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The aim of manuscript is to analyze and identify determinants of honest accounting errors leading to financial restatements based on data from SEC database and from annual reports. Reason for this study is that accounting errors are expensive for companies that need to change already published financial statements and have impact on company reputat...
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... Some studies [32][33][34] compared different models and achieved more accurate classification performance. However, they also pointed out the limitations of certain models, and the performance of the same model can vary significantly based on different accounting standards, regions, and financial indicators. ...
... Then, the predictions of the individual trees are integrated by voting (for fraud classification) to obtain a more robust and accurate model. This method pioneered the use of ensemble learning in the Decision Tree method and achieved comparable results in previous studies [32,52]. ExtraTrees [53] employs more randomness in the construction of each tree to make it more robust to noisy data, achieves faster training speeds, and generally performs well in accuracy [34]. ...
With the rapid development of the capital market, financial fraud cases are becoming increasingly common. The evolving fraud strategies pose significant threats to financial regulation, market order, and the interests of ordinary investors. In order to combine the generalization performance of different machine learning methods and improve the effectiveness of financial fraud prediction, this paper proposes a novel financial fraud prediction framework based on stacking ensemble learning. This framework, based on data from listed companies, comprehensively considers financial ratio indicators and non-financial indicators. It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, our proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. We also conduct case studies on specific companies and industries, confirming the significant interpretability and practical applicability of our proposed framework. The results show that the recall rate and Area Under Curve (AUC) of our framework reach 0.8246 and 0.8146, respectively, surpassing mainstream machine learning models such as XGBoost and LightGBM in existing studies. This research is of great significance for predicting the increasing number of financial fraud cases, providing a reliable tool for financial regulatory institutions and investors.
... Green corporate reputation refers to the image of a company as an entity committed to environmentally friendly and sustainable business practices (Papík & Papíková, 2020) . In the mining industry, green reputation can serve as a significant differentiation factor. ...
... O que se verifica na literatura sobre o tema é que as reapresentações estão associadas a empresas com desempenhos inferiores (Papík & Papíková, 2019), com resultados mais voláteis (Wu et al., 2016) e com menor previsibilidade dos ganhos potenciais (He et al., 2017). Como consequências, as reapresentações tendem a estar associadas à maior percepção de risco (Amel-Zadeh e Zhang, 2015) e perda de valor de mercado (Ali et al., 2018). ...
RESUMO O objetivo do presente estudo foi verificar se as empresas que reapresentaram as demonstrações compensam seus acionistas nos anos subsequentes à ocorrência deste evento. A literatura anterior tem sugerido que as empresas com menor qualidade das informações financeiras tendem a pagar dividendos menores. Contudo, uma explicação alternativa é que as empresas que divulgam informações financeiras de menor qualidade tendem a buscar sinalizar ao mercado maior solidez compensando os acionistas com maiores dividendos. Para alcançar o objetivo, analisamos dados de 275 empresas listadas na Brasil, Bolsa, Balcão (B3) no período de 2010-2020. Os dados foram analisados a partir da estatística descritiva, testes de diferenças entre as médias e análise de regressão com dados em painel. Os resultados evidenciaram que as empresas que reapresentam suas demonstrações buscam compensar seus acionistas com maiores dividendos quando comparadas àquelas que não reapresentaram. Além disso, no ano subsequente esses dividendos tendem a ser superiores, reforçando a hipótese de uso dos dividendos como mecanismo de sinalização, redução de assimetria e compensação dos acionistas. Os resultados têm potencial de contribuição para pesquisadores interessados no tema, gestores, contadores auditores, reguladores e demais partes interessadas em compreender as implicações das reapresentações na política de dividendos das empresas listadas.
... The goal of this research is not only to assess the differences between the two groups of companies but also to evaluate which of the eight ratios in the Beneish score individually influence the probability of identifying fraud for companies. Some authors have also modified the Beneish model to the conditions in their countries, primarily based on logistic regression (Ozcan, 2018;Erdogan & Erdogan, 2020;Papik & Papikova, 2020;Svabova et al., 2020). In this case, the logistic regression was used to analyze the interaction effects of the ratios in the Beneish model. ...
This paper investigates irregularities in financial statements by applying the Beneish and Roxas models to Polish firms listed on the Warsaw Stock Exchange from 2015 to 2020. The total sample included 110 observations. The sample comprised companies that had received an adverse or disclaimer opinion by the auditors, but had not been fined by the Polish Financial Supervision Authority (KNF Board). The control firms were selected based on the industry as selected by the
standard industrial classification code and on the financial year, with minimizing the difference in the size of total assets. The results indicate that the Roxas model revealed greater accuracy than the Beneish model on the tested sample. The use of logistic regression allowed a modification of the Beneish model to align it with the conditions of the Polish market. The modified Beneish model showed greater accuracy for the tested sample and companies fined by the KNF Board.
... Tujuan utama dari laporan keuangan yang disusun secara bermakna adalah agar laporan keuangan dapat dibandingkan dengan laporan keuangan perusahaan lain (Papík dan Papíková, 2020). Perbandingan yang berarti antara perusahaan di seluruh industri mengharuskan laporan keuangan disiapkan sesuai dengan Prinsip Akuntansi Berterima Umum (PABU) yang telah ditetapkan di Negara masing-masing. ...
