A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ribaf.2022.101869
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022.
"Artificial intelligence and machine learning in finance: A bibliometric review,"
Research in International Business and Finance, Elsevier, vol. 61(C).
- Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.
- Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
- Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
- Duan, Yuejiao & Goodell, John W. & Li, Haoran & Li, Xinming, 2022. "Assessing machine learning for forecasting economic risk: Evidence from an expanded Chinese financial information set," Finance Research Letters, Elsevier, vol. 46(PA).
- Burggraf, Tobias, 2021. "Beyond risk parity – A machine learning-based hierarchical risk parity approach on cryptocurrencies," Finance Research Letters, Elsevier, vol. 38(C).
- Le, Hong Hanh & Viviani, Jean-Laurent, 2018.
"Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios,"
Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
- Hong Hanh Le & Jean-Laurent Viviani, 2018. "Predicting bank failure: An improvement by implementing machine learning approach on classical financial ratios," Post-Print halshs-01615106, HAL.
- Hang Luo & Linfeng Chen, 2019. "Bond yield and credit rating: evidence of Chinese local government financing vehicles," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 737-758, April.
- Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
- Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
- Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Zedda, Stefano & Modina, Michele & Gallucci, Carmen, 2024. "Cooperative credit banks and sustainability: Towards a social credit scoring," Research in International Business and Finance, Elsevier, vol. 68(C).
- Satyam Kumar & Yelleti Vivek & Vadlamani Ravi & Indranil Bose, 2023. "Causal Inference for Banking Finance and Insurance A Survey," Papers 2307.16427, arXiv.org.
- Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
- Kim, Jong-Min & Kim, Dong H. & Jung, Hojin, 2021. "Applications of machine learning for corporate bond yield spread forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
- Shenghuan Yang & lonut Florescu & Md Tariqul Islam, 2020. "Principal Component Analysis and Factor Analysis for Feature Selection in Credit Rating," Papers 2011.09137, arXiv.org, revised Dec 2020.
- Bojing Feng & Wenfang Xue & Bindang Xue & Zeyu Liu, 2020. "Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks," Papers 2012.03744, arXiv.org.
- Davidescu Adriana AnaMaria & Agafiței Marina-Diana & Strat Vasile Alecsandru & Dima Alina Mihaela, 2024. "Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the Era of Artificial Intelligence and Machine Learning," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 67-85.
- Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
- Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
- Koresh Galil & Ami Hauptman & Rosit Levy Rosenboim, 2023. "Prediction of Corporate Credit Ratings with Machine Learning: Simple Interpretative Models," Working Papers 2308, Ben-Gurion University of the Negev, Department of Economics.
- Seyyide Doğan & Yasin Büyükkör & Murat Atan, 2022. "A comparative study of corporate credit ratings prediction with machine learning," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 25-47.
- Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023.
"Business model contributions to bank profit performance: A machine learning approach,"
Research in International Business and Finance, Elsevier, vol. 64(C).
- F. Bolivar & Miguel A. Duran & A. Lozano-Vivas, 2024. "Business Model Contributions to Bank Profit Performance: A Machine Learning Approach," Papers 2401.12334, arXiv.org.
- Galil, Koresh & Hauptman, Ami & Rosenboim, Rosit Levy, 2023. "Prediction of corporate credit ratings with machine learning: Simple interpretative models," Finance Research Letters, Elsevier, vol. 58(PD).
- Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
- María Jesús Segovia‐Vargas & I. Marta Miranda‐García & Freddy Alejandro Oquendo‐Torres, 2023. "Sustainable finance: The role of savings and credit cooperatives in Ecuador," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(3), pages 951-980, September.
- Yu, Baojun & Li, Changming & Mirza, Nawazish & Umar, Muhammad, 2022. "Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
- Dan Wang & Tianrui Wang & Ionuc{t} Florescu, 2020. "Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance," Papers 2010.08698, arXiv.org.
- Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
- Kai Ren, 2023. "Study on Intelligent Forecasting of Credit Bond Default Risk," Papers 2305.12142, arXiv.org, revised Jun 2023.
- Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
- Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).
- González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
More about this item
Keywords
Credit rating; Machine learning; Counterfactual explanation; Sparsity algorithm; Explainable AI;All these keywords.
JEL classification:
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531922002550. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.