Search results

The increasing availability of eXtensible Business Reporting Language (XBRL) financial statements motivates additional investigation of whether XBRL’s search-facilitating technology (SFT) and enhanced viewing capabilities facilitate information search and improve financial analysis decision quality and efficiency. This experiment investigates how using XBRL technology to view financial statements influences novice investors’ decision quality by affecting decision processes such as search strategy and effort, as well as decision efficiency (accuracy/effort) in a financial statement analysis task. In the experiment, randomly assigned student participants (n = 102) invested in companies using either static PDF-formatted or XBRL-enabled financial statements. No differences in decision quality (i.e., accuracy) due to technology use were observed. However, participants in the XBRL condition examined less information, used more directed search processes, and evidenced greater efficiency than did participants assigned to the PDF condition. Hence, the results suggest that XBRL SFT affects the use of differing decision processes relative to PDF technology.

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 chapter describes three active learning activities developed for use in the introductory financial accounting class: an interview with a financial statement user, an internal control paper, and a financial statement project where students analyze two competing businesses. We gathered student surveys and direct assessment data to see if these activities add value to the introductory accounting course.

The learning activities were originally developed using Fink’s (2003) Taxonomy of Significant Learning, aligning the activities with Fink’s learning dimensions, which also support the higher order learning skills in Bloom’s revised taxonomy. Students completed surveys by comparing how well traditional class activities (i.e., homework and tests) and the new activities support the core competencies of the American Institute for Certified Public Accountants (AICPA). We also asked students open-ended questions on how they felt about these new activities. Researchers then compared pre- and postadoption assessment data to investigate the impact of the new learning activities on class completion rates and grades.

Based on faculty comments and student survey results, the three active learning assignments appear to be more effective in developing many of the AICPA’s core competencies and real world skill sets valued by professionals, providing more value than traditional teaching methods. In addition, the passing rates in the course at the Youngstown State University increased by 12% after adopting the learning innovations.

The purpose of this paper is to teach students the fundamental and most critical aspects of performing a financial statement risk assessment, a skill vital to help ensure both auditor and public‐company compliance with guidance found in the Sarbanes‐Oxley Act of 2002 (SOX), the SEC's Interpretative Guidance regarding Management's Report on Internal Control over Financial Reporting, the control deficiency evaluation framework found in Auditing Standard No. 5 (AS5) of the Public Company Accounting Oversight Board (PCAOB), and the Committee of Sponsoring Organizations of the Treadway Commission (COSO).

This instructional case study helps students assess the impact of a set of hypothetical internal control deficiency risks in various industries, including inherent and residual financial statement risk assessment, and concludes with determining which identified internal control weaknesses are significant deficiencies and material weaknesses in internal control. Included in the financial statement residual risk assessment process are example entity‐level and process‐level controls described in COSO. Learning objectives, implementation guidance, and the efficacy of using the case study in the undergraduate or graduate auditing or accounting information systems courses are also provided.

The results of classroom testing of the case study at two universities provides evidence the case study increases student understanding of the implications of internal controls and their impact on the reliability of the financial statements significantly. Students also found the case to be challenging, interesting, relevant, clear, understandable, and a realistic approximation of what they might expect to encounter in the real‐world when performing a financial statement risk assessment.

The case study includes the development of skills important to students in performing financial statement risk assessments, either as an auditor or when working in a private industry environment, including making professional judgments related to risk assessment.

Presents a case study demonstrating financial statement ratio analysis (FSRA). This analysis matches company to industry data and builds sales forecasting models. FSRA imputes forecast standards of sales and costs, and applies them to a budgeted financial statement variance analysis for the EE (electronic and electrical) industry. Develops the concept of industry base standards, integrating them into the more traditional statistical and accounting concepts of quality control standards. Provides an implementation example, and reviews possible improvements to the current methodology and approach. Uses a similar methodology to forecast the stock market value with some exceptions. Models sales and costs of an individual company and an industry based largely on aggregate industry databases. For this purpose, uses a multivariate linear trend regression analysis for the sales forecasting model. Defines and tests related hypotheses and evaluates their significance and confidence levels. For an illustration uses the EE industry and the APM company. Also demonstrates a microcomputer‐based FSRA software that speeds, facilitates, and helps to accomplish the stated objectives. The FSRA software uses industry financial statement databases, computes financial ratios and builds forecasting models.

