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The purpose of this paper is to examine how publicly available accounting risk metrics influence the aggressiveness of managers’ discretionary accounting decisions by making those decisions more transparent to the public.

The experiment used a 2 × 3 between-participants design, randomly assigning 122 financial reporting managers among conditions in which we manipulated whether the company was currently beating or missing analysts’ consensus earnings forecast and whether an accounting risk metric was indicative of low risk, high risk or a control. Participants chose whether to manage company earnings by deciding whether to report an amount of discretionary accruals that was consistent with the “best estimate” (i.e. no earnings management) or an amount above or below the best estimate.

Aggressive (income-increasing) earnings management is deterred when managers believe such behavior will cause their firm to be flagged as aggressive (i.e. high risk) by an accounting risk metric. Some managers attempt to “manage” the risk metric into an acceptable range through conservative (income-decreasing) earnings management. These results suggest that by making the aggressiveness of accounting choices more transparent, public risk metrics may reduce one type of earnings management (income-increasing), while simultaneously increasing another (income-decreasing).

The operationalization of the manipulated variables of interest may limit the study’s generalizability.

Users of accounting risk metrics (e.g. investors, auditors, regulators) should be cautious when relying on such risk metrics that may be of limited reliability and usefulness due to managers’ incentives to manipulate their companies’ risk scores by being overly conservative in an effort to prevent being labeled “aggressive”.

By increasing the transparency of the aggressiveness of accounting choices, public risk metrics may reduce one type of earnings management (income-increasing), while simultaneously increasing another (income-decreasing).

This paper aims to investigate whether the Section 404 of Sarbanes–Oxley Act (SOX 404) changed the way banks use accounting information to price corporate loans.

The study uses a sample of 1,173 US-listed firms that issued syndicated loans both before and after their compliance with SOX 404 to analyze the changes in loan spread’s sensitivity to some key accounting metrics such as ROA, interest coverage, leverage and net worth.

The study finds that the interest spread’s sensitivity to key accounting metrics, most noticeably for ROA, declined following the borrower’s compliance with the requirements of SOX 404. The decline was not explainable by borrowers that disclosed internal control weaknesses but concentrated among borrowers suspected of real earnings management (REM).

By examining the effects of SOX 404 on banks’ pricing process, this study augments the literature on SOX’s economic consequences. The findings suggest that lenders perceive little new information from SOX 404 disclosures of internal control deficiencies and are cautious about the accounting information provided by REM borrowers. It also extends the research on the use of accounting information in debt contracting. By examining loan interest’s sensitivity to accounting metrics, it broadens the concept of debt contracting value of accounting information to include accounting’s usefulness for assessing credit risk at loan inception.

This paper aims to investigate the role of upper management in designing performance measurement systems (PMS) that account for external turbulence of the organization and to show how this PMS design for turbulence impacts organizational resilience and distribution service performance.

Hypotheses are developed by integrating management accounting and strategic management perspectives into supply chain management and subsequently tested based on data from 431 logistics organizations (i.e. both logistics companies and internal logistics departments of manufacturing and retailing companies).

Attention focusing usage type of the PMS by the upper management fosters incorporating the element of risk into the PMS of the company. Further, PMS design for turbulence enhances organizational resilience, and, indirectly, this also leads to improved distribution service performance.

This paper is the first to introduce the concept of PMS design for turbulence to the literature and to show that it is relevant for supply chain risk management by fostering the capabilities and the performance of logistics organizations. Further, it is shown that a seemingly detached issue such as the general PMS use focus of the upper management impacts supply chain risk management.

This paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.

The author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.

The study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.

The paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.

To the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.

The purpose of this paper is the development for a conceptual framework with regard to the risk management of real estate positions as foundation for transaction decisions. In this context, the current market environment and legal obligations are the main drivers for market participants to improve their risk management practices. Based on this environment, a practical but science backed model is outlined.

The paper uses a conceptual approach based on the existing literature to develop a practical decision support system. In addition, the current risk management best practices are outlined to illustrate the corporate and methodological foundation for the decision support system.

The conceptual model development reveals a clear necessity for the supplementation of price to value measures. Additional measures are derived from theoretic considerations based on Monte Carlo Simulation approaches to the risk management of property investments. These additional risk metrics support investors in order make risk-appropriate decisions.

The resulting decision support system can be applied to the risk management of transaction decisions. Here, the model can be applied in any investment decision to support portfolio management considerations from a comprehensive risk management perspective. Investors can implement the system as part of their transaction procedure.

