Document Type : Research Paper
Authors
Abstract
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.
Keywords
قیمت و بازدهی آتی سهام، فصلنامه علمی پژوهشی مطالعات تجربی حسابداری مالی،
1393 ، شماره 42
نمازی، محمد و رضایی، غلامرضا. بررسی اثرات کیفیت اقلام تعهدی و مربوط بودن
اطلاعات مالی بر هزینه های نمایندگی شرکت های پذیرفته شد ه در بورس اوراق
، بهادار تهران، فصلنامه علمی پژوهشی مطالعات تجربی حسابداری مالی، شماره 44
1393
نیکو اقبال، علی اکبر، گندلی علیخانی، نادیا و نادری، اسماعیل، ارزیابی مدل های شبکه
عصبی مصنوعی ایستا و پویا در پیش بینی قیمت سهام، فصلنامه علمی پژوهشی دانش
1392 ، مالی تحلیل اوراق بهادار، شماره 22
همت فر، محمود، حسینی، علی اکبر، شاه ویسی، فرهاد و نجفی، یوسف. روابط خطی و
غیرخطی بین متغیرهای حسابداری و بازده سهام شرکت های صنعت خودرو و ساخت
1390 ، قطعات، پژوهشنامه حسابداری مالی و حسابرسی، شماره 12
Alavi rad, A. Macroeconomic variables and stock market interactions:
evidence from Iran, International Journal of Economics &
Financial Studies, (3)(1), 2011
Bhanot, K. Anatomy of a government interventions in index stocks:
Price pressure or information effects?, Journal of business,(79)(2), 2006
Gan, C. Macroeconomic variables and stock market interactions: Newzeland evidence, Investment Management and Financial Innovations,(3)(4),2006
Huang, w., Nakamori, Y. and Wang, S. Forecasting stock market movement direction with support vector machine. Elsevier, Computers & Operations Research, (32),2005
Kim, K. Financial time series forecasting using support vector machines, Neuro computing, (55),2003
Sangami, H., Hassan, M. Macroeconomic Variables on Stock Market
Interactions: The Indian Experience, Journal of Business and
Management,(11),2013
Shaverdi, M., Fallahi,S. and Bashiri, V. Prediction of stock price of Iranian petrochemical industry using GMDH-Type neural network and genetic algorithm. Applied Mathematical Sciences, (6),2012
پیش بینی نوسان شاخص صنعت شرکتهای پذیرفته شده در بورس... 121
Tay, F. E. H. and Cao, L. Application of support vector machines in financial time series forecasting. Omega (29),2001
Wei, Z. A svm approach in forecasting the moving direction Chinese stock indices, Department of industrial and systems engineering, Thesis of Master of Sciences, Lehigh University ,2012
)