Does Financial Performance Influence Credit Ratings? An analysis of Korean KRX Firms

Credit rating agencies offer information about default risk. Previous literature suggests that firm’s credit ratings are influenced by various metrics, specifically, numerous risk considerations such as size, leverage and growth. However, there is limited evidence to support the relationship between credit ratings and financial performance. Our research is motivated by this caveat. The purpose of this paper is to discover if financial performance measures can be included as an indicator for default risk since the relation between financial performance and default risk/credit rating is a question left unanswered in a South Korean context. In this paper, we empirically test if financial performance measures can provide additional information about credit ratings and credit rating changes. We perform a battery of tests to establish if the following financial performance measures: EPS, CPS, ROA, ROE, and ROS have any explanatory power in explaining credit ratings levels and credit rating changes. Using a sample from 2002 to 2013, we find that EPS and CPS has a statistically positive relation to credit ratings, suggesting that firms with higher credit ratings have higher levels of EPS and CPS compared to firms with lower credit ratings. Moreover, we find that firms with positive performance measured by EPS and CPS in period t have the potential to experience a credit ratings change in period t+1. However, in South Korea, the majority of firms do not experience a credit ratings change. When we estimate the financial performance of the firms that do not experience a credit ratings change, we find a statistically significant relation between credit rating and financial performance for EPS and CPS. The results suggest that credit ratings for firms with positive financial performance remain stable Finally, we examine the relation between performance in period t and credit ratings increase and decrease in period t+1. The results suggest that the credit ratings of firms with high level of financial performances increase or remain the same. We do not find a relation between financial performance and credit rating decreases; this result may be due to our small sample size. The previous literature has largely ignored the association between credit ratings and performance. Taken together, our results suggests that EPS and CPS can be used as financial performance measures by investors, government agencies and debt issuers as additional information about a firms credit rating levels, and subsequent changes. We contribute to the literature by providing empirical evidence of a relationship between performance metrics and credit ratings, specifically the link between EPS

A Case-Based Reasoning Approach to Bankruptcy Prediction Modeling.

This study examines the usefulness of an artificial intelligence method, case-based reasoning (CBR), in predicting corporate bankruptcy. Based on prior research, CBR is believed to be a viable method of predicting bankruptcy. Hypotheses are developed to test the usefulness of a CBR system and to compare the accuracy of such a system to the model considered to be the benchmark model in bankruptcy prediction, Ohlson\u27s (1980) nine-factor logistic regression (logit) model. Sample data consisting of manufacturing and industrial firms is drawn from the Compustat database in a 20:1 ratio of nonbankrupt to bankrupt firms, consistent with Ohlson\u27s (1980) proportions. Three CBR models representing one, two, and three years before bankruptcy are designed and developed using a CBR development tool, ReMind. Cross-validation is done using a 10% in-period holdout sample as well as a holdout sample of firms from outside the period from which the model is constructed. Three logit models based on Ohlson (1980) representing one, two, and three years before bankruptcy are constructed. The usefulness of the CBR system is determined by examination of type I and type II error rates. Chi-square statistics are used to compare the predictive accuracy of the three CBR models with the three logit models. The results indicate that the CBR method using ReMind is not useful in predicting corporate bankruptcy. It is believed that the small sample of bankrupt firms (relative to the sample size of nonbankrupt firms) contributes to the failure of these CBR models to accurately predict bankruptcy. Compared with two other studies that also use ReMind as development tools, there is evidence that the algorithm in ReMind does not accommodate small sample sizes. The results also indicate that CBR is not more accurate than the Ohlson (1980) logit model. Ohlson\u27s (1980) logit models attain a much higher accuracy rate than the CBR models and appear to be more stable over time than the CBR models

NEURAL NETWORKS FOR DECISION SUPPORT: PROBLEMS AND OPPORTUNITIES

Neural networks offer an approach to computing which - unlike conventional programming - does not necessitate a complete algorithmic specification. Furthermore, neural networks provide inductive means for gathering, storing, and using, experiential knowledge. Incidentally, these have also been some of the fundamental motivations for the development of decision support systems in general. Thus, the interest in neural networks for decision support is immediate and obvious. In this paper, we analyze the potential contribution of neural networks for decision support, on one hand, and point out at some inherent constraints that might inhibit their use, on the other. For the sake of completeness and organization, the analysis is carried out in the context of a general-purpose DSS framework that examines all the key factors that come into play in the design of any decision support system.Information Systems Working Papers Serie

Do credit ratings influence the demand/supply of audit effort?

