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This paper aims to examine how proportional appropriation systems affect the quality of financial reporting in entities controlled by local governments.

The authors examine this issue using the setting of Italian municipally owned entities (MOEs) following the implementation of a new accounting regulation that limits the spending power of the participating municipality when the owned entity reports losses. The authors apply Benford's law on net income figures using the Chi-square and Z-tests on the adjusted version of the Mean Absolute Deviation (MAD) criterion to spot any sign of low data quality. The sample, which consists of 2,120 MOEs, covers the years 2010–2019 and is evenly divided into the periods pre- and post-policy introduction.

Widespread data anomalies were detected following the introduction of the new regulation for MOEs controlled by local governments. Evidence is stronger for entities owned entirely by municipalities. The results suggest that the extent of data manipulation grows as the municipality's ownership stake increases, consistent with the hypothesis that a decrease in spending power through the appropriation of financial resources affects earnings management practices in municipally controlled entities.

This paper sheds light on government-based accounting policies by documenting evidence of somewhat inefficient responses by those responsible for the preparation of financial statements on behalf of municipally owned entities, and, accordingly, insights are provided to help review these policies so as to forestall even indirectly detrimental repercussions on public services.

This paper extends prior research in public-sector earnings management by being the first to test whether MOEs manipulate their earnings as a consequence of participating municipalities' reduced spending capability. Understanding factors influencing earnings management practices driven by governments, other than political incentives, is still an open issue.

This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.

Despite the growing body of empirical literature on earnings management in municipal bodies and enterprises on the municipal level, the theoretical foundations often remain limited. This article contributes to filling this research gap by discussing the agency relationships between entities, managers, politicians and the electorate based on the framework of political economy or, more specifically, economic theory of political action. It derives theoretical explanations for earnings management in municipally owned enterprises based on political-economic approaches and provides conceptual guidelines for further theoretical and empirical research in this field.

The article discusses three specific theoretical approaches of political economy (political budget cycles/election cycles, interest fragmentation and partisan ideology) and applies them to the governance structures of municipally owned enterprises. For each of the perspectives, predictions about the use of earnings management and its possible motivations are derived.

The study shows that political budget cycles/election cycles, interest fragmentation and partisan ideology in municipal decision-making bodies can serve as possible explanations as to which extent managers of municipally owned enterprises present manipulated financial statements. All three approaches can help to explain the prevalence of earnings management but lead to different results regarding the methods and aims of earnings management.

As a significant portion of the current literature on earnings management in municipally owned enterprises lacks a theoretical foundation, our article aims to fill this caveat. To do so, we introduce three new conceptual perspectives that explain the prevalence of earnings management in such enterprises and the political-economic environments that are more likely to produce manipulated financial statements.

The purpose of this study is to investigate the presence of psychological barriers both in the main stock market indices of the Baltic states and the most actively traded individual stocks. A psychological barrier refers to a specific price point, often at round numbers (i.e. powers of 10), that investors believe is challenging to breach, influencing their behavior and trading decisions.

We conduct uniformity tests and barrier tests, such as barrier proximity tests and barrier hump tests, to evaluate the presence of psychological barriers. Additionally, we explore variations in means and variances near these potential barriers using regression and GARCH analysis.

The findings reveal that psychological barriers do exist in the Baltic stock markets, particularly within market indices. The Estonian market index stands out with the most pronounced indications of psychological barriers. Individual stocks also display significant changes in means and variances related to potential barriers, albeit with less uniformity.

Collectively, our findings challenge the traditional assumption of random returns within the Baltic stock markets. For practitioners, the finding that psychological barriers exist opens up opportunities for investment strategies that can capitalize on them.

This study is the first to comprehensively investigate psychological barriers in the Baltic stock markets. Our results provide a valuable contribution to understanding the impact of that phenomenon on pricing dynamics, which is particularly pertinent in less-researched frontier markets like the Baltic states.

The purpose of this paper is to study the turnover of Italian shell companies (SCs)/missing traders used for tax crimes, a predicate offence of money laundering. This research could be useful for Italian obliged entities to the Anti-Money Laundering Law to detect and report SCs, as soon as possible, to the Financial Intelligence Unit for Italy.

