Nonlinear Regression Model Based on Fractional Bee Colony Algorithm for Loan Time Series
Subject Areas : Signal Processing
Farid Ahmadi
1
(Computer Engineering Department, Urmia University of Technology, Urmia, Iran )
Mohammad Pourmahmood Aghababa
2
(Faculty Electrical Engineering, Urmia University of Technology, Urmia, Iran)
Hashem Kalbkhani
3
(Faculty Electrical Engineering, Urmia University of Technology, Urmia, Iran)
Keywords: Artificial Bee Colony, Fractional Calculus, Nonlinear Economic Model, Loan Status Prediction,
Abstract :
High levels of nonperforming loans provide negative impacts on the growth rate of gross domestic product. Therefore, predicting the occurrence of nonperforming loans is a vital issue for the financial sector and governments. In this paper, an intelligent nonlinear model is proposed for describing the behavior of nonperforming loans. In order to find the optimal parameters of the model, a new fractional bee colony algorithm (BCA) based on fractional calculus techniques is proposed. The inputs of the nonlinear model are the loan type, approved amount, refund amount, and economic realm. The output of the regression model is that whether the current information is for a nonperforming loan or not. Consequently, the model is modified to detect the status of a loan. So, the modified model predicts the occurrence of a nonperforming loan and determines the loan status, i.e., current, overdue, and nonperforming. The proposed procedure is applied to data gathered from an economic institution in Iran. The findings of this study are helpful for the managers of banks, and financial sectors to forecast the future of the loans and, therefore, manage the budget for the upcoming loan requests.
[1] Y. Zhang, G. Yu, and D. Yang, "Predicting nonperforming loan of business bank by multiple classifier fusion algorithms," Journal of Interdisciplinary Mathematics, vol. 19, no. 4, pp. 657-667, 2016.
[2] A. Belgrave, G. Kester, and M. Jackman, "Industry Specific Shocks and Nonperforming Loans in Barbados," The Review of Finance and Banking, vol. 4, no. 2, 2012.
[3] Y. Tang, H. Chen, B. Wang, M. Chen, M. Chen, and X. Yang, "Discriminant analysis of zero recovery for China's NPL," Advances in Decision Sciences, vol. 2009, 2009.
[4] S. L. Lin, "A new two-stage hybrid approach of credit risk in banking industry," Expert Systems with Applications, vol. 36, no. 4, pp. 8333-8341, 2009.
[5] A. O. Adewusi, T. B. Oyedokun, and M. O. Bello, "Application of artificial neural network to loan recovery prediction," International Journal of Housing Markets and Analysis, vol. 9, no. 2, pp. 222-238, 2016.
[6] G. Kou, Y. Peng, and C. Lu, "MCDM approach to evaluating bank loan default models," Technological and Economic Development of Economy, vol. 20, no. 2, pp. 292-311, 2014.
[7] P. Saha, I. Bose, and A. Mahanti, "A knowledge based scheme for risk assessment in loan processing by banks," Decision Support Systems, vol. 84, pp. 78-88, 2016.
[8] Z. Bitvai and T. Cohn, "Predicting peer-to-peer loan rates using bayesian nonlinear regression," in Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, pp. 2203-2209.
[9] R. Calabrese, "Predicting bank loan recovery rates with a mixed continuous‐discrete model," Applied stochastic models in business and industry, vol. 30, no. 2, pp. 99-114, 2014.
[10] T. Chen, X. Li, and J. He, "Complex dynamics of credit risk contagion with time-delay and correlated noises," in Abstract and Applied Analysis, 2014, vol. 2014: Hindawi.
[11] T. Chen, J. He, and J. Wang, "Bifurcation and chaotic behaviour of credit risk contagion based on Fitzhugh–Nagumo system," International Journal of Bifurcation and Chaos, vol. 23, no. 07, p. 1350117, 2013.
[12] S. Lahmiri, "On fractality and chaos in Moroccan family business stock returns and volatility," Physica A: Statistical Mechanics and its Applications, vol. 473, pp. 29-39, 2017.
[13] S. Radhakrishnan, A. Duvvuru, S. Sultornsanee, and S. Kamarthi, "Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations," Physica A: Statistical Mechanics and its Applications, vol. 444, pp. 259-270, 2016.
[14] Q. Tian, P. Shang, and G. Feng, "The similarity analysis of financial stocks based on information clustering," Nonlinear Dynamics, vol. 85, no. 4, pp. 2635-2652, 2016.
[15] B. B. Nair, P. S. Kumar, N. Sakthivel, and U. Vipin, "Clustering stock price time series data to generate stock trading recommendations: An empirical study," Expert Systems with Applications, vol. 70, pp. 20-36, 2017.
[16] H.-L. Yang and H.-C. Lin, "Applying the hybrid model of EMD, PSR, and ELM to exchange rates forecasting," Computational Economics, vol. 49, no. 1, pp. 99-116, 2017.
