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Stock market domain is a promising domain in Machine learning approach. Internet technologies helps to gathered various kinds of structured and unstructured data, such as blogs, message threads, an enormous amount of technical data, company meetings, quarterly results of company etc has been accumulated. Currently, many applications have used data mining techniques or machine learning to exploit such data. This study is a comprehensive overview on Machine Learning. Unlike other domains Stock market data has a correlation among different features, and its characteristics change variously with time, political and natural changes. In this paper, we discuss a various methodology for predicting power to manipulate stock prices. However, there is no structured defined frame for solving the problem in stock market domain. We believe the existing ambiguity on the topic is due to its interdisciplinary properties that require both factors affecting the economics as well as artificial intelligence. We review the work related to prediction of stock market based on text-mining and gives an outer picture of the generalized modules. Our comparative analysis expands the theoretical and technical aspects behind each.
Traders & Investors typically uses tools of two classes of to find stocks to buy-sell; technical and fundamental analysis, both helps to analyze and predict the change in demand-supply (Turner, 2007). This change in demand-supply forms a basis for most forecasting. If buyers are more than seller for a stock, the theory states that the price will increase, and vice versa. The capability to visualize these changes in demandsupply thus gives the ability to trader to book a profitable entry and exit, which is the aim of analysis. Analysis of fundamental requires the study of company basics such as balance sheet, expenses and revenues, annual return, market position etc. While Technical analysis is deals with volume, price data,
mainly volume spikes and price patterns (Turner, 2007; Murphy, 1999).
The major structures in mining of online text have been studied and some of the key lacunas have been recognized. The assessment was ended on three key aspects, precisely: pre-processing of records, machine learning and the machinery to assess; which breakdowns into many areas. It is a big task to make available a comprehensive review from numerous aspects of data analysis. Here the papers, we studied the current developments in market forecast models and it has the capability to forecast the movement of market more accurately compare to other techniques. The Machine Learning supports to learn relationships among the data which supports the systems such as stock markets more accurately.
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