Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

Feng, Bojing; Xue, Wenfang; Xue, Bin; Liu, Zeyu

doi:10.1109/iccc51575.2020.9344973

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…To predict financial distress, various types of financial data have been commonly employed in machine learning models. These models have been widely used due to their capability of modeling complicated features of financial data [33]. The types of financial data commonly employed in machine learning models for predicting financial distress include financial ratios, discriminant analysis, and linear discriminant analysis [1].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

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Financial distress prediction is a crucial aspect of financial planning, particularly in the face of emerging uncertainties. This study introduces a novel methodology for predicting financial distress by amalgamating network analysis and machine learning techniques. The approach involves establishing two networks among companies based on their similarity and correlation in key financial indicators. The first network reflects similarity across five features, while the second captures correlation in the most critical feature. Subsequently, seven network-centric features are extracted and integrated into the dataset as new variables. Additionally, community detection algorithms are applied to cluster companies, with the resulting labels added as categorical variables. This process yields a modified dataset comprising both initial and network-based variables. Five classification algorithms are employed to forecast financial distress across three scenarios. Initially, models are trained using only the initial features. In subsequent scenarios, network-centric features from both similarity and correlation networks are incorporated, enhancing the predictive accuracy of machine learning models. Notably, features from the similarity network play a pivotal role in this improvement. The proposed model not only showcases superior predictive capabilities but also offers a holistic understanding of the dynamic interactions among financial entities. The results underscore the efficacy of network-based strategies in refining financial distress prediction models, providing valuable insights for decision-makers.

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Section: Literature Reviewmentioning

confidence: 99%

“…To obtain the best number of neighbors we will try the algorithm with different numbers of K in our dataset and choose the best number. This method to construct the network was earlier used to create a network between time series [33].…”

Section: ) Construct the Network Based On Similaritymentioning

confidence: 99%

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Kadkhoda,

Amiri

2024

IEEE Access

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“…Deep learning plays a significant role in corporate credit rating and assessment. Two-layer additive risk model [13], Artificial Neural Network [15], LSTM and AdaBoost [9], denoising-based neural network [21], deep belief network [14,75], probabilistic neural network (PNN) [76], Genetic algorithm with neural network [77], CNN [78] all show their great competency in estimation and assessing.…”

Section: Corporate Credit Riskmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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“…The financial data of corporation includes six aspects: profit capability, operation capability, growth capability, repayme capability, cash flow capability, dupont identity. After the same preprocess as work [Feng et al, 2020b], we get 39 features and 9 rating labels: AAA, AA, A, BBB, BB, B, CCC, CC, C. Table 1 shows the detail information. And following Figure 3 shows the data distribution and label imbalanced problem in corporate credit rating.…”

Section: Experimental Configurationsmentioning

confidence: 99%

Contrastive Pre-training for Imbalanced Corporate Credit Ratings

Feng¹,

Xue²

2021

Preprint

Self Cite

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Corporate credit rating reflects the level of corporate credit and plays a crucial role in modern financial risk control. But real-world credit rating data usually shows long-tail distributions, which means heavy class imbalanced problem challenging the corporate credit rating system greatly.To tackle that, inspried by the recent advances of pre-train techniques in selfsupervised representation learning, we propose a novel framework named Contrastive Pre-training for Corporate Credit Rating (CP4CCR), which utilizes the self-surpervision for getting over class imbalance. Specifically, we propose to, in the first phase, exert constrastive self-superivised pretraining without label information, which want to learn a better class-agnostic initialization. During this phase, two self-supervised task are developed within CP4CCR: (i) Feature Masking (FM) and (ii) Feature Swapping(FS). In the second phase, we can train any standard corporate redit rating model initialized by the pre-trained network. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CP4CCR can improve the performance of standard corporate credit rating models, especially for class with few samples.

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Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

Cited by 12 publications

References 27 publications

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

Machine learning-driven credit risk: a systemic review

Contrastive Pre-training for Imbalanced Corporate Credit Ratings

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