Rating firms and sensitivity analysis

Magni, Carlo Alberto and Malagoli, Stefano and Marchioni, Andrea and Mastroleo, Giovanni (2019): Rating firms and sensitivity analysis. Forthcoming in: Journal of the Operational Research Society

Abstract

This paper introduces a model for rating a firm's default risk based on fuzzy logic and expert system and an associated model of sensitivity analysis (SA) for managerial purposes.

The rating model automatically replicates the evaluation process of default risk performed by human experts. It makes use of a modular approach based on rules blocks and conditional implications. The SA model investigates the change in the firm's default risk under changes in the model inputs and employs recent results in the engineering literature of Sensitivity Analysis. In particular, it (i) allows the decomposition of the historical variation of default risk, (ii) identifies the most relevant parameters for the risk variation, and (iii) suggests managerial actions to be undertaken for improving the firm's rating.

• Item Type: MPRA Paper
• Language: English
• Keywords: Credit rating, default risk, fuzzy logic, fuzzy expert system, sensitivity analysis.
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C67 - Input-Output Models; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill

References

Agliardi, E., Agliardi, R. (2009). Fuzzy defaultable bonds. Fuzzy Sets and Systems, 160, 18, 2597-2607.

An, H.H., Chan, K.C. (2008). Credit ratings and IPO pricing. Journal of Corporate Finance, 14, 584-595.

Andriantiatsaholiniaina, L.A., Kouikoglou, V.S., Phillis, Y.A. (2004). Evaluating strategies for sustainable development: fuzzy logic reasoning and sensitivity analysis. Ecological Economics, 48, 149-172.

Angilella, S., Mazzu, S. (2015). The financing of innovative SMEs: A multicriteria credit rating model. European Journal of Operational Research, 244, 540-554.

Angilella, S., Mazzu, S. (2017). A credit risk model with an automatic override for innovative small and medium-sized enterprises. Journal of the Operational Research Society, DOI: 10.1080/01605682.2017.1411313.

Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing & Applications, 19, 1165-1195.

Bai, C., Shi, B., Liu, F., Sarkis, J. (2019). Banking credit worthiness: Evaluating the complex relationships. Omega, 83 (March), 26-38.

Bank for International Settlements (2011). Principles for the sound management of operational risk, http://www.bis.org/publ/bcbs195.htm.

Bellman, R.E., Zadeh, L.A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17(4), Application Series , B141-B164.

Bonsall IV, S.B., Holzman, E.R., Miller, B.P. (2017). Managerial Ability and Credit Risk Assessment. Management Science, 63(5), 1425-1449.

Borgonovo, E. (2010). A Methodology for Determining Interactions in Probabilistic Safety Assessment Models by Varying One Parameter at a Time. Risk Analysis, 30(3), 385-399.

Borgonovo, E. (2010). Sensitivity analysis with finite changes: An application to modified EOQ models. European Journal of Operational Research, 200, 127-138.

Borgonovo, E., Gatti, S., Peccati, L. (2010). What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions. European Journal of Operational Research, 205, 227-236 .

Borgonovo, E., Peccati, L. (2004). Sensitivity analysis in investment project evaluation. International Journal of Production Economics, 90, 17--25.

Borgonovo, E., Peccati, L. (2006). Uncertainty and global sensitivity analysis in the evaluation of investment projects. International Journal of Production Economics, 104 , 62-73 .

Borgonovo, E., Plischke, E. (2016). Sensitivity analysis: A review of recent advances. European Journal of Operational Research, 248(3), 869-887.

Chapman, C.B., Ward, S.C., Cooper, D.F., Page, M.J. (1984). Credit Policy and Inventory Control, Journal of the Operational Research Society, 35(12), 1055-1065.

Chen, C.C., Li, S.T. (2014). Credit rating with a monotonicity-constrained support vector machine model. Expert Systems with Applications, 41, 7235-7247.

Chen, L.-H., Chiou, T.-W. (1999). A fuzzy credit-rating approach for commercial loans: A Taiwan case. Omega, 27(4) (August), 407-419.

