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Application of Deep Neural Networks to assess corporate Credit Rating

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  • Parisa Golbayani
  • Dan Wang
  • Ionut Florescu

Abstract

Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze companies from the energy, financial and healthcare sectors in US. The goal of the analysis is to improve application of machine learning algorithms to credit assessment. To this end, we focus on three questions. First, we investigate if the algorithms perform better when using a selected subset of features, or if it is better to allow the algorithms to select features themselves. Second, is the temporal aspect inherent in financial data important for the results obtained by a machine learning algorithm? Third, is there a particular neural network architecture that consistently outperforms others with respect to input features, sectors and holdout set? We create several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedure.

Suggested Citation

  • Parisa Golbayani & Dan Wang & Ionut Florescu, 2020. "Application of Deep Neural Networks to assess corporate Credit Rating," Papers 2003.02334, arXiv.org.
    • Handle: RePEc:arx:papers:2003.02334
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    References listed on IDEAS

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    1. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
    2. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    3. Kuldeep Kumar & Sukanto Bhattacharya, 2006. "Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances," Review of Accounting and Finance, Emerald Group Publishing, vol. 5(3), pages 216-227, August.
    4. Shangeth Rajaa & Jajati Keshari Sahoo, 2019. "Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction," Papers 1905.07581, arXiv.org.
    5. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    6. Yang Liu & Qingguo Zeng & Joaquín Ordieres Meré & Huanrui Yang, 2019. "Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach," Complexity, Hindawi, vol. 2019, pages 1-15, August.
    7. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
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    Citations

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    Cited by:

    1. Dan Wang & Zhi Chen & Ionut Florescu, 2021. "A Sparsity Algorithm with Applications to Corporate Credit Rating," Papers 2107.10306, arXiv.org.
    2. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Nov 2024.
    3. 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.
    4. 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.
    5. Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023. "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. 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.
    7. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    8. Long Ren & Shaojie Cong & Xinlong Xue & Daqing Gong, 2024. "Credit rating prediction with supply chain information: a machine learning perspective," Annals of Operations Research, Springer, vol. 342(1), pages 657-686, November.

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