Stock market is responsible for trading the shares of public listed companies. Stock exchange facilitates stockbrokers to trade company stocks and other securities. Stock price prediction is one of the most challenging issue which is attracting researchers from many fields including economics, history, finance, and mathematics and computer science. It is difficult to apply simple time-series or regression techniques on stock market because of the volatile nature. Proposed framework attempts to predict whether a stock price sometimes in the future will be higher or lower than it is on a given day. We find a little predictive ability in the short run but definite predictive ability in the long run. Using the social communication network within company among employees, the proposed algorithm can analyze the relationship between communication context and the movements (high and low) of stock price. We have also extended the system by using sentiment analysis for email content which determines whether email context is negative or positive. System gives aggregated result from number of mail exchanged and sentiment of message body.

Stock price prediction is a popular topic in financial studies. Stock market is basically nonlinear in nature and predicting share price is very difficult because there are no specific set of rules to estimate the price of the share in share market. Many methods are used to predict the share price like statistical analysis, time series analysis but none of these methods are considered to be consistently acceptable prediction methods and applying traditional methods may not ensure the accuracy of prediction. Various machine learning algorithms have been used to study the highly unpredictable nature of stock market by capturing repetitive patterns. Various companies have their preferred analysis tool for stock market forecasting and the reason for preference is the accuracy with which they predict. This paper gives brief survey of well-known prediction techniques used for prediction of stock in the stock market.