Cheng, M.Y., Wu, Y.W., Dan, L.T., Van Roy, A.F. (2013). Enhanced time-dependent evolutionary fuzzy support vector machines inference model for cash flow prediction and estimate at completion, International Journal of Information Technology & Decision Making, 12, 4, 679-710.

Dawood, N.N. (1996). A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry. Advances in Engineering Software, 25, 225-234.

Deleze, F., Korkeamaki, T. (2018). Interest rate risk management with debt issues: Evidence from Europe, Journal of Financial Stability, 36, 1-11.

Dirks, S., Kingston, J.K.C., Haggith, M. (1995). Development of a KBS for personal financial planning guided by pragmatic KADS. Expert Systems with Applications, 9, 91-101.

Donders, P., Jara, M., Wagner, R. (2018). How sensitive is corporate debt to swings in commodity prices? Journal of Financial Stability, 39, 237--258.

Doumpos, M., Figueira, J.R. (2019). A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method. Omega, 82 (January 2019), 166-180.

Doumpos, M., Niklis, D., Zopounidis, C., Andriosopoulos, K. (2015). Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms. Journal of Banking & Finance, 50, 599-607.

Doumpos, M., Zopounidis, C. (2011). A multicriteria outranking modeling approach for credit rating. Decision Sciences, 42(3), 721-742.

Ferreira, F.A.F., Meidute-Kavaliauskiene, I., Zavadskas, E.K., Jalali, M.S., Catarino, S.M.J. (2019). A Judgment-Based Risk Assessment Framework for Consumer Loans, International Journal of Information Technology & Decision Making, 18(1), 7-33

Galindo, J., Tamayo, P. (2000). Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications. Computational Economics, 15, 107-143.

Gomez-Skarmeta, A.F., Delgado, M., Vila, M.A. (1999). About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets and Systems, 106(2) (September), 179–188.

Griffin, P.A., Hong, H.A., Ryou, J.W. (2018). Corporate innovative efficiency: Evidence of effects on credit ratings. Journal of Corporate Finance, 51, 352-373.

Grigoroudis, E., Kouikoglou, V.S., Phillis, Y.A. (2014). SAFE 2013: Sustainability of countries updated. Ecological Indicators, 38, 61-66.

Guillame, S. (2001). Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE Transactions on Fuzzy Systems 9(3) (June), 426-443.

Guillaume, S., Charnomordic, B. (2011). Learning interpretable fuzzy inference systems with FisPro. Information Sciences, 181, 4409-4427.

Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37, 543-558.

Huefner, R.J. (1972). Sensitivity analysis and risk evaluation. Decision Sciences, 3(3), 128-135.

Hwang, R.C. (2013). Forecasting credit ratings with the varying-coefficient model. Quantitative Finance, 13, 12, 1947-1965.

Hwang, R.C. (2013). Predicting issuer credit ratings using generalized estimating equations. Quantitative Finance, 13(3), 383-398.

Ignizio, J.P. (1990). A brief introduction to expert systems. Computers & Operations Research, 17(6), 523-533.

Jiao, Y., Syau, Y.R., Lee, E.S. (2007). Modelling credit rating by fuzzy adaptive network. Mathematical and Computer Modelling, 45 , 717-731.

Karampatsas, N., Petmezas, D., Travlos, N.G. (2014). Credit ratings and the choice of payment method in mergers and acquisitions. Journal of Corporate Finance, 25, 474-493.

Kim, K.J., Ahn, H. (2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research, 39, 1800-1811.

Kisgen, D.J. (2006). Credit ratings and capital structure. The Journal of Finance, LXI, 3, 1035-1072.

Kouvelis, P., Zhao, W. (2017). Who Should Finance the Supply Chain? Impact of Credit Ratings on Supply Chain Decisions. Manufacturing & Service Operations Management, 20(1), 19-35.

Lee, Y.C. (2007). Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications, 33, 67-74.

Levy, J., Mallach, E., Duchessi, P. (1991). A Fuzzy Logic Evaluation System for Commercial Loan Analysis. Omega, 19(6), 651-669.

Lim, S.C., Mann, S.C., Mihov, V.T. (2017). Do operating leases expand credit capacity? Evidence from borrowing costs and credit ratings. Journal of Corporate Finance, 42, 100-114.

