Information asymmetry and credit rating: A quasi-natural experiment from China

Hu, Xiaolu; Huang, Haozhi; Pan, Zheyao; Shi, Jing

doi:10.1016/j.jbankfin.2019.06.003

Cited by 53 publications

(21 citation statements)

References 36 publications

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“…CBR, established in August 2010, is the only investor‐pay re‐rating CRA in China. It adopts more stringent standards compared to the issuer‐pay CRAs and its ratings are regarded as more accurate (Hu et al ., 2019c). By merging CBR’s rating records between November 2011 and the end of 2017 with our sample, we find that on average, the ABC’s indicative rating sits in the middle between the CBR rating (the lowest) and the published rating (the highest).…”

Section: Resultsmentioning

confidence: 99%

“…The investor-pay CRA is China Bond Rating Co. Ltd. (CBR), which was established in August 2010. CBR does not directly compete with the issuer-pay CRAs as it is a re-rating CRA (Hu et al, 2019c). For more details on the industry structure and regulatory environment of the CRAs, see Amstad and He (2018), Livingston et al (2018), Hu et al (2019a), andJiang andPacker (2019).…”

Section: Market Overviewmentioning

confidence: 99%

“…For more details on the industry structure and regulatory environment of the CRAs, see Amstad and He (2018), Livingston et al (2018), Hu et al (2019a), andJiang andPacker (2019). Following Hu et al (2019c), we focus on LTIR because it makes ratings comparable across different bond issues. Moreover, it facilitates testing rating inflation as CBR only discloses long-term ratings.…”

Section: Market Overviewmentioning

confidence: 99%

“…We also control for other issuer and issue characteristics, following Livingston et al (2018) and Hu et al (2019c). The issuer characteristics include the following: Leverage, which is the ratio of the total liability of the balance sheet to total assets, where a higher leverage ratio indicates a smaller fraction of equity to cover losses and therefore leads to lower creditworthiness (Altman, 1968); Tangibility is the ratio of tangible assets to total assets, and because tangible assets are easy to collateralise, firms with higher tangibility are prone to have lower distress costs; EBIT/Interest is the ratio of earnings before interest and tax (EBIT) to the interest payment, and Ncf/Borrowing is the ratio of net operating cash flow to borrowing, where a larger value of both variables indicates a higher level of solvency.…”

Section: Variablesmentioning

confidence: 99%

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Rating shopping: evidence from the Chinese corporate debt security market

Chang

Pan

et al. 2020

Accounting & Finance

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We provide the first direct evidence on how issuers choose a credit rating agency (CRA). Using rating data from a leading CRA in China, we find that although in most cases the issuers publish more favourable ratings, in some cases issuers just select the ratings provided by CRAs they have business relationships with, especially when the more favourable ratings are above issuers' prior ratings. Our further analysis suggests that this phenomenon is driven by the switching cost arising from the issuer being considered as a rating shopper when it obtains an upgrade from a CRA without a business relationship.

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Section: Resultsmentioning

confidence: 99%

Section: Market Overviewmentioning

confidence: 99%

Section: Market Overviewmentioning

confidence: 99%

Section: Variablesmentioning

confidence: 99%

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Rating shopping: evidence from the Chinese corporate debt security market

Chang

Pan

et al. 2020

Accounting & Finance

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“…the due date), little research has been conducted in predicting the tax status of firms. Most studies have tended to focus on predicting other default risks, such as credit default [2] and corporate bankruptcy [3]. Measuring the tax default status of firms is considered to be an even more challenging prediction task because it is characterized by the presence of sample selection bias due to the small number of labelled data (known as tax default cases) [4].…”

Section: Introductionmentioning

confidence: 99%

Tax Default Prediction Using Feature Transformation-Based Machine Learning

et al. 2021

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This study proposes to address the economic significance of unpaid taxes by using an automatic system for predicting a tax default. Too little attention has been paid to tax default prediction in the past. Moreover, existing approaches tend to apply conventional statistical methods rather than advanced data analytic approaches, including state-of-the-art machine learning methods. Therefore, existing studies cannot effectively detect tax default information in real-world financial data because they fail to take into account the appropriate data transformations and nonlinear relationships between early-warning financial indicators and tax default behavior. To overcome these problems, this study applies diverse feature transformation techniques and state-of-the-art machine learning approaches. The proposed prediction system is validated by using a dataset showing tax defaults and non-defaults at Finnish limited liability firms. Our findings provide evidence for a major role of feature transformation, such as logarithmic and square-root transformation, in improving the performance of tax default prediction. We also show that extreme gradient boosting and the systematically developed forest of multiple decision trees outperform other machine learning methods in terms of accuracy and other classification performance measures. We show that the equity ratio, liquidity ratio, and debt-to-sales ratio are the most important indicators of tax defaults for 1-year-ahead predictions. Therefore, this study highlights the essential role of well-designed tax default prediction systems, which require a combination of feature transformation and machine learning methods. The effective implementation of an automatic tax default prediction system has important implications for tax administration and can assist administrators in achieving feasible government expenditure allocations and revenue expansions.INDEX TERMS Default prediction, corporate tax, machine learning, feature transformation.

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Exploration on the Implementation Path of Drawing Course Under the Information-Based Background

Yan

2020

Advances in Intelligent Systems and Computing

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Information asymmetry and credit rating: A quasi-natural experiment from China

Cited by 53 publications

References 36 publications

Rating shopping: evidence from the Chinese corporate debt security market

Rating shopping: evidence from the Chinese corporate debt security market

Tax Default Prediction Using Feature Transformation-Based Machine Learning

Exploration on the Implementation Path of Drawing Course Under the Information-Based Background

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