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This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure.

This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries.

The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital.

The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms' choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital).

The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results' robustness and generalizability.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

I seek to identify whether cash flow management can affect the performance and risk of the Greek listed companies.

This study examines the relationship of cash flow management with performance and risk, using a sample of 80 non-financial companies listed in the Athens Exchange. The study covers the period 2018–2022, and panel data analysis is applied. Both financial performance and stock return are taken into consideration, while risk concerns the volatility of the companies’ share prices. The various explanatory variables used include the net cash flow, free cash flow, cash conversion cycle days, cash flow from operating activities, cash flow from investing activities, cash flow from financing activities, inventory days, customer days and supplier days.

The empirical results provide evidence of a positive relationship between financial performance and net cash flow and free cash flow. In addition, operating cash flow is positively related to financial performance. The opposite is the case for investing and financing cash flow. Finally, some evidence of a negative relationship between financial performance and inventory and customer days is provided too. On the other hand, stock return and risk are not related to the cash flow management variables at all.

To the best of my knowledge, this is one of the few studies to examine the relationship of cash flow management with performance and risk, using data from the Greek stock market. The results can form an effective selection tool for investors seeking Greek companies with the highest financial performance potential, which may reward them with higher dividends.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

According to the Association of Certified Fraud Examiners, financial statement fraud represents the smallest amount of fraud cases but results in the greatest monetary loss. The researcher previously investigated the characteristics of financial statement fraud and determined the presence of 16 fraud indicators. The purpose of this study is to establish whether investors and other stakeholders can detect and identify financial statement fraud using these characteristics in an analysis of a company’s annual report.

This study analyses a financial statement fraud case, using the same techniques that were previously applied, including horizontal, vertical and ratio analysis. These are preferred because stakeholders have relatively easy access to them.

The findings show several fraud characteristics, with a few additional ones not previously found prevalent. Financial statement fraud thus tends to differ between cases. It is also easier to detect and identify fraud indicators ex post facto.

This study is a practical case showing that financial statement fraud can be detected and identified in the financial statements of companies that commit fraud.

This study compares the motives of holding cash between developed (Australian) and developing (Malaysian) financial markets.

For the period 2006–2020, the t-test, fixed-effect and generalised method of moment (GMM) model have been applied to a sample of 1878 (1,165 Australian and 713 Malaysian) firms.

The empirical results reveal that firms in developed financial markets hold higher cash compared to the developing financial markets. The findings confirm that motives to hold cash differ between developed and developing financial markets. The GMM findings further show that cash holdings (CH) in Australia are higher due to higher ratios of cash flow, research and development (R&D) and return on assets (ROA), and lower due to larger dividend payments. In the Malaysian market, however, cash flows and R&D are ineffectual, ROA falls and dividend payments rise CH.

The study helps managers, practitioners and investors understand that firms' distinct economic, institutional, accounting and financial environments are important. To attain the desired outcomes, they must thus comprehend and consider these considerations while developing suitable liquidity strategies.

To the authors' best knowledge, this is the initial research demonstrating how varied cash motives and their ramifications are in developed and developing financial markets. Therefore, this study identifies the importance that CH motives varied among financial markets and that findings from a particular market cannot be generalised to other markets because of the market and financial structural variations.

This study aims to explore the determinants (bank-specific, industry-specific and macroeconomic) of income diversification across interest income and non-interest income as well as for non-traditional income sources (non-interest income) from 2004–2005 to 2021–2022.

An unbalanced data set comprising 110 Indian commercial banks with 1480 observations is sampled in this study. Because of the bounded nature of the dependent variables (proxies of income diversification), the panel Tobit regression model is used.

The findings reveal that income diversification is positively influenced by bank size, technological advancements, cost–income ratio, return on assets, market competition and inflation in the economy. However, the decision to diversify income sources is adversely impacted by the capital ratio, GDP and financial intermediation ratio. Moreover, factors such as asset quality (loan loss provisions) and liquidity ratio do not directly influence the diversification strategies in the Indian banking industry.

The present study uses an extensive set of variables to provide insights into key factors for bank managers, regulators and policymakers to consider before developing diversification strategies.

To the best of the authors’ knowledge, this is the first study to examine the various bank-specific and macroeconomic determinants that affect income diversification in the Indian banking sector. The current study also investigates new variables such as technological advancements and a market concentration index for measuring competition, which have not been investigated in existing literature concerning bank income diversification in the Indian context.

The phenomenon of corruption requires extra handling to achieve zero corruption. The purpose of this paper is to examine the integrated governance, risk management and compliance (GRC) implementation, the quality of internal audits and management's commitment to improving the ability to detect corruption and its impact on the company's financial performance.

This paper used primary and secondary data. Financial statement data and survey results from participants in 69 state-owned companies were analyzed using the Partial Least Square method.

There was a positive and significant effect of the integrated GRC implementation, quality of internal audit and management's commitment to increasing the organization's internal capability in detecting corruption. However, the failure to detect corruption mediates the effect of management commitment on financial performance. Besides, the organization's three internal factors could be better because their functions could be more optimal and require further improvement.

State-owned companies are continuing to be restructured, so these results can be helpful for now. However, they must update continuously with developments related to the composition and classification of state-owned companies.

Organizations can improve their ability to detect corruption in the workplace by using an early warning system such as the integrated GRC, internal audit quality and a high commitment from management.

To the author's limited knowledge, empirical research on integrated GRC implementation, internal audit quality and management commitment are still rare if they improve the detection of corruption ability. It uses the factors that cause corruption in the fraud hexagon to analyze the financial performance.

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 case is written based on publicly available data from primary sources such as the company’s annual reports, company website and the company’s presentations, as well as from secondary sources comprising newspaper articles, research papers, research magazines, magazine articles, industry reports, research reports, etc. as indicated in the references. The company’s financials and peer data are sourced from the Thomson Reuters Eikon database.

The case examines the financial position of Macy’s, Inc., America’s largest and one of the oldest premier departmental stores, with a consolidated annual turnover of US$18,097m in the fiscal year 2020/2021 (FY, 2021). Over the previous few years, the company had been struggling with decreasing market share and profitability mainly due to increasing competition from online retailers and deep discounters, which was affecting the company’s share price. With the appointment of a new chief executive officer (CEO) in fiscal year (FY) 2017, Macy’s, Inc. undertook several changes to revive its financial health and improve its market share. However, it still registered heavy losses of US$3,944m in the FY 2020/2021, the company’s first time in the past decade. With many retailers filing for bankruptcy, was there more that Macy’s could do to improve the company’s position and regain lost investor confidence? Will its entry into emerging markets play a crucial role in its turnaround?

The case can be used in undergraduate and postgraduate courses such as accounting for managers, financial statement analysis, management accounting, introduction to accounting and advanced financial statement analysis. The case can also be effectively used to understand the primary fundamental analysis of the company that involves understanding the company’s positioning and strengths, weaknesses, opportunities and threats analysis. The case would also help business management and entrepreneurship students to get a preliminary idea about the change management process. Finally, the case can be used to familiarize students with using Microsoft Excel to build financial analysis worksheets.

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