The Examination of Gordon Model: A Neural Network Approach

Subject Areas : Financial Knowledge of Securities Analysis

Shahnaz Mashayekh ¹ , Nahaleh Hayati ²

1 - استادیار گروه حسابداری دانشکده علوم اجتماعی و اقتصادی دانشگاه الزهرا )س(، تهران، ایران
2 - دانشجوی دکتری حسابداری دانشکده علوم اجتماعی و اقتصادی دانشگاه الزهرا )س(، تهران، ایران

Received: 2014-11-01 Accepted : 2014-12-22 Published : 2016-04-03

Keywords: Gordon model, Neural Network, price-dividend relation,

Abstract :

Abstract: This study tends to model Gordon relation using feed forward neural network approach for Tehran Stock Exchange listed companies. In this research, the examination of Gordon model with a nonlinear approach is discussed and the results are compared with linear regression. The examination of nonlinear Gordon model using neural network has not been considered in the studies as yet. In this research, data for 247 companies and 1135 observations (firm- year) between 2006- 2013 are used (unbalanced panel). Comparative analysis of results for linear regression and nonlinear neural network approaches shows that the coefficient of determination for neural network approach is higher than the coefficient of determination for linear regression; so using nonlinear model can improve prediction power of model and lead to more profitable investment strategies. In the modeling procedure, various structures of network are tested by changing the number of neurons for getting optimal network.

References:

 Ackert, L.F., Hunter, W.C. (1999). Intrinsic Bubbles: The case of stock prices: Comment. American Economic Review, 89 (5): 1372-1376.
 Ackert, L.F., Hunter, W.C. (2001). An empirical examination of the price-dividend relation with dividend management. Journal of Financial Services Research, 19 (2): 115-129.
 Chen, H.J., Shaio-Yan, H., Chung-Long, K. (2009). Using the artificial neural network to predict fraud litigation: Some empirical evidence from emerging markets, Expert Systems with Applications, 36 (2): 1478–1484
 Cuthbertson, K., Nitzsche, D. (2004). Quantitative financial economics: stocks, Bonds and Foreign Exchange. John Wiley and Sons.
 D’Amico, G. (2013). A semi-Markov approach to the stock valuation Problem. Annals of Finance, 2013, 9 (4), 589-610.
 Damodaran, A. (2002). Investment valuation, New York: John Wiley & Sons.
 Gordon, J. M. (1959). Dividend, earning and stock price, Review of Economic and Statistics, 41 (2), 99-105.
 Ferris, K., Petitt, B. (2013). Valuation for mergers and acquistions: An overview. New Jersey, Pearson Education, Inc .
 Gordon, J. M. (1962). The Investment, financing, and valuation of the corporation, Irvin, Homewood, IL.
 Hurley, W.J. (2013). Calculating First Moments and Confidence Intervals for Generalized Stochastic Dividend Discount Models. Journal of Mathematical Finance, 2013, 3, 275-279.
 Kaastra, I., Boyd, M. (1996). Designing a neural networks for forecasting financial and economic time series. Neurocomputing, 10 (3): 215–236 .
 Kaliva, K., Koskinen, L. Dynamic model for stock market risk evaluation. Working Paper.
 Kamstra, M. (2003). Pricing firms on the basis of fundamentals, Federal Reserve Bank of Atlanta, Economic Review, First Quarter 2003. Kiley, M.T. (2000). Stock prices and fundamentals in a production economy, Finance and Economics Discussion Series 2000-05, Board of Governors of the Federal Reserve System (U.S.).
 Kuan, C.M., Liu, T. (1995). Forecasting exchange rates using feedforward and recurrent neural networks, Journal of Applied Econometrics, 10(4): 347-364.
 Madsen, J. B., Milas, C. (2005). The price-dividend relationship in inflationary and deflationary regimes, Finance Research Letters, 2 (4): 260–269.
 Mc Nelis, P.D. (2005). Neural networks in finance, gaining predictive edge in the market. San Diego, CA, Elsevier Acadmic Press .
 Olson, D., Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios, International Journal of Forecasting, 19 (3): 453–465.
 Serrano-Cinca, C. (1996). Self organizing neural networks for financial diagnosis, Decision Support Systems, 17 (3): 227- 238.
 Stewart, G. B. (1991). The Quest for Value: A guide for senior managers. New York, NY: HarperCollins, Publishers Inc.
 Thomas, R., Gup, B. E. (2010). The valuation handbook, valuation techniques from today’s top practitioners, Hoboken, New Jersey. John Wiley&Sons, Inc,.
 White, H. (1988). Economic prediction using neural networks: The case of IBM Stock Prices, in Proceedings of the IEEE Second International Conference on Neural Networks, 2, 451-458, SOS Printing, San Diego.
 Zhang, G., Hu, M. Y., Patuwo, B. E., Indro, D. C. (1999). Theory and Methodology, Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research, 116 (1999): 16-32