represented in the form of a Graph. The survey describes different theories and conventional approaches to stock market prediction. Along with it, it discusses recent machine learning techniques along with pros and cons of each technique for effectively predicting the future stock prices followed by various researchers.