Prediction the Return Fluctuations with Artificial Neural Networks' Approach

محورهای موضوعی : Financial Accounting

Masoud Taherinia ¹ ( Department of Accounting, Lorestan University, Economic and Administration Science Faculty, Lorestan, Iran. )
Mohsen Rashidi Baghi ² ( Department of Accounting, Lorestan University, Economic and Administration Science Faculty, Lorestan, Iran. )

تاریخ دریافت : 1397/09/16 تاریخ پذیرش : 1398/02/12 تاریخ انتشار : 1398/02/11

کلید واژه: Prediction, Artificial Neural Networks, Earning quality,

چکیده مقاله :

Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study includes 120 listed companies in Tehran stock securities during 2005 to 2017. Independent variables in this research are market variables (Earning quality, free cash flow) and dependent variable is share return. The obtained outputs from estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks.

منابع و مأخذ:

 [1] Abhyankar, L.S., Copeland, W., Uncovering nonlinear structure in real-time stock-market indexes: the S&P 500, the DAX, the Nikkei 225, and the FTSE-100, J. Business Econ. Statist., 1997, 15, P.1–14. Doi: 10.2307/13 92068

 [2] Balkin, S.D., Ord, J.K., Automatic neural network modelling for univariate time series. International Journal of Forecasting, 2000, 16, P.509-515.  Doi:10.1016/S0169-2070(00)00072-8.

 [3] Brooks, C., Linear and non-linear (non-) forecastability of high frequency exchange rates, Journal of Forecasting, 1997, 16, P.125–145. Doi:10.1002/(SICI)1099-131X(199703)16:23.0.CO;2-T.

 [4] Darbellay, G.A., Slama, M., Forecasting the short-term. Demand for electricity? Do neural networks stand a better chance? International Journal of Forecasting, 2000, 16, P.71–83. Doi:10.1016/S0169-2070(99)00045-X.

 [5] Fama, E.F., French, K.R., Dividend yields and expected stock returns, J. Financial Econ., 1988, 22, P. 3–25.  Doi:10.1016/0304-405X(88)90020-7.

[6] Gencay, R., Stengos, T., Moving Averages Rules, Volume and the Predictability of Security Returns with Feed-Forward Networks; Journal of Forecasting, 1998, 17, P.401-414. Doi:10.1002/(SICI)1099-131X(1998090) 17:5/63.0.CO;2-C.

 [7] Gooijer, J.G.D., Hyndman, R.J., 25 years of time series forecasting. International Journal of Forecasting, 2006. 22, P. 443–473.  Doi:10.1016/j.ijforecast.2006.01.001.

 [8] Karayiannis, N.B., Venetsanopoulos A.N., Artifical Neural Network: Learning Alogorithms, Performance Evaluation, and Application. Kluwer Academic Publisher, Boston. 1993.  Doi:10.1007/978-1-4757-4547-4

 [9] Kumar, P.R., Ravi, V., Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review, European Journal of Operational Research, 2007, 180, P 1–28.  Doi:10.1016/j.ejor.2006.08.043.

[10] Kuo, R.J., Chen C. H., Hwang, Y.C., An Intelligent Stock Trading Decision Support System Through Integration of Genetic Algorithm Based Fuzzy Neural Network and Artificial Neural Network. Fuzzy sets and systems, 2001, 118(1), P.21-45.  Doi:10.1016/S0165-0114(98)00399-6

[11] Nasr, N., Farhadi Sartangi, M., Madahi, Z., A Fuzzy Random Walk Technique to Forecasting Volatility of Iran Stock Exchange Index+. Advances in Mathematical Finance and Applications, 2019, 4(1), P. 15-30. Doi:10 .22034/amfa.2019.583911.1172

[12] Preminger, A., Franck, R., Forecasting exchange rates: A robust regression approach. International Journal of Forecasting, 2007, 23, P. 71–84.  Doi:10.1016/j.ijforecast.2006.04.009.

[13] Qi, M., Predicting US recessions with leading indicators via neural network models. International Journal of Forecasting, 2001, 17, P. 383–401.  Doi:10.1016/S0169-2070(01)00092-9. 

[14] Thawornwong, S., Enke. D., The adaptive selection of financial and economic variables for use with artificial neural networks, Intelligent Systems Center, 1870 Miner Circle 204, Eng Management 2004.  Doi:10.101 6/j.neucom.2003.05.001.

[15] Tacz, G., Neural network forecasting of Canadian GDP growth. International Journal of Forecasting, 2001. 17, P.57–69. Doi:10.1016/S0169-2070(00)00063-7 

[16] Schwert, W., Stock returns and real activity: a century of evidence, J. Finance. 1990, 45, P. 1237–1257.  Doi:10.1111/j.1540-6261.1990.tb02434.x.

[17] Yehuda, N., Penman, S.H., The pricing of earnings and cash flows and an affirmation of accrual accounting. Review of Accounting Studies, 2009, 14(4). P.453-479. Doi:10.1007/s11142-009-9109-4 

[18] Zalaghi, H., Godini, M., Mansouri, K., The Moderating Role of Firms characteristics on the Relationship between Working Capital Management and Financial Performance. Advances in Mathematical Finance and Applications, 2019, 4(1), P. 71-88. Doi:10.22034/amfa.2019.581878.1158

[19] Zamanian, M.R., Sadeh, E., Amini Sabegh, Z., Ehtesham Rasi, R., A Fuzzy Goal-Programming Model for Optimization of Sustainable Supply Chain by Focusing on the Environmental and Economic Costs and Revenue: A Case Study. Advances in Mathematical Finance and Applications,2019, 4(1), P.103-123. Doi:10.22034/amfa .2019.578990.1134

[20] Zomorodian, G., Barzegar, L., Kazemi, S., Poortalebi, M., Effect of Oil Price Volatility and Petroleum Bloomberg Index on Stock Market Returns of Tehran Stock Exchange Using EGARCH Model.Advances in Mathematical Finance and Applications, 2016, 1(2), P.69-84. Doi:10.22034/amfa.2016.527821