Investigating Revenue Smoothing Thresholds That Affect Bank Credit Scoring Models: An Iranian Bank Case Study | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 1، فروردین 2023، صفحه 131-148 اصل مقاله (1.76 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.11982.2346 | ||
| نویسندگان | ||
| Seyed Mahdi Sadatrasoul* ؛ Omid Mahdi Ebadati؛ Amir Amirzadeh Irani | ||
| Information Technology Management department, Management school, Kharazmi, Tehran, Iran. | ||
| چکیده | ||
| Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than 20% change in adjusting company’s financial statements and balance sheets parameters. | ||
| کلیدواژهها | ||
| Data mining؛ credit scoring؛ credit models؛ revenue smoothing؛ stacking | ||
| مراجع | ||
|
[1] S. Watts, L. Ross and J. Zimmerman, “Towards a positive theory of the determination of accounting standards”, Accounting review, vol. 53, pp.34-112, Jan.1978.
[2] R. W. Holthausen, “Evidence on the effect of bond covenants and management compensation contracts on the choice of accounting techniques: The case of the depreciation switch-back”, Journal ofAccounting and Economics, Vol. 3, pp. 73-109, June 2003.
[3] K. Kanagaretnam, G. J. Lobo, and R. Mathieu. “Managerial incentives for income smoothing through bank loan loss provisions”, Review of Quantitative Finance and Accounting, Vol.20, pp, 63-80., 2003.
[4] S. E. Michelson, J. Wagner, and C. W. Wootton. “The relationship between the smoothing of reported income and risk-adjusted returns”, Journal of Economics and Finance, Vol. 24, pp 59-141, 2000.
[5] S. M. Sadatrasoul, M. Gholamian, M. Siami, and Z. Hajimohammadi, "Credit scoring in banks and financial institutions via data mining techniques: A literature review." Journal of AI and Data Mining 1, Vol. 2 ,pp. 119-129, 2013.
[6] F. Louzada, A. Ara and B. F. Guilherme, “Classification methods applied to credit scoring: Systematic review and overall comparison”, Surveys in Operations Research and Management Science, Vol. 21, pp34-117., 2016.
[7] S. M. Sadatrasoul, "Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering" Iranian Journal of Management Studies, Vol. 11 , pp: 91-111, 2018.
[8] G. Dong, L. Kin Keung and J. Yen, “Credit scorecard based on logistic regression with random coefficients”, Procedia Computer Science, Vol. 1, pp.2463-2468, 2010.
[9] N. Siddiqi, “Credit risk scorecards: developing and implementing intelligent credit scoring”, John Wiley & Sons, ISBN: 978-1-119-20173-1, 2012.
[10] L. C.Thomas, “Consumer Credit Models: Pricing, Profit and Portfolios: Pricing, Profit and Portfolios”, OUP Oxford, 2009.
[11] A. Blöchlinger and L. Markus, “Economic benefit of powerful credit scoring”, Journal of Banking & Finance, Vol. 30, pp. 873-851., 2006.
[12] H. Zhao, “A multi-objective genetic programming approach to developing Pareto optimal decision trees”, Decision Support Systems, Vol. 43, pp. 809-826., 2007.
[13] H. Ince and A. Bora, “A comparison of data mining techniques for credit scoring in banking: A managerial perspective”, Journal of Business Economics and Management, Vol, 10, pp. 40-233., 2009.
[14] C. Beidleman, "Income Smoothing: The Role of Management', "The Accounting Review, vol.48(4), pp. 653-667., 1973.
[15] J. R. Graham, R. H. Campbell and S. Rajgopal, "The economic implications of corporate financial reporting", Journal of accounting and economics, Vol.40, pp 3-73., 2005.
[16] R. LaFond, M. H. Lang and H. A. Skaife, "Earnings smoothing, governance and liquidity: international evidence", 2007.
[17] M. Skurichina, R. P. W. Duin, "The Role of Combining Rules in Bagging and Boosting." Advances in Pattern Recognition, Vol. 1876. pp. 631-640 10, 2000.
[18] J. Sill, G. Takács, L. Mackey and D. Lin, "Feature-weighted linear stacking.", arXiv preprint arXiv:0911.0460, 2009.
[19] M.-W. Li, D.-Y. Xu, J. Geng and W.-C. Hong, "A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm" Nonlinear Dynamics Vol. 107 Issue 3 , pp. 2447-2467, 2022.
| ||
|
آمار تعداد مشاهده مقاله: 1,093 تعداد دریافت فایل اصل مقاله: 257 |
||