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Evolutionary optimization of transition probability matrices for credit decision-making

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  • Zhang, Jingqiao
  • Avasarala, Viswanath
  • Subbu, Raj

Abstract

Statistical transition probability matrices (TPMs), which indicate the likelihood of obligor credit state migration over a certain time horizon, have been used in various credit decision-making applications. A standard approach of calculating TPMs is to form a one-year empirical TPM and then project it into the future based on Markovian and time-homogeneity assumptions. However, the one-year empirical TPM calculated from historical data generally does not satisfy desired properties. We propose an alternative methodology by formulating the problem as a constrained optimization problem requiring satisfaction of all the desired properties and minimization of the discrepancy between predicted multi-year TPMs and empirical evidence. The problem is high-dimensional, non-convex, and non-separable, and is not effectively solved by nonlinear programming methods. To address the difficulty, we investigated evolutionary algorithms (EAs) and problem representation schemas. A self-adaptive differential evolution algorithm JADE, together with a new representation schema that automates constraint satisfaction, is shown to be the most effective technique.

Suggested Citation

  • Zhang, Jingqiao & Avasarala, Viswanath & Subbu, Raj, 2010. "Evolutionary optimization of transition probability matrices for credit decision-making," European Journal of Operational Research, Elsevier, vol. 200(2), pages 557-567, January.
  • Handle: RePEc:eee:ejores:v:200:y:2010:i:2:p:557-567
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    References listed on IDEAS

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    1. Jafry, Yusuf & Schuermann, Til, 2004. "Measurement, estimation and comparison of credit migration matrices," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2603-2639, November.
    2. Richard Cantor & Frank Packer, 1996. "Determinants and impact of sovereign credit ratings," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Oct), pages 37-53.
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    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    2. Piotrowski, Adam P. & Napiorkowski, Jaroslaw J. & Kiczko, Adam, 2012. "Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 33-46.
    3. Zhao, Zhiwei & Yang, Jingming & Hu, Ziyu & Che, Haijun, 2016. "A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems," European Journal of Operational Research, Elsevier, vol. 250(1), pages 30-45.
    4. Lützenkirchen, Kristina & Rösch, Daniel & Scheule, Harald, 2014. "Asset portfolio securitizations and cyclicality of regulatory capital," European Journal of Operational Research, Elsevier, vol. 237(1), pages 289-302.
    5. Hira Zaheer & Millie Pant & Sushil Kumar & Oleg Monakhov & Emilia Monakhova & Kusum Deep, 2017. "A new guiding force strategy for differential evolution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 2170-2183, December.

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