Corporate Credit

CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration ArXiv ID: 2508.02738 “View on arXiv” Authors: Yumeng Shi, Zhongliang Yang, DiYang Lu, Yisi Wang, Yiting Zhou, Linna Zhou Abstract Corporate credit rating serves as a crucial intermediary service in the market economy, playing a key role in maintaining economic order. Existing credit rating models rely on financial metrics and deep learning. However, they often overlook insights from non-financial data, such as corporate annual reports. To address this, this paper introduces a corporate credit rating framework that integrates financial data with features extracted from annual reports using FinBERT, aiming to fully leverage the potential value of unstructured text data. In addition, we have developed a large-scale dataset, the Comprehensive Corporate Rating Dataset (CCRD), which combines both traditional financial data and textual data from annual reports. The experimental results show that the proposed method improves the accuracy of the rating predictions by 8-12%, significantly improving the effectiveness and reliability of corporate credit ratings. ...

Bankruptcy analysis using images and convolutional neural networks (CNN) ArXiv ID: 2502.15726 “View on arXiv” Authors: Unknown Abstract The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and bankruptcy in these more prominent companies. Historically, risk analysis studies have focused on data from publicly traded or stock exchange-listed companies, leaving a gap in knowledge about small and medium-sized enterprises (SMEs). Addressing this gap, this study introduces a method for evaluating SMEs by generating images for processing via a convolutional neural network (CNN). To this end, more than 10,000 images, one for each company in the sample, were created to identify scenarios in which the CNN can operate with higher assertiveness and reduced training error probability. The findings demonstrate a significant predictive capacity, achieving 97.8% accuracy, when a substantial number of images are utilized. Moreover, the image creation method paves the way for potential applications of this technique in various sectors and for different analytical purposes. ...

Special Purpose Vehicles and Securitization ArXiv ID: ssrn-684716 “View on arXiv” Authors: Unknown Abstract Firms can finance themselves on- or off-balance sheet. Off-balance sheet financing involves transferring assets to “special purpose vehicles” (SPVs), Keywords: Off-balance sheet financing, Special Purpose Vehicles (SPVs), Asset transfer, Corporate finance, Balance sheet management, Corporate Credit Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper relies on game theory and contractual theory, but the math presented is relatively conceptual rather than dense with advanced proofs or LaTeX. It demonstrates strong empirical rigor by using unique credit card securitization data to test theoretical predictions, focusing on real-world implementation and data analysis. flowchart TD A["Research Goal<br>Assess SPV impact on corporate finance & credit"] --> B["Methodology"] B --> C{"Data Sources"} C --> D["1. SEC EDGAR<br>ABS/SPV filings"] C --> E["2. Moody's/Refinitiv<br>Corporate credit data"] C --> F["3. Bloomberg<br>Balance sheet metrics"] D & E & F --> G["Computational Process<br>Fixed Effects Regressions"] G --> H["Key Outcomes/Findings"] H --> I["1. Off-balance sheet<br>reduces leverage ratios"] H --> J["2. SPV issuance<br>lowers funding costs"] H --> K["3. Risk transfer<br>affects corporate credit"]