Lincy, G.R.M., John, C.J. (2016). A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange. Expert Systems With Applications, 44, 13-21.

Linn, S.C., Tay, N.S.P. (2007). Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning. Management Science, 53(7), 1165-1180.

Luo, C., Seco, L., Wu, L.L.B. (2015). Portfolio optimization in hedge funds by OGARCH and Markov Switching Model. Omega, 57, 34-39.

Madu, C.N. (1988). The Sensitivity of (Q,S) Inventory Model. Omega, 16(1), 61-64.

Magni, C.A., Malagoli, S., Mastroleo, G. (2006). An alternative approach to firms' evaluation: Expert systems and fuzzy logic. International Journal of Information Technology & Decision Making, 5(1), 195-225.

Magni C.A., Mastroleo G., Vignola M., Facchinetti G. (2004). Strategic options and expert systems: a fruitful marriage, Soft Computing, 8(3), 179-192.

Malagoli, S., Magni, C.A., Mastroleo, G. (2007). The use of fuzzy logic and expert systems for rating and pricing firms. A new perspective on valuation. Managerial Finance, 33(11), 836-852.

Marchioni, A., Magni, C.A. (2018). Investment decisions and sensitivity analysis: NPV-consistency of rates of return. European Journal of Operational Research, 68(1) (July), 361-372.

Marzouk M., Aboushady A. (2018). A fuzzy expert system model for risks qualification in real estate projects. International Journal of Sustainable Real Estate and Construction Economics, 1(2), 142-163.

Matsatsinis, N.F., Doumpos, M., Zopounidis, C. (1997). Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Systems with Applications, 12, 247-262.

Parnes, D. (2010). Time to default and other sensitivities of credit ratings. Quantitative Finance, 10, 9, 947-952.

Pena, A., Bonet, I., Lochmuller, C., Patino, H.A., Chiclana, F., Gongora, M. (2018). A fuzzy credibility model to estimate the Operational Value at Risk using internal and external data of risk events, Knowledge-Based Systems, 159, 98-109.

Pfeuffer, M., Mostel, L., Fischer, M. (2019). An extended likelihood framework for modelling discretely observed credit rating transitions. Quantitative Finance, 19(1), 93-104.

Pianosi, F., Beven, K., Freer, J., Hall, J.W., Rougier, J., Stephenson, D.B., Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214-232.

Rabitz, H., Alis, O.F. (1999). General foundations of high-dimensional model representations. Journal of Mathematical Chemistry, 25, 197-233.

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis. The Primer. John Wiley & Sons.

Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity analysis in practice. A guide to assessing scientific models. John Wiley & Sons.

Syau, Y.R., Hsieh, H.T., Lee, E.S. (2001). Fuzzy Numbers in the Credit Rating of Enterprise Financial Condition. Review of Quantitative Finance and Accounting, 17, 351-360.

Talavera, D.L., Nofuentes, G., Aguilera, J. (2010). The internal rate of return of photovoltaic grid-connected systems: A comprehensive sensitivity analysis. Renewable Energy, 35(1), 101-111.

Vassiliou, P.C.G. (2013). Fuzzy semi-Markov migration process in credit risk. Fuzzy Sets and Systems, 223, 39-58.

Vazquez-Abad, F.J., LeQuoc, P. (2001). Sensitivity analysis for ruin probabilities: canonical risk model, Journal of the Operational Research Society, 52(1) 71-81.

Volberda, H. W., Rutges, A. (1999). FARSYS: A knowledge-based system for managing strategic change. Decision Support Systems, 26, 99–123.

von Altrock, C. (1995). Fuzzy Logic and NeuroFuzzy Applications Explained. Prentice Hall.

Xiao, L., Liu, G. (2005). Automatic fuzzy rule extraction based on fuzzy neural network ISNN 2005, Advances in Neural Networks, Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, Proceedings, Springer Berlin Heidelberg, pp. 688-693.

Zhang, H., Zhang, B., Wang, F. (2009). Automatic fuzzy rules generation using fuzzy genetic algorithm, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, August 14-16, pp.~107-112.