Comparison of Feedforward Neural Network and Classical Statistics Methods: Application in Finance
DOI:
https://doi.org/10.20956/j.v19i3.25379Keywords:
Finance, Neural Network, StatisticsAbstract
The flexibility and elevated accurateness of the statistical machine learning method makes this method widely applied in various fields. One of the statistical machine learning methods is the neural network, which can be used for data analysis. The great performance of the neural network method can be used in the field of finance. In this study, the neural network method was used to predict Non-Performing Loans (NPL) data and forecast credit receivables. In the NPL prediction, the banks used are State-Owned Banks, Regional Government Banks, and National Private Banks with a main capital of more than 6 trillion rupiahs in March 2021, i.e. 26 banks with the period of March 2018 until March 2021. In predicting NPL, a moving window scheme involves several different periods. In the forecast of the number of credit receivables, the data used is the number of financing receivables. The period from November 2012 to December 2020 is used as training data, while data for the period from January to June 2021 is used as testing data. The results of the analysis show that the neural network for NPL prediction and credit receivables forecasting shows better performance compared to classical methods such as multiple linear regression and ARIMA. A comparison of methods for banking NPLs prediction is based on the RMSE data testing values, while forecasting credit receivable is based on RMSE, MAE, and MAPE data testing values.
References
Russell S. & Norvig P., 2021. Artificial Intelligence: A Modern Approach, 4th ed., New York: Pearson Education.
Sarle W. S., 1994. Neural Networks and Statistical Models, in Annual SAS Users Group International Conference, , pp. 1–13.
Shams S. R., Jahani A., Kalantary S., Moeinaddini M., & Khorasani N., 2021, The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration, Urban Climate, vol. 37, p. 100837.
Özçelep Y., Sevgen S., & Samli R., 2020, A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression, Renewable Energy, vol. 156, pp. 570–578.
Kim M. K., Kim Y. S., & Srebric J., 2020, Predictions of electricity consumption in a campus building using occupant rates and weather elements with sensitivity analysis: Artificial neural network vs. linear regression, Sustainable Cities and Society, vol. 62, p. 102385.
Etli Y., Asirdizer M., Hekimoglu Y., Keskin S., & Yavuz A., 2019. Sex estimation from sacrum and coccyx with discriminant analyses and neural networks in an equally distributed population by age and sex, Forensic Science International, vol. 303, p. 109955.
Alunni V., Jardin P. du, Nogueira L., Buchet L., & Quatrehomme G., 2015. Comparing discriminant analysis and neural network for the determination of sex using femur head measurements, Forensic Science International, vol. 253, pp. 81–87.
Mohammadi F. et al., 2021, Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran, Biomedical Journal, vol. 44, no. 3, pp. 304–316.
Sanderson M., Bulloch A. G. M., Wang J. L., Williamson T., & Patten S. B., 2019. Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?, Journal of Affective Disorders, vol. 257, pp. 741–747.
Fei Y., Hu J., Gao K., Tu J., Li W., & Wang W., 2017, Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models, Journal of Critical Care, vol. 39, pp. 115–123.
Fanoodi B., Malmir B., & Jahantigh F. F., 2019. Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models, Computers in Biology and Medicine, vol. 113, p. 103415.
Gencer K. & Başçiftçi F., 2021. Time series forecast modeling of vulnerabilities in the android operating system using ARIMA and deep learning methods, Sustainable Computing: Informatics and Systems, vol. 30, p. 100515.
Suhermi N., Suhartono, Prastyo D. D., & Ali B., 2018. Roll motion prediction using a hybrid deep learning and ARIMA model, Procedia Computer Science, vol. 144, pp. 251–258.
Azayite F. Z. & Achchab S., 2016. Hybrid Discriminant Neural Networks for Bankruptcy Prediction and Risk Scoring, Procedia Computer Science, vol. 83, pp. 670–674.
Zhao Z., Xu S., Kang B. H., Kabir M. M. J., Liu Y., & Wasinger R., 2015. Investigation and improvement of multi-layer perceptron neural networks for credit scoring, Expert Systems with Applications, vol. 42, no. 7, pp. 3508–3516.
Sun X. & Lei Y., 2021. Research on financial early warning of mining listed companies based on BP neural network model, Resources Policy, vol. 73, p. 102223.
Tavana M., Abtahi A. R., Caprio D. Di, & Poortarigh M., 2018. An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking, Neurocomputing, vol. 275, pp. 2525–2554.
Bank Indonesia, 2013. Peraturan Bank Indonesia Nomor 15/2/PBI/2013.
Barus A. C. & Erick, 2016. Analisis Faktor-Faktor yang Mempengaruhi Non Performing Loan pada Bank Umum di Indonesia, Jurnal Wira Ekonomi Mikroskil, vol. 6, no. 2, pp. 113–122.
Margaretha F. & Kalista V., 2016. Faktor yang Mempengaruhi Non Performing Loan pada Bank di Indonesia, Jurnal Kesejahteraan Sosial Maret, vol. 3, no. 1, pp. 65–80.
Kristian E. F. & Hartomo K. D., 2019. Peramalan Pertumbuhan Kredit Menggunakan Algoritma Arima, Universitas Kristen Satya Wacana.
Putri K. I., 2019. Pemodelan dan Peramalan Pertumbuhan Kredit dengan ARIMA, Sekolah Tinggi Ilmu Ekonomi Perbanas.
Suhartono, 2007. Feedforward Neural Network untuk Pemodelan Runtun Waktu, Universitas Gajah Mada.
Zhang G. P. & Qi M., 2005. Neural Network Forecasting for Seasonal and Trend Time Series, European Journal of Operational Research, pp. 501–514.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Author and publisher
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Jurnal Matematika, Statistika dan Komputasi is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution License, allowing third parties to copy and redistribute the material in any medium or format, transform, and build upon the material, provided the original work is properly cited and states its license. This license allows authors and readers to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference.