The British Accounting Review Special Issue: New Perspectives on Financial Distress and Corporate Risk Management


We invite submissions of original articles in the topic areas of Financial Distress and Corporate Risk Management for publication in a Special Issue of The British Accounting Review.

This Special Issue is associated with the 2nd Credit Scoring and Credit Rating conference (CSCR II) to be held online and in Ningbo, China on 14-16 October 2022:

15 July 2022: Deadline for CSCRII conference submission
14 August 2022: Notice of acceptance for the conference
14-16 October 2022: A two-day conference will be held in Ningbo, at University of Nottingham Ningbo China
15 January 2023: Deadline of invited paper submission to the journal
July of 2023: Completion of the first round of reviews
End of 2023: Completion of all reviews
Early 2024: Publication of the issue

Guest Editors
- Edward Altman, Stern School of Business, New York University (NYU)
- Anthony Bellotti, School of Computer Science, University of Nottingham Ningbo China (UNNC)
- Yizhe Dong, Business School, University of Edinburgh Business School (UEBS)
- Zhiyong Li, School of Finance, Southwestern University of Finance and Economics (SWUFE)

The guest editors welcome informal enquiries from those who are interested in submitting a paper for consideration. It is anticipated that the special issue will be published in 2024. Initial queries about the special issue and CSCR II should be directed to Dr Yizhe Dong ( and Professor Zhiyong Li (

Due to the impact of the Covid-19 pandemic, the world economy is characterized by an overall downward trend, and enterprises are facing highly changeable economic environments and huge competitive pressure. The unforeseen influences have been summarised in Covid-19 and the Credit Cycle (Altman, 2020) and COVID-19 and the Credit Cycle: 2020 Revisited and 2021 Outlook (Altman, 2021). The recent Russia-Ukraine conflict further imposes huge risks on a world economy that’s yet to fully recover from the pandemic shock. Without a good mechanism to adapt to this complex environment, companies are more likely to encounter financial difficulties, such as excessive liabilities, insufficient liquidity or even bankruptcy. These problems will not only adversely affect the sustainable development of companies, but also result in significant losses to investors, creditors, customers and other stakeholders. When the number of companies in financial distress accumulates to a certain level, larger-scale social financial distress will emerge, and even the sustainability and stability of macroeconomic will be seriously undermined.

It is not so long ago since the Subprime Crisis spread from the banking industry to the whole financial systems, which caused disasters for the global economy. Basel II and III have been set up as international regulatory guidelines for bank supervision (Jacobson et al., 2005) and IFRS 9 also has a great impact on how to estimate the economic capitals to guarantee the soundness of financial institutions (Dong & Oberson, 2021). To ensure the credit supply to businesses and consumers, which is vital to economic growth, credit rating is considered an essential tool for financial institutions to estimate corporate credit risk and an important element of financial services for the bond and derivatives market. Nevertheless, financial distress and corporate bankruptcy are still common in all sectors which may trigger loan and debt defaults of underlying assets (Fich & Slezak, 2008; Charitou et al., 2004; Bryan et al., 2013).

Although financial technology leads to continuous innovations in financial models to assess credit risk more accurately and efficiently, financial distress and corporate risk remain important central issues for corporate studies. With this background in mind, we will hold the 2nd Credit Scoring and Credit Rating conference (CSCR II) in Ningbo China on 14-16 October 2022. This special issue and the associated CSCR II conference will share new perspectives on financial distress and corporate risk management and discuss research findings related to new information and methodologies for credit scoring and credit rating.

Rationale and Scope
The topic of financial distress prediction has long been regarded as a critical problem and of interest to finance and accounting studies for several decades since the inception of financial markets. One of the pioneering works was from Beaver (1966) who used financial ratios to predict bankruptcy. In general, the goal of corporate credit risk modeling is to predict whether a company will fall into financial distress or bankruptcy where the former is a period of financial conditions while the latter involves a formal judicial process such as in administration or liquidation. The prediction models play a significant role for banks, lenders, investors and regulators. In order to reduce information asymmetries and make reasonable decisions, accurate early warning models are particularly important for financial institutions.

Studies on financial distress or bankruptcy prediction have undergone a number of significant changes in recent decades. Previous research into bankruptcy prediction is primarily based on accounting information extracted from financial statements (Wilcox, 1973). Back in the 1960s, some academics developed scoring models by using financial ratios, such as the famous Z-score model (Altman, 1968), the logit model (Ohlson, 1980), the probit model (Zmijewski, 1984), and the hazard model (Shumway, 2001). Discriminant analysis dominated the early days (Deakin, 1972) and the logit model has since become typical (Jones and Hensher, 2004; 2007). Most of these studies used statistical methods, which are parametric regression with strong interpretability, but require strict assumptions on data distributions. They are comprehensively reviewed in Balcaen and Ooghe (2006), Kumar and Ravi (2007).

However, as financial institutions have become more demanding in terms of the effectiveness of predictive models, the key aim of studies has transferred primarily to identifying the best models that can provide the best predictive accuracy, since the early work from Marais et al (1984) who used decision trees to classify bank loans. With the development of information technologies, various novel machine learning algorithms and data mining techniques are more frequently employed to construct predictive models, such as Sun et al. (2021) and Tsai et al. 2021. Kumar & Ravi (2007) classified the intelligent techniques in financial distress prediction from the following groups, namely: neural networks, case-based reasoning, decision trees, operational research, rough sets, and so on. These models are widely used in classification problems because they run quickly and do not require too many assumptions. Surveys on them can be found in Bahrammirzaee (2010) and Verikas et al. (2010).

In order to ensure the validity of financial distress prediction, scholars continue searching for additional information and key factors that have significant impacts on the probability of distress and bankruptcy. For example, the classical Merton’s structural model (Merton, 1974) assumes the market price (share or option) to integrate forward looking information that is available in the market so that the Merton model is effective in calculating the Distance to Default. Later, Hillegeist et al. (2004), Duffie et al (2007) and Campbell et al. (2008) work were all developed on it. Other efforts include the incorporation of external resource factors (Hu and Ansell, 2007), legal actions from creditors, audit opinions, board characteristics (Wilson and Altanlar, 2014), business strategy (Bryan et al., 2013), corporate social responsibility (Sun and Cui, 2013), business plans (Perry, 2001), corporate efficiency (Becchetti and Sierra, 2013) corporate governance (Platt and Platt, 2012), and macroeconomic factors (Liou, 2007), all of which have found relationships to financial distress.

Habib et al. (2020) reviewed the literature on the determinants of financial distress and categorised these determinants into three groups: firm-level fundamental determinants, macroeconomic determinants, and corporate governance determinants. In general, firm-level fundamental determinants include employee relations, corporate social responsibility (CSR) activities, corporate hedging policies, management narrative disclosures, and other qualified audit opinions. It is intuitive to assume that the risk of financial distress for companies increases due to a decline in sales, cash flow and profitability in times of economic recession. Finally, weak corporate governance including board structure, CEO characteristics, ownership structure, etc., provide an opportunity for companies falling into financial distress and it has been one of the most discussed topics among researchers and policy-makers alike (Habib et al., 2020; Li et al., 2021).

Although some early empirical works still play important roles in the field of the prediction of corporate distress and bankruptcy, new methodologies and information are constantly being added to this area, particularly in the era of financial technology, big data, artificial intelligence, climate change and the pandemic, which all present new opportunities and challenges for insights to the deeper understanding of corporate failure and financial distress. Thus, we would like to encourage authors to submit papers on the following topics, but are not limited to:

- Accounting and auditing in predicting financial distress and bankruptcy
- Textual analysis to detect financial fraud and corporate failure
- Corporate distress forecasts with machine learning
- ESG engagement, ESG reporting, and corporate insolvency and failure
- The impact of climate change on corporate financial distress
- COVID-19 and corporate distress and default
- Russia-Ukraine conflict and corporate financial distress
- The impact of geopolitics on corporate risk management
- Combining accounting and alternative data to predict financial distress
- Corporate disclosures in predicting corporate default and failure
- Risk assessment of small and micro businesses
- Early warning systems and stress testing for bank failure
- Corporate loan, bonds and debt risk management

Submission information
This special issue of the British Accounting Review (BAR) is associated with the 2nd Credit Scoring and Credit Rating conference (CSCR II) held by University of Nottingham Ningbo China in Ningbo China on 14-16 October 2022. Authors who wish to participate need to submit a full paper to the conference via the link Papers for the conference should be submitted by 15 July 2022. The guest editorial team will invite the high-quality papers presented at the conference to submit their papers to the special issue. However, attendance and/or presentation at the conference is not a prerequisite for submission to the special issue.

All submissions will be subject to an initial screening and reviewing by the guest editors of the special issue. Papers which fall outside the scope of the special issue, or which are considered unlikely to be suitable for the special issue will be desk rejected. All manuscripts considered for inclusion in the special issue will be subject to a double-blind review process and must be submitted via the journal website before by 15 Jan 2023. All papers should have an original contribution and meet the high publishing quality standard of the journal. The submissions must be in accordance with journal guidelines. There is no submission fee. The Joint-Editors of the British Accounting Review will oversee the final set of accepted papers prior to publication.

BAR is ranked by the Australian Business Deans Council (ABDC) Quality Journal List as A*. In the Association of Business Schools (ABS) Academic Journal Guide (AJG), it is 3-rated. It currently has a Scopus Cite Score of 7.0 and an impact factor of 5.577. Further information on the journal can be found at:

- Faculty of Science and Engineering, University of Nottingham Ningbo China
- Business School, University of Nottingham Ningbo China
- School of Finance, Southwestern University of Finance and Economics
- Business School, University of Edinburgh

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