The existing body of literature mainly focuses on macroeconomic ratios in the context of decision support. In contrast, the present paper reveals a corporate decision support system, which is supposed to foster decisions of market agents especially with regard to potential price and value divergences and tightening legal obligations.

Recent public policy initiatives seek greater transparency in financial reporting through an honest, balanced and thorough management discussion of company performance in the annual report. Management’s discussion invariably includes key performance indicators, such as financial ratios, relevant to external stakeholders. We model the impact of accounting estimates, assumptions, choices and errors on the risk of misleading financial ratios. This framework is illustrated through good and bad examples of financial reporting practices and by simulation of financial data of public companies. We provide a structured approach to inform policymakers, auditors and other stakeholders of the incremental financial reporting risk that accompanies current regulatory efforts.

The United States (US) sugar program protects domestic sugar farmers from unrestricted imports of heavily-subsidized global sugar. Sugar-using firms (SUFs) criticize that program for causing US sugar prices to be higher than world sugar prices. This study examines the financial performance of publicly traded SUFs to determine if they are performing at an economic disadvantage in terms of accounting profitability, risk and economic profitability compared to other industries.

Firm-level financial accounting and market data from 2010 to 2019 were utilized to construct financial metrics for publicly traded SUFs, agribusinesses and general US firms. These financial metrics were analyzed to determine how SUFs compare to their agribusiness peer group and general US companies. The comprehensive financial analysis in this study covers: (1) accounting profit rates, (2) drivers of profitability, (3) economic profit rates, (4) trend analysis and (5) peer comparisons. Quantile regression analysis and Wilcoxon–Mann–Whitney statistics are employed for statistical comparisons.

Regarding various profitability and risk measures, SUFs outperform their agribusiness peers and the general benchmark of all US firms in terms of accounting profit rates, risk levels and economic profit rates. Furthermore, compared to other US industries using the 17 French and Fama classifications, SUFs have the highest return on investment and economic profit rate―measured by the Economic Value Added® margin―and the second-lowest opportunity cost of capital, measured by the weighted average cost of capital.

This study finds nothing to suggest that the US sugar program hinders the financial success of SUFs, contrary to recent claims by sugar-using firms. Notably in this analysis is the evaluation of economic profit rates and a series of robustness techniques.

The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

The accounting literature has traditionally focused on firm-level studies to examine the capital market implications of earnings and other accounting variables. We first develop the arguments for studying capital market implications at the aggregate level as well. A central issue is that diversification makes equity investors at least partially and potentially almost completely immune to several firm-level properties of earnings by holding diversified portfolios. Diversification is particularly important when assessing the welfare consequences of random errors in accounting measurement (imperfect accruals) and, to the extent it is independent across firms, of deliberate manipulation (earnings management). Consequently, some firm-level metrics of association, timeliness, value relevance, conservatism and other earnings properties do not map easily into investor welfare. Similarly, earnings-related risk manifests itself to equity investors largely through systematic earnings risk (covariation with aggregate earnings and/or other macroeconomic indicators). We conclude that the design and evaluation of financial reporting must adopt at least in part an aggregate perspective. We then summarize the literature in accounting, economics and finance on aggregate earnings and stock prices. Our review highlights the importance of studying earnings at the aggregate level.

SCOR 10.0, released in late 2010, is the second version of the supply chain operations reference model (SCOR) to incorporate risk management processes, metrics and best practices. Given the paucity of studies that have explored the coverage and integration of supply chain risk management (SCRM) within SCOR, the analysis and suggested improvements for SCRM are designed to enhance SCOR’s collaborative and coordinated management of supply chain (SC) risks. The paper aims to dicsuss these issues.

Critical analysis was used to analyse the coverage and integration of SCRM within SCOR 10.0.

Discrepancies were identified in how SCRM has been incorporated into SCOR, including issues with the hierarchical representation of SCRM processes, metrics, best practices and skills. These may potentially propagate into difficulties in embedding risk management processes within other SC processes, visualizing risk metrics in a SC’s value hierarchy and reconciling SCOR’s SCRM with organizational enterprise risk management.

This paper is limited to theoretical analysis of the coverage and integration of risk in SCOR 10.0. Once the issues identified are remedied, the subsequent suggested improvements require validation through empirical testing.

Despite SCOR’s wide acceptance as a reference model in managing SC operations, there has been no investigation of its approach to SCRM. The analysis addresses this lack of prior investigation by analysing SCRM in the latest version, SCOR 10.0. The paper identifies deficiencies and suggests amendments regarding SCRM’s coverage and integration of SCRM.