Corporate credit-risk evaluation system: Integrating explicit and implicit financial performances

被引:18
|
作者
Zhang, Faming [1 ]
Tadikamalla, Pandu R. [2 ]
Shang, Jennifer [2 ]
机构
[1] Nanchang Univ, Sch Econ & Management, Nanchang 330031, Peoples R China
[2] Univ Pittsburgh, Joseph M Katz Grad Sch Business, Pittsburgh, PA 15260 USA
基金
中国国家自然科学基金;
关键词
Credit-risk; Decision analysis; Dynamic evaluation; Incentive point; Explicit incentive; Implicit incentive; SCORING MODELS; NETWORKS;
D O I
10.1016/j.ijpe.2016.04.012
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traditional credit-risk evaluation methods focus mainly on static credit evaluation and rarely consider incentive factors. This paper proposes a comprehensive method of credit-risk evaluation based on dynamic incentives. First, an "explicit incentive" model is constructed based on the firm's current financial standing, and an "implicit incentive" model is subsequently developed focusing on the trend of the firm's past performance. Geometric (or arithmetic) procedures are applied to integrate the two models. To validate the proposed approach, we apply it to 12 publicly traded companies, each with 24 quarters and 20 indicators. We find the proposed integrated evaluation model outperforms the conventional models by better reflecting the key credit-risk management concept of "motivation and guidance". (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:77 / 100
页数:24
相关论文
共 25 条
  • [1] Using Neural Network Rule Extraction for Credit-Risk Evaluation
    Arns Steiner, Maria Teresinha
    Steiner Neto, Pedro Jose
    Soma, Nei Yoshihiro
    Shimizu, Tamio
    Nievola, Julio Cesar
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2006, 6 (5A): : 6 - +
  • [2] Corporate Carbon-Risk and Credit-Risk: The Impact of Carbon-Risk Exposure and Management on Credit Spreads in Different Regulatory Environments
    Dumrose, Maurice
    Hoeck, Andre
    FINANCE RESEARCH LETTERS, 2023, 51
  • [3] An Exploration of FNN Method for Corporate Credit Risk Evaluation
    Chen, Wenjun
    ADVANCES IN BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING, 2008, 5 : 829 - 833
  • [4] Transparent Neural based Expert System for Credit Risk (TNESCR): An Automated Credit Risk Evaluation System
    Dattachaudhuri, Abhinaba
    Biswas, Saroj Kr
    Sarkar, Sunita
    Boruah, Arpita Nath
    Chakraborty, Manomita
    Purkayastha, Biswajit
    2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PERFORMANCE EVALUATION (COMPE-2020), 2020, : 13 - 17
  • [5] Information System for the Measurement of Credit Risk in Financial Institutions
    Guzman Aguilar, Diana
    Montes Gomez, Luis Fernando
    Bedoya Londono, David Alberto
    Adrian Zuluaica, Camilo
    2019 14TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 2019,
  • [6] Using DEA and financial ratings for credit risk evaluation: an empirical analysis
    Iazzolino, Gianpaolo
    Bruni, Maria Elena
    Beraldi, Patrizia
    APPLIED ECONOMICS LETTERS, 2013, 20 (14) : 1310 - 1317
  • [7] The influence of ESG performance on credit risk and financial distress: an empirical study on Taiwan corporate sustainability
    Ding, Yu-Jia
    Guo, Jiunn-Liang
    Tsai, Cai-Wen
    APPLIED ECONOMICS, 2025, 57 (12) : 1351 - 1367
  • [8] Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
    Rudin, Cynthia
    Shaposhnik, Yaron
    JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
  • [9] Moody's KMV model and its apply in credit risk evaluation of corporate
    Deng, ZW
    Proceedings of 2005 International Conference on Construction & Real Estate Management, Vols 1 and 2: CHALLENGE OF INNOVATION IN CONSTRUCTION AND REAL ESTATE, 2005, : 773 - 775
  • [10] XBRL INTEGRATION INTO INTELLIGENT SYSTEM FOR CREDIT RISK EVALUATION
    Garsva, Gintautas
    Danenas, Paulius
    TRANSFORMATIONS IN BUSINESS & ECONOMICS, 2011, 10 (02): : 88 - 103