This paper aims to explore the effectiveness of fraud prevention and detection techniques, including data analytics, machine learning and data mining, and to understand how widespread the use of data analytics is across different sectors and to identify and understand the potential barriers to implementing these techniques to detect and prevent fraud.

A survey was administered to 73 Irish businesses to determine to what extent traditional approach, data mining or text mining are being used to prevent or detect fraudulent financial reporting, and to determine the perception level of their effectiveness.

The study suggests that whilst data analytics is widely used by businesses in Ireland there is an under-utilisation of data analytics as an effective tool in the fight against fraud. The study suggests there are barriers that may be preventing companies from implementing advanced data analytics to detect financial statement fraud and identifies how those barriers may be overcome.

In contrast to the majority of literature on big data analytics and auditing, which lacks empirical insight into the diffusion, effectiveness and obstacles of data analytics, this explanatory study contributes by providing useful insights from the field on big data analytics. While the extant auditing literature generally addresses the avenues of big data utilisation in auditing domain, our study explores particularly the use big data analytics as a fraud prevention and detection techniques.

Despite the relevance of financial information relating to logistics service providers (LSPs), recent research has paid little attention to the financial analysis of LSPs. The aim of this paper is to examine the balance sheet structure of LSPs in order to find out if there are differences between single providers or defined LSP groups (clusters), respectively. Furthermore, the dependency of asset, capital and liquidity structures on LSPs specific characteristics is pointed out. Finally, we show which financial indicators positively influence profitability.

A total of 150 quoted LSPs from all over the world, allocated to six different clusters depending on scope of service were examined. A detailed balance sheet analysis using contingency theory, complemented by a correlation analysis, provides information about the financial structure, similarities and differences within and in-between the LSP clusters.

It was found that there are many differences regarding the financial structures of LSPs. The asset and liquidity structure of LSPs show significant differences, while the capital structure is mostly homogeneous. Profitability is achieved in various ways: Focusing on high net profit margin or asset turnover rates.

Only quoted LSPs are analyzed. With this broad research approach the authors point out the range of possibilities for financial statement analysis of LSPs and demonstrate the potential for future research.

Financial analysis yields information for making strategic decisions including organic growth, outsourcing, mergers and acquisitions or cooperation between LSPs.

This paper contributes to further performance examinations of LSPs by providing a profound financial statement analysis with potential benefits for logistics executives, analysts and researchers.

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

This study examines how the presence of labor unions affects a firm’s pay disparity between executives and employees and its financial statement comparability.

It uses firm-level labor union data in Korea and applies regression analyses to a sample of 1,776 firm-year observations from 2004 to 2008.

The authors find that unionized firms have a smaller pay disparity between executives and employees than non-unionized firms, suggesting that labor unions place pressure on the pay structure. Unionization also lowers financial statement comparability, which helps managers of unionized firms maintain information asymmetry. Further, this negative relationship between unionization and financial statement comparability is stronger in non-chaebol firms, implying that they are more motivated than chaebol firms to reduce their financial statement comparability in response to the presence of labor unions. In addition, the negative relationship between unionization and financial statement comparability is pronounced in profit-making firms, firms with less analyst following, firms with fewer foreign investors and firms in more competitive product markets.

The finding that firms adjust comparability in response to labor unions interests regulators and policymakers, who emphasize the role of comparability in providing usefulness to information users.

The findings add to the existing literature on the effect of labor unions on firms' pay structures and accounting choices.