Mapping the Landscape: A Bibliometric Analysis of Rating Agencies in the Era of Artificial Intelligence and Machine Learning
By:
References
- Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11–12), 1721–1742.
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11, 959–975.
- Atenstaedt, R. (2012). Word Cloud Analysis of the BJGP. British Journal of General Practice, 62, 148.
- Bin Yang. (2017). Stock Market Crash, New Market Failure Theory and Extremely Great Economic and Financial Crises. World Review of Political Economy, 8(1), 4–34.
- Brennan, D., & Brabazon, A. (2004). Corporate Bond Rating Using Neural Networks. International Conference on Artificial Intelligence.
- Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12(5–6), 373–379.
- Chen, S., Härdle, W. K., & Moro, R. A. (2011). Modeling default risk with support vector machines. Quantitative Finance, 11(1), 135–1.
- Costas, R., & Bordons, M. (2007). The h-index: advantages, limitations and its relation with other bibliometric indicators at the micro level. Journal of Informetrics, 1, 193–203.
- Cowan, A. R. (1991). Inside information and debt rating changes. Journal of the Midwest Finance Association, 20(1), 47–58.
- Daniels, H., & Kamp, B. (1999). Application of MLP networks to bond rating and house pricing. Neural Computation & Applications, 8(8), 226–234.
- Dutta, S., & Shekhar, S. (1988). Bond rating: a nonconservative application of neural networks. IEEE International Conference on Neural Networks, pp. 443–450.
- Ebenroth, C. T., & Dillon, T. J. (1993). The international rating game: An analysis of the liability of rating agencies in Europe, England, and the United States. Law and Policy in International Business, 24, 783–834.
- Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green Supply Chain Management: A Review and Bibliometric Analysis. International Journal of Production Economics.
- Giudici P., Centurelli M., Turchetta S. (2024). “Artificial Intelligence risk management”. Expert Systems with Applications, 235, ISSN 0957-4174.
- Golbayani, P., Florescu, I., & Chatterjee, R. (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, 54, 101251.
- Griffith-Jones, S., & Kraemer, M. (2021). Credit rating agencies and developing economies. UN, DESA, Dec. 2021. (Working Paper, n. 175).
- Hao, Z., Rallings, A. M., Espinoza, V., Luo, P. P., Duan, W. L., et al. (2021). Flowing from East to West: A Bibliometric Analysis of Recent Advances in Environmental Flow Science in China. Ecological Indicators.
- Hirsch J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.
- Horrigan, J. O. (1966). The determination of long-term credit standing with financial ratios. Journal of Accounting Research, 44–62.
- Irmatova, E. (2016). Relarm: A rating model based on relative pca attributes and k-means clustering. arXiv preprint arXiv:1608.06416.
- Bousmalis, K., Zafeiriou, S., Morency, L.P. & Pantic, M. (2013). Infinite Hidden Conditional Random Fields for Human Behavior Analysis. IEEE Transactions on Neural Networks and Learning Systems, 24(1), 170-177.
- Kaplan, R. S., & Urwitz, G. (1979). Statistical models of bond ratings: A methodological inquiry. Journal of Business, 231–261.
- Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, 201(3), 838–846.
- Kim, J. W., Weistroffer, H. R., & Redmond, R. T. (1993). Expert systems for bond rating: A comparative analysis of statistical, rule-based, and neural network systems. Expert Systems, 10(3), 167–172.
- Kim, K. S. (2005). Predicting bond ratings using publicly available information. Expert Systems with Applications, 29, 75–81.
- Kumar, R., Singh, S., Sidhu, A.S., Pruncu, C.I. (2021). Bibliometric Analysis of Specific Energy Consumption (SEC) in Machining Operations: A Sustainable Response. Sustainability, 13(10), 5617.
- Lee, T.S., Chiu, C.C., Chou, Y.C., & Lu, C.J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113–1130.
- Lessmann, S., Baesens, B., Seow, H.V., & Thomas, L.C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136.
- Maher, J. L., & Sen, T. K. (1997). Predicting bond ratings using neural networks: a comparison with logistic regression. Intelligent Systems in Accounting, Finance & Management, 6, 59–72.
- Maldonado, S., Bravo, C., Lopez, J., & Perez, J. (2017). Integrated framework for profit-based feature selection and SVM classification in credit scoring. Decision Support Systems, 104, 113-121.
- Mattarocci, G. (2014). The Independence of Credit Rating Agencies. Elsevier Monographs.
- ODLIS (2022). Retrieved from https://odlis.abc-clio.com/odlis_b.html
- Ozturk, H., Namli, E., & Erdal, H. I. (2016). Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample. Economic Modelling, 54(C), 469-478.
- Paleologo, G., Elisseeff, A., & Antonini, G. (2010). Subagging for credit scoring models. European Journal of Operational Research, 201(2), 490–499.
- Ubarhande P. & Chandani A. (2021). Elements of Credit Rating: A Hybrid Review and Future Research Agenda. Cogent Business & Management, 8(1), 1878977-187.
- Doğan, S., Büyükkör, Y. & Atan, M. (2022). A comparative study of corporate credit ratings prediction with machine learning. Operations Research and Decisions, 32(1), 25-47.
- Surkan, A., & Singleton, J. (1990). Neural networks for bond rating improved by multiple hidden layers. Proceedings of the IEEE International Conference on Neural Networks, pp. 157–162.
- Sylla, R. (2002). A historical primer on the business of credit rating. In Ratings, Rating Agencies and the Global Financial System, ed. R.M. Levich, G. Majnoni, and C. Reinhart, Part 1, Sec. 1, 19-40. Norwell, Massachusetts: The New York University Salomon Series on Financial Markets and Institutions, Kluwer Academic Publishers.
- Trivedi, S. K. (2020). A study on credit scoring modeling with different feature selection and machine learning approaches. Technology in Society, 63, 101413.
- Vapnik, V. (2013). The Nature of Statistical Learning Theory. Springer Science & Business Media.
- Zeng, J. J., Qu, J. S., & Ma, H. Q. (2021). Characteristics and Trends of Household Carbon Emissions Research from 1993 to 2019: A Bibliometric Analysis and Its Implications. Journal of Cleaner Production.
DOI: https://doi.org/10.2478/picbe-2024-0007 | Journal eISSN: 2558-9652
Language: English
Page range: 67 - 85
Published on: Jul 3, 2024
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2024 Adriana AnaMaria Davidescu, Marina-Diana Agafiței, Vasile Alecsandru Strat, Alina Mihaela Dima, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.