Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting

Document Type : Research Paper

Authors

1 Associate Prof. of Accounting, University of Mazandaran, Mazandaran, Iran

2 Assistant Prof. in Industrial Management, University of Mazandaran, Mazandaran, Iran

3 MSc. in Accounting, University of Mazandaran, Mazandaran, Iran

Abstract

Accurate forecasting of stock prices according to high volatility and inherent risk of stock market is a major concern of investors and financial analysts, hence applying novel approaches to predict the stock priceisan inevitable necessity. Accordingly, the purpose of this research is to compare the performance of forecasting models such as neural network with classical model and introducing appropriate model to forecast tomorrow stock price. The daily market prices data and financial indicator have been used as input variables for designing neural network model and daily closing price data set as input variable for designing ARIMA and also tomorrow's closing price is considered as output variable from 2011to2014. The results show that the Bayesian neural network represents less error sand higher Predictive power than the ARIMA model. The findings indicate the efficiency of Bayesian neural network incapture short-term investment opportunities and also can help investors to choose the appropriate portfolio and to obtain more returns.

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Asadi, S., Hadavandi, E., Mehmanpazir, F. & Nakhostin, M. M. (2012). Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction. Knowledge-Based Systems, 35, 245-258.
Assaf, N. A. (2011). Mercado financeiro. (10th ed.). So Paulo: Editora Atlas.
Azar, A. & Karimi, S. (2010). Neural Network Forecasts of Stock Return Using Accounting Ratios. Journal of financial research, 11(28), 3-20. (in Persian)
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. springer.
Chen, M. Y. & Chen, B. T. (2015). A hybrid fuzzy time series model based on granular computing for stock price forecasting. Information Sciences, 294, 227-241.
Chester, M. (1993). Neural networks: a tutorial. Prentice-Hall, Inc.
Dash, R. & Dash, P. (2016). Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified technique. Expert Systems with Applications, 52, 75-90.
Garshasbi, A. (2010). A comparative study of stock returns predictors in Tehran Stock Exchange and New York Stock Exchange using neural network approach and linear regression. Master Thesis, University of Mazandaran.
(in Persian)
Hanafizade, P. & Jafari, A. (2010). The hybrid model of feed forward and kohonen’s self organizing artificial neural networks in predicting the stock price. Journal of Industrial Management Studies, 8(19), 165-187. (in Persian)
Hykin, S. (1999). Neural Networks: A Comprehensive Foundation. Printice-Hall, Inc., New Jersey.
Khashei, M. & Bijari, M. (2010). An artificial neural network model for timeseries forecasting. Expert Systems with Applications, 37(1), 479-489.
Li, X., Xie, H., Wang, R., Cai, Y., Cao, J., Wang, F. ..., & Deng, X. (2016). Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1), 67-78.
Mohammad Alizadeh, A., Raie, R., Mohammadi, Sh. (2016). Prediction of stock market crash using self-organizing maps. Journal of financial research, 17(1), 159-178. (in Persian)
Monadjemi, S. A., Abzari, M. & Rayati Shavazi, A. (2009). Modeling of stock price forecasting in stock exchange market using fuzzy neural networks and genetic algorithms. Journal of Quantitative Economics, 6(22), 1-26.
Mostafa, M. M. (2010). Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait. Expert Systems with Applications, 37(9), 6302-6309.
Murat, Y. S. & Ceylan, H. (2006). Use of artificial neural networks for transport energy demand modeling. Energy Policy, 34(17), 3165-3172.
Namazi, M. & Kiamehr, M. M. (2008). Predicting Daily Stock Returns of Companies listed in Tehran Stock Exchange Using Artificial Neural Networks. Journal of financial research, 9(3), 115-134. (in Persian)
Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268.
Ramezanian, M. R., Ramezanpour, E., & Pourbakhsh, S. H. (2011). New Approaches in Forecasting Using Neuro-Fuzzy Networks (Case Study: The Crude Oil Price). Journal of management research in Iran, 15(3), 149-168. (in Persian)
Ramnath, S., Rock, S. & Shane, P. (2008). The financial analyst forecasting literature: A taxonomy with suggestions for further research. International Journal of Forecasting, 24(1), 34-75.
Rather, A. M., Agarwal, A. & Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234-3241.
Saeidi, A. & Aghaei, A. (2009). Predicting Financial Distress of firms Listed in Tehran Stock Exchange Using Bayesian networks. Journal of Accounting And Auditing Review, 16 (56), 59-78.
┼×enol, D. & Özturan, M. (2010). Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey. Journal of artificial Intelligence, 3, 261-268.
Shams, Sh., NajiZavareh, M. (2016). Comparison between the Hybrid Model of Genetic Fuzzy and Self Organizing Systems and Linear Model to Predict the Price of Gold Coin Futures Contracts. Journal of financial research, 17(2), 239-258. (in Persian)
Sözen, A. & Arcaklio─člu, E. (2007). Prospects for future projections of the basic energy sources in Turkey. Energy Sources, Part B, 2(2), 183-201.
Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications40(14), 5501-5506.
Van Eyden, R. J. (1996). The Application of Neural Networks in the Forecasting of Share Prices (Finance and Technology Publishing, Haymarket, VA.
Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42, 368-376.