The author uses a sample of 32 SCs that are present in the Third Penal Section of the Italian Supreme Court in the period 2018–2020, involved in tax crimes and that have presented their balance sheets for two or more years. For SCs, the author computes the averages for: the turnover growth rate from first and the maximum turnover; the years between these two values; and the years in which SCs are active. Moreover, the author verifies if SCs are similar in terms of turnover by studying the relationship between the turnover growth rate and the first turnover and the reduction of turnover standard deviations.

SCs growth is very high and they reach, on average, their peak in less than three years; SCs are active, on average, for about four years; eventually, SCs exhibit a convergence process in turnover levels.

To the best of the author’s knowledge, there are no studies that analyse the SC turnover.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

Even though the end-user satisfaction construct has gained prominence as a surrogate measure of information systems performance assessment, it has received scant formal treatment and empirical examination in the data analytics systems field. In this respect, this study aims to examine the vital role of user satisfaction as a proxy measure of data analytics system performance in the financial engineering context.

This study empirically validated the proposed model using primary quantitative data obtained from financial managers, engineers and analysts who are working at Jordanian financial institutions. The quantitative data were tested using partial least squares-based structural equation modeling.

The quantitative data analysis results identified that technology quality, information quality, knowledge quality and decision quality are key factors that enhance user satisfaction in a data analytics environment with an explained variance of around 69%.

This empirical research has contributed to the discourse regarding the pivotal role of user satisfaction in data analytics performance in the financial engineering context of developing countries such as Jordan, which lays a firm foundation for future research.

Financial statement fraud has become a global concern, and auditors are increasingly focused on identifying and investigating it. Auditors may play a crucial role in investigating and reducing financial statement fraud, and this is particularly important in developing countries where fraudulent practices are more prevalent due to the lack of strict regulations and oversight. This study investigates whether enhanced audit quality has an impact on reducing financial statement fraud. The primary aim is to recognize whether a higher level of audit quality relates with a decrease in fraudulent activities in Indonesia, which is one such country that has not yet adopted IFRS.

This study investigates the effect of audit quality, as measured by audit tenure, audit fee, and audit size, on the dependent variable of financial statement fraud, as indicated by Dechow F-value. The sample for this study comprises 951 observations from 2015 to 2020, and the research design utilizes a panel data approach. To test the main hypothesis, OLS, and GMM estimation techniques are employed.

The analyses reveal a negative relationship between audit tenure and financial statement fraud. This suggests that shorter audit tenure may be associated with an increased risk of financial statement fraud. This heightened risk could stem from auditors having limited time to thoroughly understand the company's operations and internal controls, potentially making it more challenging to detect and prevent fraudulent activities perpetrated by the client. Conversely, a positive relationship is identified between audit fees and financial statement fraud, suggesting that companies paying higher fees may be engaging auditors less adept at detecting fraudulent activities. Furthermore, a negative relationship is observed between Big-5 and financial statement fraud, which may be due to the greater resources, expertise, quality control, scrutiny, reputation, and ethical conduct of Big-5 audit companies.

This study only focused on listed companies in Indonesia, therefore, caution should be exercised when generalizing the findings to other developing and Muslim countries such as Malaysia. The findings may differ due to the adoption of IFRS in Malaysia. As such, it is important for future studies to include Malaysia as a sample and compare the results with those of Indonesia. This comparison would demonstrate the impact of IFRS adoption on the relationship between audit quality and financial statement fraud and provide insights for policy makers in Indonesia.

The findings of this study have important implications for developing countries that have been shown to be more susceptible to fraud than developed countries. This study contributes to the existing research on the role of audit quality in reducing financial statement fraud and emphasizes the need for auditors and accountants to take a proactive approach in detecting and investigating financial fraud.

This study is a new study because it investigates the relationship between audit quality and financial statement fraud in Indonesia, a developing Muslim country that has not yet adopted International Financial Reporting Standards (IFRS). The study provides valuable evidence on the unique factors that influence fraud in Indonesia and fills a gap in the literature as previous studies on this topic have largely focused on developed countries. Additionally, the study recommends that policymakers in Indonesia consider implementing IFRS to improve the reliability of financial reporting and strengthen the effectiveness of the auditing process, thus reducing the incidence of fraud.