Net income prediction of several leading bank in Indonesia using neural approach

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The IFRS (International Financial Reporting Standards) defines net income as synonymous with profit and loss. Net income can be used as a consideration for investment decision making for investors who will invest their capital into a company. Net income for the next year cannot be ascertained but can be predicted by using several financial ratios that affect the change in net income. This study tries to predict net income next year by using several financial ratios obtained from four leading banks in Indonesia. The time series data modeling by using Artificial Neural Network (ANN) based Auto-Regressive with Exogenous input (ARX) model. In this study only use one net structure to model time series data in order to improve the efficiency of the model. Back-Propagation (BP) doing backpropagation to fix the weight of each layer of ANN such that to achieve appointed target error.


  • Keywords

    net income, financial ratios, ANN-based ARX model

  • References

    1. [1] L. J. Gitman and C. J. Zutter. ((2012)). Principles of Managerial Finance - Thirteenth Edition.

      [2] S. Saeed, L. Hussain, I. A. Awan, and A. Idris, "Comparative Analysis of different Statistical Methods for Prediction of PM2.5 and PM10 Concentrations in Advance for Several Hours," IJCSNS International Journal of Computer Science and Network Security, vol. 17, (2017).

      [3] A. S. Ahmar, "A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia," International Journal of Engineering & Technology, vol. 7, pp. 9-11, 2018.

      [4] A. S. Ahmar, S. Guritno, A. Rahman, I. Minggi, M. Arif Tiro, M. Kasim Aidid, et al., "Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)," Journal of Physics: Conf. Series, vol. 954, 2018.

      [5] A. Rahman and A. S. Ahmar, "Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models," in AIP Conference Proceedings vol. 1885, ed, 2017.

      [6] U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. V. Deventer, et al., "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews - Elsevier, vol. 81, (2018).

      [7] K. Kwon, W.-S. Cho, and J. Na, "ARIMAX and ARX Models with Social Media Information to Predict Unemployment Rate," Journal of Advanced Management Science, pp. 401-404, 2016.

      [8] A. Noreen, R. Asif, S. Nisar, and N. Qayyum, "Model Building and Forecasting of Bank Credit to Public and Private Sector," Universal Journal of Accounting and Finance, vol. 5, pp. 73-77, 2017.

      [9] F. Piltan, S. TayebiHaghighi, and N. B. Sulaiman, "Comparative Study between ARX and ARMAX System Identification," International Journal of Intelligent Systems and Applications, vol. 9, pp. 25-34, 2017.

      [10] A. Sapronova, C. Meissner, and M. Mana, "Short time ahead wind power production forecast," Journal of Physics: Conference Series, vol. 749, p. 012006, 2016.

      [11] T. Wang, "Forecast of Economic Growth by Time Series and Scenario Planning Method—A Case Study of Shenzhen," Modern Economy, vol. 07, pp. 212-222, 2016.

      [12] Haviluddin and R. Alfred, "Forecasting Network Activities Using ARIMA Method," Journal of Advances in Computer Networks (JACN), vol. 2, (3) September 2014, pp. 173-179, 2014.

      [13] B. Al-hnaity and M. Abbod, "Predicting Financial Time Series Data Using Hybrid Model," vol. 650, pp. 19-41, 2016.

      [14] A. M. Al-saadi, S. K. Zamiem, L. A. A. Al-Jumaili, M. JameelJubair, and H. A. A.-. Hashemi, "Estimating the Optimum Duration of Road Projects Using Neural Network Model," International Journal of Engineering and Technology (IJET), vol. 9, (2017).

      [15] A. Dingli and K. S. Fournier, "Financial Time Series Forecasting - A Machine Learning Approach," Machine Learning and Applications: An International Journal, vol. 4, pp. 11-27, 2017.

      [16] P. Enyindah and O. U. C., "A Neural Network Approach to Financial Forecasting," International Journal of Computer Applications (IJCA), vol. 135, pp. 28-32, (2016).

      [17] E. U. A. Gaffar, "Prediction of Regional Economic Growth in East Kalimantan using Genetic Algorithm," International Journal of Computing and Informatics (IJCANDI), vol. 1, pp. 58-67, May, (2016).

      [18] K. Gomathi and D. S. Priyaa, "A fuzzy analytic hierarchy attribute weighting and deep learning for improving CHD prediction of optimized semi parametric extended dynamic bayesian network," International Journal of Engineering & Technology (IJET), vol. 7, pp. 150-157, (2018).

      [19] M. Khairalla, Xu-Ning, and N. T. AL-Jallad, "Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 8, pp. 319-327, (2017).

      [20] M. B. Patel and S. R. Yalamalle, "Stock Price Prediction Using Artificial Neural Network," International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), vol. 3, pp. 13755-13762, (2014).

      [21] N. Vivekanandan, "Prediction of Rainfall Using MLP and RBF Networks," Int. J. Advanced Networking and Applications, vol. 5, pp. 1974-1979

      [22] Haviluddin, R. Alfred, J. H. Obit, M. H. A. Hijazi, and A. A. A. Ibrahim, "A Performance Comparison of Statistical and Machine Learning Techniques in Learning Time Series Data," Advanced Science Letters, pp. 3037-3041, 2015.

      [23] M. H. Beale, M. T. Hagan, and H. B. Demuth. ((2015)). Neural Network Toolbox™ User's Guide.

      [24] I. S. Exchange, "LQ45 Index Constituents for the period of February – July 2013," I. S. Exchange, Ed., ed: Indonesia Stock Exchange, (2013).

      [25] I. S. Exchange, "LQ45 Index Constituents for the period of February – July 2014," I. S. Exchange, Ed., ed: Indonesia Stock Exchange, (2014).

      [26] I. S. Exchange, "LQ45 Index Constituents for the period of February – July 2015," I. S. Exchange, Ed., ed: Indonesia Stock Exchange, (2015).

      [27] I. S. Exchange, "LQ45 Index Constituents for the period of February – July 2016," I. S. Exchange, Ed., ed: Indonesia Stock Exchange, (2016).

      [28] I. S. Exchange, "LQ45 Index Constituents for the period of February – July 2017," I. S. Exchange, Ed., ed: Indonesia Stock Exchange, (2017).




Article ID: 12743
DOI: 10.14419/ijet.v7i2.2.12743

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