1. Home
  2. Vol 6, No 5 (2022)
  3. Rubi

Fitting Multi-Layer Feed Forward Neural Network and Autoregressive Integrated Moving Average for Dhaka Stock Exchange Price Predicting

Maksuda Akter Rubi, Shanjida Chowdhury, Abdul Aziz Abdul Rahman, Abdelrhman Meero, Nurul Mohammad Zayed, K. M. Anwarul Islam

Abstract


The stock market plays a vital role in the economic development of any country. Stock market performance can be measured by the market capitalization ratio as well as many other factors. The primary purpose of this study is to predict the movement of the stock market based on the total market capitalization of the Dhaka Stock Exchange (DSE) using autoregressive integrated moving average (ARIMA) models as well as artificial neural networks (ANN). The data set covers monthly time series data of total market capitalization from November 2001 to December 2018. This study also shows the best model for forecasting the movement of DSE market capitalization. The ARIMA (2,1,2) model is chosen from among the several ARIMA model combinations. From several artificial neural networks (ANN) models as a modern tool, a three-layer feed-forward topology using a backpropagation algorithm with five nodes in the hidden layer, one lag, and a learning rate equal to 0.01 is selected as the best model. Finally, these selected two models are compared based on the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The results showed that the estimated error of ANN is less than the estimated error of the traditional method.

 

Doi: 10.28991/ESJ-2022-06-05-09

Full Text: PDF


Keywords


Dhaka Stock Exchange; Predicting; ARIMA; ANN; Multi-Layer Feed Forward Neural Network; Bangladesh.

References


Tsai, C. F., & Hsiao, Y. C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258–269. doi:10.1016/j.dss.2010.08.028.

Chowdhury, A. R. (2021). Testing Capital Asset Pricing Model (CAPM) on Dhaka Stock Exchange. doi:10.21203/rs.3.rs-573032/v2.

Ravichandra, T., Thingom, C. (2016). Stock Price Forecasting Using ANN Method. Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, 435, Springer, New Delhi, India. doi:10.1007/978-81-322-2757-1_59.

Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International Journal of Circuits, Systems and Signal Processing, 10(2016), 403–413.

Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S and P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999. Applied Financial Economics, 14(4), 285–297. doi:10.1080/0960310042000201228.

Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14), 5501–5506. doi:10.1016/j.eswa.2013.04.013.

Altay, E. (2005). Stock Market Forecasting: Artificial Neural Network and Linear Regression Comparison in an Emerging Market. Journal of Financial Management and Analysis, 18(2), 8–33.

Dutta, G., Jha, P., Laha, A. K., & Mohan, N. (2006). Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange. Journal of Emerging Market Finance, 5(3), 283–295. doi:10.1177/097265270600500305.

Bildirici, M., & Ersin, Ö. Ö. (2009). Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange. Expert Systems with Applications, 36(4), 7355-7362. doi:10.1016/j.eswa.2008.09.051.

Hair Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis (5th Ed.).Prentice Hall, Hoboken, United States.

Antonopoulou, H., Mamalougou, V., & Theodorakopoulos, L. (2022). The Role of Economic Policy Uncertainty in Predicting Stock Return Volatility in the Banking Industry: A Big Data Analysis. Emerging Science Journal, 6(3), 569-577. doi:10.28991/ESJ-2022-06-03-011.

Patil, R. B. (1990). Neural networks as forecasting experts: test of dynamic modeling over time series data. PhD Thesis, Oklahoma State University, Stillwater, United States.

Di Persio, L., & Frigo, M. (2016). Gibbs sampling approach to regime switching analysis of financial time series. Journal of Computational and Applied Mathematics, 300, 43–55. doi:10.1016/j.cam.2015.12.010.

Tang, Z., de Almeida, C., & Fishwick, P. A. (1991). Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation, 57(5), 303–310. doi:10.1177/003754979105700508.

Yoon, Y., & Swales, G. (1991). Predicting stock price performance: A neural network approach. Proceedings of the Annual Hawaii International Conference on System Sciences, 4, 156–162. doi:10.1109/HICSS.1991.184055.

Hamid, S. A., & Iqbal, Z. (2004). Using neural networks for forecasting volatility of S&P 500 Index futures prices. Journal of Business Research, 57(10), 1116–1125. doi:10.1016/S0148-2963(03)00043-2.

Olson, D., & Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19(3), 453–465. doi:10.1016/S0169-2070(02)00058-4.

Cao, Q., Leggio, K. B., & Schniederjans, M. J. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers and Operations Research, 32(10), 2499–2512. doi:10.1016/j.cor.2004.03.015.

de Faria, E. L., Albuquerque, M. P., Gonzalez, J. L., Cavalcante, J. T. P., & Albuquerque, M. P. (2009). Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), 12506–12509. doi:10.1016/j.eswa.2009.04.032.

Isfan, M., Menezes, R., & Mendes, D. A. (2010). Forecasting the Portuguese stock market time series by using artificial neural networks. Journal of Physics: Conference Series, 221, 012017. doi:10.1088/1742-6596/221/1/012017.

Jabin, S. (2014). Stock Market Prediction using Feed-forward Artificial Neural Network. International Journal of Computer Applications, 99(9), 4–8. doi:10.5120/17399-7959.

Vaisla, K. S., & Bhatt, A. K. (2010). An analysis of the performance of artificial neural network technique for stock market forecasting. International Journal on Computer Science and Engineering, 2(6), 2104-2109.

Hossain, M. M., Rajeb, M., & Shitan, M. (2011). Forecasting the market capital of dhaka stock exchange in bangladesh: a comparative study of garch and arima models. 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding, 14-16 March, 2011, Langkawi Kedah, Malaysia.

Maqsood, H., Mehmood, I., Maqsood, M., Yasir, M., Afzal, S., Aadil, F., Selim, M. M., & Muhammad, K. (2020). A local and global event sentiment based efficient stock exchange forecasting using deep learning. International Journal of Information Management, 50, 432–451. doi:10.1016/j.ijinfomgt.2019.07.011.

Cheng, J., Huang, K., & Zheng, Z. (2020). Towards Better Forecasting by Fusing Near and Distant Future Visions. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3593–3600. doi:10.1609/aaai.v34i04.5766.

Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243–247. doi:10.1007/BF02532251.

Anaghi, M. F., & Norouzi, Y. (2012). A model for stock price forecasting based on ARMA systems. 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). doi:10.1109/ICTEA.2012.6462880.

Groda, B., & Vrbka, J. (2017). Prediction of stock price developments using the Box-Jenkins method. SHS Web of Conferences, 39, 01007. doi:10.1051/shsconf/20173901007.

Senapati, M. R., Das, S., & Mishra, S. (2018). A Novel Model for Stock Price Prediction Using Hybrid Neural Network. Journal of the Institution of Engineers (India): Series B, 99(6), 555–563. doi:10.1007/s40031-018-0343-7.

Sayavong, L., Wu, Z., & Chalita, S. (2019). Research on Stock Price Prediction Method Based on Convolutional Neural Network. 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS). doi:10.1109/icvris.2019.00050.

Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences, 218, 1026. doi:10.1051/e3sconf/202021801026.

Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 1–30. doi:10.3390/ASI4010009.

Mehtab, S., & Sen, J. (2020). Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries. TechRxiv. Preprint. doi:10.36227/techrxiv.15088734.

Mehtab, S., & Sen, J. (2020). A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing. Proceedings of the 7th International Conference on Business Analytics and Intelligence (BAICONF, 2019). doi:10.48550/arXiv.1912.07700.

Roy, M., & Ashrafuzzaman, M. (2015). EMH, Earning Multiples and Common Stock Valuation: The Case of Dhaka Stock Exchange. International Review of Business Research Papers, 11(2), 62–76. doi:10.21102/irbrp.2015.09.112.06.

Islam, A., & Khaled, M. (2005). Tests of weak‐form efficiency of the Dhaka stock exchange. Journal of Business Finance & Accounting, 32(7‐8), 1613-1624. doi:10.1111/j.0306-686x.2005.00642.x.

Ramesh, V. P., Baskaran, P., Krishnamoorthy, A., Damodaran, D., & Sadasivam, P. (2019). Back propagation neural network based big data analytics for a stock market challenge. Communications in Statistics-Theory and Methods, 48(14), 3622-3642. doi:10.1080/03610926.2018.1478103.

Hossain, M. M. (2019). Forecasting the General Index of Dhaka Stock Exchange. International Research Journal of Finance and Economics, 171(171), 20–37.

Chowdhury, S., Rubi, M. A., & Bijoy, M. H. I. (2021). Application of Artificial Neural Network for Predicting Agricultural Methane and CO2 Emissions in Bangladesh. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). doi:10.1109/icccnt51525.2021.9580106.

Rubi, M. A., Bijoy, M. H. I., & Bitto, A. K. (2021). Life Expectancy Prediction Based on GDP and Population Size of Bangladesh using Multiple Linear Regression and ANN Model. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). doi:10.1109/ICCCNT51525.2021.9579594.

Bailey, D. L., & Thompson, D. (1990). Developing neural-network applications. AI Expert, 5(9), 34–41. doi:10.5555/87334.87341.

Klimasauskas, C.C. (1992). Applying Neural Networks, in Trippi, R.R. and E. Turban (Eds.), Neural Networks in Finance and Investing, Probus Publishing Company, Chicago, United States 47–72.

Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons, Hoboken, United States.

Koehler, A. B. (2001). Time Series Analysis and Forecasting with Applications of SAS and SPSS. International Journal of Forecasting, 17(2), 301–302. doi:10.1016/s0169-2070(01)00087-5.

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014, 1-7. doi:10.1155/2014/614342.


Full Text: PDF

DOI: 10.28991/ESJ-2022-06-05-09

Refbacks

  • There are currently no refbacks.