© 2020, Emerald Publishing Limited. Purpose: Firm management has an incentive to improve credit ratings to enjoy the reputational and financial benefits associated with higher credit ratings. In this study, the authors question whether audit effort in hours can be considered incrementally increasing with credit ratings. Based on legitimacy theory, the authors conjecture that firms with higher credit ratings will demand higher levels of audit effort to signal audit and financial quality compared to firms with higher levels of credit risk. Design/methodology/approach: The authors conduct empirical tests using a sample of Korean-listed firms using a sample period covering 2001–2015. Findings: The results show that firms with higher credit ratings demand higher audit effort in hours compared to client firms with lower credit ratings. The authors interpret that firms with higher ratings (lower risk) demand higher levels of audit effort in hours to reduce information asymmetry and to demonstrate that financial reporting systems are robust based on audit effort signaling audit quality. The authors also interpret that firms with lower credit ratings do not have incentives to signal similar audit quality. The authors also capture the “Big4 auditor expertise” effect by demonstrating that client firms audited by nonBig4 auditors demand additional audit effort with increasing credit rating compared to Big4 clients. Research limitations/implications: Audit effort is considered a signal of firm risk in the literature. This study’s results show evidence that audit effort is inversely related to firm risk. Practical implications: The results show that audit hour information is informative and likely managed by firm stakeholders. Internationally, it is not possible to capture the audit demand of clients because listing audit hours on financial statements is not a rule. Given that audit hours can be considered informative, the authors believe that legislators could consider implementing a policy to mandate that audit hours be recorded on international annual reports to enhance transparency. Originality/value: South Korea is one of few countries to list audit effort on annual reports. Therefore, the link between audit effort and credit ratings is unique in South Korea because it is one of few countries in which market participants likely monitor audit effort

The Missing Link in the Integration of Knowledge Management Practices and Technological Solutions

The development of knowledge management tools shows a difference between the information system practice and the non-technological solutions: most of the time, the direction of the efforts is not presenting the same way, and not integrating each other. It is the same in the real life practice: there is a problem that the technological tools are not able to give an efficient support to the practice of the company. In this paper an integrated framework is developed to assign the right technological solutions for organisational demands

Does Market Performance (Tobin’s Q) Have A Negative Effect On Credit Ratings? Evidence From South Korea

Tobin's Q is an established measure of firm performance, based on investor confidence. However, the association between Tobin's Q and credit ratings is not well-established in the literature. Results Using a sample of Korean listed firms over the 2001-2016 sample period, Probit regression analysis shows that overall, Tobin's Q is positively associated with credit ratings. However, for firms with a >1 (1<) Tobin's Q ratio, a negative (positive) relationship exists. Moreover, in independent regressions, a threshold level if found where the effect of Tobin's Q on credit ratings changes from being positive (0.2), to negative (0.3). Originality To the best of our knowledge, we are the first to demonstrate that credit rating agencies are nuanced when making default risk assessments. Specifically, that in South Korea, a threshold level exists, at which increasing Tobin's Q values reduce credit ratings. Empirical evidence of the different association between Tobin's Q (market confidence) and credit ratings can extend the literature and offer insights to market participants. Furthermore, because Tobin's Q is a commonly used proxy for 2 financial performance in accounting lectures, the study has practical implications for academics in classrooms

End-user engagement: The missing link of sustainability transition for Australian residential buildings

This paper argues that attempts to transform Australia's urban environment into a sufficiently sustainable one has been misdirected. The ‘green rating tool,’ industry's adherence to relevant standards and governmental policies represent the primary means of effecting the sustainability transition. However, only high-profile commercial building owners seem interested in being green-rated; the actual end-users of buildings are far less committed (e.g. employees ensconced in commercial buildings and residential home occupiers). Through a systematic review of 103 journal articles published on the topic of end-users and sustainability transition, original findings are presented. The findings reveal that most residential end-users do not purchase green homes and without their ‘buy-in,’ sustainability transition across Australia will continue to fail. This paper offers a critical analysis of the status-quo, identifying where the effort to generate a sustainable urban environment has been misdirected, what challenges prevail, and why residential end-users have been overlooked. In looking for a way forward that engages end-users, the paper proposes that financial incentives for the purchase of low-carbon buildings must be introduced into the residential real-estate market. And the modeling for this rebate is discussed in terms of emissions trading schemes or carbon tax