[17] H. Ye and A. Bellotti, "Modelling recovery rates for nonperforming loans," Risks, vol. 7, no. 1, p. 19, 2019.
[18] Y. Tanoue and S. Yamashita, "Loss given default estimation: a two-stage model with classification tree-based boosting and support vector logistic regression," Journal of Risk, vol. 21, no. 4, 2019.
[19] M. Otair and E. Hattab, "An implementation of a new type of online auction," International Journal of E-Business Research (IJEBR), vol. 4, no. 2, pp. 88-102, 2008.
[20] N. S. Sidnal and S. S. Manvi, "Intelligent Agent Based Model for Auction Service Discovery in Mobile E-Commerce," International Journal of E-Business Research (IJEBR), vol. 8, no. 1, pp. 76-97, 2012.
[21] Y. Lu, Z. Zeng, H. Wu, G. G. Chua, and J. Zhang, "An intelligent system for taxi service: Analysis, prediction and visualization," AI Communications, vol. 31, no. 1, pp. 33-46, 2018.
[22] E. Trandafili and M. Biba, "A review of machine learning and data mining approaches for business applications in social networks," International Journal of E-Business Research (IJEBR), vol. 9, no. 1, pp. 36-53, 2013.
[23] R. Chandra and S. Chand, "Evaluation of co-evolutionary neural network architectures for time series prediction with mobile application in finance," Applied Soft Computing, vol. 49, pp. 462-473, 2016.
[24] M. A. Elnaggar, M. A. EL Azeem, and F. A. Maghraby, "Machine Learning Model for Predicting Non-performing Agricultural Loans," in The International Conference on Artificial Intelligence and Computer Vision, 2020: Springer, pp. 395-404.
[25] S. K. Oli, "Deprived Sector Lending and Non-Performing Loans in Nepal," Applied Economics and Finance, vol. 8, no. 4, pp. 1-10, 2021.
[26] F. Medeiros Assef and M. T. Arns Steiner, "Ten-year evolution on credit risk research: a Systematic Literature Review approach and discussion," Ingeniería e Investigación, vol. 40, no. 2, pp. 50-71, 2020.
[27] G. Teles, J. Rodrigues, R. A. Rabê, and S. A. Kozlov, "Artificial neural network and Bayesian network models for credit risk prediction," Journal of Artificial Intelligence and Systems, vol. 2, no. 1, pp. 118-132, 2020.
[28] S. Deng, R. Li, Y. Jin, and H. He, "CNN-based feature cross and classifier for loan default prediction," in 2020 International Conference on Image, Video Processing and Artificial Intelligence, 2020, vol. 11584: International Society for Optics and Photonics, p. 115841K.
[29] N. Sopitpongstorn, P. Silvapulle, J. Gao, and J.-P. Fenech, "Local logit regression for loan recovery rate," Journal of Banking & Finance, vol. 126, p. 106093, 2021.
[30] M. R. Pradhan, S. Akter, and A. Al Marouf, "Performance Evaluation of Traditional Classifiers on Prediction of Credit Recovery," in Advances in Electrical and Computer Technologies: Springer, 2020, pp. 541-551.
[31] A. Min, M. Scherer, A. Schischke, and R. Zagst, "Modeling Recovery Rates of Small-and Medium-Sized Entities in the US," Mathematics, vol. 8, no. 11, p. 1856, 2020.
[32] A. Al Imran and M. N. Amin, "Loan Charge-Off Prediction Including Model Explanation for Supporting Business Decisions," in International Conference on Intelligent Systems Design and Applications, 2020: Springer, pp. 1281-1291.
[33] L. Li and D. Huang, "A Loan risk assessment model with consumption features for online finance," in Journal of Physics: Conference Series, 2021, vol. 1848, no. 1: IOP Publishing, p. 012068.
[34] N. Siddique and H. Adeli, "Simulated annealing, its variants and engineering applications," International Journal on Artificial Intelligence Tools, vol. 25, no. 06, p. 1630001, 2016.
[35] S. Parusheva, "Social Media Banking Usage From Banks' Perspective," International Journal of E-Business Research (IJEBR), vol. 15, no. 1, pp. 38-54, 2019.
[36] V. D. W. Aryanto, Y. Wismantoro, and K. Widyatmoko, "Implementing Eco-Innovation by Utilizing the Internet to Enhance Firm's Marketing Performance: Study of Green Batik Small and Medium Enterprises in Indonesia," in Disruptive Technology: Concepts, Methodologies, Tools, and Applications: IGI Global, 2020, pp. 1290-1307.
[37] J. Liu, C. Wu, and Y. Li, "Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method," Computational Economics, vol. 53, no. 2, pp. 851-872, 2019.
[38] D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer …, 2005.
[39] Y. Xu, P. Fan, and L. Yuan, "A simple and efficient artificial bee colony algorithm," Mathematical Problems in Engineering, vol. 2013, 2013.
[40] I. Podlubny, Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications. Elsevier, 1998.