Predicting the Industry Index Volatility of Companies Listed in Tehran Stock Exchange, Emphasizing on Corporate Financial Variables Using Support Vector Machine

The purpose of study is to investigate comparative ability of accounting information to predict indices volatility of companies listed in Tehran Stock Exchange using intelligent methods including Support Vector Machine, Artificial Neural Network and classic Logistic Regression model. Sample of study includes 91 companies listed in Tehran Stock Exchange that have been classified in 9 industrious groups during time period of 2003-3013.Considering 11 corporate financial variables, study results show that despite predicting ability of around 60% by Support Vector Machine and Artificial Neural Network, there is significant difference between actual and predicted results. Classic Logistic Regression model also can explain only 4% industries’ indices volatility using selected 11 corporate financial variables. Finally, although intelligent methods are superior to classic methods, accounting information solely are not well-explainer variables for predicting industry index volatility and variety of variables such as financial, political, economical are effective in predicting industry index volatility.