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Jensen and Murphy (1990) and others have found a small but statistically significant relationship between firm performance (as measured by change in shareholder wealth or firm profits) and executive compensation. In this study we investigate the pay‐ performance relationship further by considering the relationship between an outside measure of firm performance (changes in the firm's bond rating) and the contemporaneous change in the compensation of the firm's CEO. We find that when a firm's bond rating is down‐graded, CEO total compensation declines by a relatively small amount ($165,500) and when a firm's bond rating is upgraded, CEO total compensation increases markedly ($3,202,900). Thus, while a positive pay‐performance relationship exists, the relationship is not symmetric. CEO compensation changes (increases) much more when firm performance improves than it changes (decreases) when firm performance declines. Further, most of the change in CEO compensation occurs in the stock gains (profits from the exercise of stock options) category for both firms experiencing bond rating upgrades and down‐grades.

The effect of situational factors on the rating ability of 28 industrial analysts was determined through the use of rating films. The rating ability was evaluated in terms of rating accuracy and consistency. Significant differences in rating accuracy were found among the analysts from five different companies. The analysts who used time standards for planning functions surprisingly rated more consistently than those who employed time standards for a wage incentive programme. Shop labour organization, union or non‐union, had no significant impact on the analysts′ rating ability. The analysts′ rating consistency was significantly better for the medium (85‐120 per cent) and fast (125‐145 per cent) pace ranges than for the slow (60‐80 per cent) pace range. The rating consistency of the fast pace range was significantly better than the medium pace range. The familiar (machining) operations were rated more accurately and consistently than the unfamiliar (sheet metal) operations. The rating accuracy for the simple operations was significantly better than the moderate and complex operations. The simple and complex operations were rated significantly more consistently than the moderate operations.

The proliferation of environmental rating tools over the past two decades has endeavoured to assist the industry in measuring sustainability. Recent changes to the International Valuation Standards (IVS) have directed valuers to consider ESG. The purpose of this study aims to examine how commonly utilized sustainability tools, which have been employed to communicate building sustainability credentials, align with the IVS categories of ESG.

The research utilises the IVS categorisation of ESG and maps sustainability tools adopted at scale by the Australian Commercial Real Estate market. The approach identifies the various attributes within the commonly utilised rating tools that align with IVS defined ESG criteria.

The mapping provides insights into the coverage of the IVS ESG criteria in the mainstream tools used in Australia. Further, the research identifies existing sustainability criteria that are relevant to the built environment, that have not been clearly identified by the IVS, but have an important role in evaluating the sustainability of commercial real estate.

For investors, occupiers and valuers, this research provides insights on how the current, commonly utilised sustainability rating tools align with the IVS-defined ESG metrics. This research assists in providing greater clarity regarding the relationship between ESG criteria and existing rating tools, which have been recently identified as key considerations in valuation practice and help to provide transparency and understanding for property stakeholders.

The importance of monitoring, reporting and enhancing transparency in ESG disclosures has emerged as a central issue with significant implications for the property industry. This research provides the first evaluation of how existing sustainability rating tools map against ESG criteria as directed in the IVS.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

The purpose of the study is to measure the performance level of halal compliance practices by the halal-certified restaurants in Malaysia and rank and rate them based on several dimensions of halal compliance.

A total of 320 halal restaurants were surveyed. The absolute measurement approach of the analytic hierarchy process (AHP) was applied to rank and rate the restaurants. Finally, ANOVA and independent t-test were applied to assess if there is any significant difference in halal compliance performance between different groups of the respondents.

The AHP application resulted in only 19 restaurants (5.94%) achieving an “Excellent” rating. A significant difference has been observed between different groups of the respondents regarding their halal compliance performance.

An onsite audit and ranking of all the restaurants in Malaysia were beyond the scope of the study. The research was able to rank only 320 restaurants across Malaysia.

The findings and methodology of the study will provide policymakers with a clear roadmap for establishing a comprehensive rating system in the fields of the halal food industry to enhance the quality and integrity of the halal food management system.

To the best of the authors’ knowledge, this is the first time an empirical approach, like AHP, has been used to determine how Malaysia’s halal-certified restaurants stack up against one another. Similar studies can be carried out in other sectors of the halal industry as well as in similar context.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This chapter is a study of the impact of environmental, social, and governance (ESG) issues on credit rating in India. It will help issuers, investors, and other market participants understand rating agencies' approach to incorporating the sustainability-related factors in its analysis. This will provide an overall perspective on the considerations that are usually the most important. Under environment considerations, climate change, waste recycling, air pollutants, and natural capital sustainability can be important factors. Social considerations are becoming more and more important among investors and consumers and are raising awareness about prosperous and failing communities. Moreover, COVID-19 pandemic has further highlighted the need for redirecting capital flows toward sustainable activities, making our economy and society more resilient against shocks. Governance factors primarily involve the corporate governance practices prevalent in the entity reflecting the different rights and responsibilities among its different stakeholders management, board of directors, employees, lenders, shareholders, customers, and suppliers. It also encompasses the corporate's business conduct and practices related to transparency and disclosure. This chapter will focus on the contemporary issues of including ESG in credit rating and what are the probable impacts of doing the same.

With favorable rating, companies can access international debt market easily. Moreover a favorable sovereign rating is beneficial for the overall economy of a country as better rating enables the government of the country to access the international debt market. Even capital allocation by foreign institutional investors increases which is beneficial for the equity market too.

This chapter examines the central role played by credit rating agencies in the production of “knowledge” about financial instruments. That “knowledge,” in the form of credit ratings, underpinned disintermediation in mortgage markets by giving investors confidence that they knew the risk-and-return properties of otherwise opaque collateralized debt obligations (CDOs) and mortgage-backed securities. Credit ratings helped to “standardize” structured financial products and create liquid markets. However, this cognitive machinery failed and liquidity collapsed during the current crisis. I use this failure to examine the role of institutionalized cognition in the production of market liquidity.