Implementation of Web mining in predicting the stock market price of chemical products group trend

Market forecasting, like stock's market, with high volume of transactions have an effect on researchers and investors and get their attention. Important issue factors in any investment decision are risk and turnover. Understanding market momentum gives the ability to predict future movements. The ability to predict in a market economy, enables to achieve a higher turnover by reducing risk and avoiding financial losses. News plays an important role in the process of evaluating the current stock price. The development of data mining methods, computational intelligence and machine learning algorithms have led to new models of prediction. phpCrawler is a php base content crawler that use of DomCralwer and Guzzle packages for storing web data. With this tool, the news releases are stored and categorized from 17 News Agency.    Then, by using text mining and Support Vector Machine with different kernel, predict stock price direction. In this research use 948990, news has been stored from 17 news agencies. More than 300,000 news regarding political and economic categories were used and stock prices of chemicals between November 2017 till March 2018 (123 trading days) were studied. The results show that by using the linear kernel Support Vector Machine algorithm, the prediction accuracy of average price movement reached to 83%. Using nonlinear kernel Support Vector Machine with poly kernel increased two percent prediction accuracy to 85% on average and other kernel had poorer prediction.