A review on prediction of stock market using various methods in the field of data mining

Abstract

Objectives: In the current emerging competitive market, predicting the stock returns as well as the company's financial status in advance will provide more benefits for the investors in order to invest confidently. Stock prediction can be done by using the current and previous data available on the market. Methods: The performance metrics that need to be attained in case of stock prediction are accuracy, scalability and less time consumption. There are many researches done so far in order to predict the stock market to achieve the defined metrics. Many models have been available in the field of data mining for predicting the stock market such as if-then-else rules, Artificial Neural Network (ANN), Fuzzy systems, Bayesian algorithm and so on. Findings: In this paper, the various methods available and used for predicting the stock market are discussed. This survey helps to know which technique is the best to use in the field of predicting stock market in the area of mining. Applications: Forecasting and predicting the trends of market is the most important applications of stock market. It also uncovers the future market behavior which always helps the investors to understand when and what stocks can be purchased for the growth of their investment. For this reasons, many of the researches have been done so far in the area of analyzing the stock market using data mining.