Perubahan standar akuntansi menjadikan perusahaan terdaftar untuk patuh mengikuti ketentuan tersebut. Akibat penerapan PSAK baru atau revisi menyebabkan perusahaan melakukan penyajian kembali laporan keuangan. Penelitian ini bertujuan untuk mengetahui fakta penyajian kembali laporan keuangan perusahaan publik di Indonesia. Metode penelitian yang digunakan adalah kuantitatif deskriptif dengan menganalisis laporan keuangan dan catatan atas laporan keuangan yang telah diaudit. Hasil penelitian ini menunjukkan bahwa 69 persen perusahaan melakukan penyajian kembali laporan keuangan. Urutan sektor yang paling banyak melakukan penyajian kembali adalah (1) sektor basic industry & chemical, (2) sektor consumer goods industry, kemudian (3) sektor agriculture. Penyajian kembali laporan keuangan mayoritas terjadi pada tahun 2015 dan 2018 dengan PSAK 24, PSAK 69, dan reklasifikasi sebagai penyebab utamanya. Penelitian ini memberikan implikasi pada para pengguna laporan keuangan untuk memperhatikan dampak perubahan standar akuntansi yang mungkin menjadi pertimbangan dalam pengambilan keputusan.
... It is necessary to focus on the values of discretionary accruals. If the company shows extreme values of discretionary accruals in several consecutive periods, it does not matter whether they are positive or negative, as long as they are the same in the periods (only positive or only negative), it is appropriate to find out if these values are caused by the real situation between the company and the market or if the company modified these data for various reasons (Papik and Papikova 2020). Increased attention should also be paid if the company meets several established categories for individual values of discretionary accruals. ...
Various models have been created all around the world to identify enterprises that manipulate their earnings. These earnings management techniques aid businesses in enhancing their financial performance or gaining some competitive advantages. The primary goal of this article was to identify the firm-specific characteristics that affect how businesses manage their earnings using a sample of 15,716 businesses from various economic sectors in the Slovak environment during a 3 year period. The level of earnings management was measured by discretionary accruals using the Kasznik model. In this paper, a correspondence analysis using the chi-square distance measure was applied to find the dependence between the earnings management practices and firm-specific features (firm size, legal form, and sectoral classification). The results of the study indicate that aggressive (income-increasing) earnings management practices are typical of small enterprises with a public limited ownership structure, mostly in sectors R and M (using the NACE sectoral classification). Conservative (income decreasing) practices can be observed in enterprises in the sectors J or F, and they are also used by medium-sized enterprises and those with private limited ownership structure. The results revealed that large enterprises do not tend to manipulate their earnings, as well as enterprises operating in sector K. The insights of this study may provide important and useful information for shareholders and regulators in evaluating determinants that are effective in mitigating earnings management practices. Authorities, regulators, analysts, and auditors may find the importance of the discovered variances helpful in identifying various strategies and techniques for earnings manipulation that may differ among industries according to their typical characteristics.
... In addition to these studies, several other studies applied Beneish variables to build accounting fraud detection models (Halilbegovic et al., 2020;Papík and Papíková, 2020;Š vábová et al., 2020;Tarjo & Herawati, 2015). Halilbegovic et al. (2020) developed a prediction model on 4,580 small and medium companies from the Federation of Bosnia and Herzegovina (FBiH) using LR. ...
... (2020) and Papík and Papíková (2020) achieved a similar accuracy (62-80%) in detecting accounting fraud or unintentional accounting errors leading to financial restatements by applying DA and LR. Although all these studies agreed on the usefulness of Beneish variables in the prediction of accounting fraud, Beneish variables have not yet been widely used to develop prediction models except for the research by Gepp (2015), Kim et al. (2016), and Papík and Papíková (2020). ...
... The comprehensive list of variables and associated formulas are listed in Table 3, along with studies that previously used these variables. Variables x 1, x 5, x 7, x 9, x 11, x 15, x 16 and x 17 are original Beneish variables used by Gepp (2015), Kim et al. (2016), Papík and Papíková (2020) and Tarjo and Herawati (2015). The remaining variables are the most common financial analysis ratios, as previously used by Dutta et al. (2016), Kim et al. (2016), Lin et al. (2015), Gaganis (2009), Wang (2020), Yao et al. (2019) and Jan (2018). ...
The accounting fraud detection models developed on financial data prepared under US Generally Accepted Accounting Principles (GAAP) in the current literature achieve significantly weaker performance than models based on financial data prepared under different accounting standards. This study contributes to the US GAAP accounting fraud data mining literature through the attainment of higher model performance than that reported in the prior literature. Financial data from the 10-K forms of 320 fraudulent financial statements (80 fraudulent companies) and 1,200 nonfraudulent financial statements (240 nonfraudulent companies) were collected from the US Security and Exchange Commission. The eight most commonly used data mining techniques were applied to develop prediction models. The results were cross-validated on a testing dataset and then compared according to parameters of accuracy, F-measure, and type I and II errors with existing studies from the US, China, Greece, and Taiwan. As a result, the developed predictive models for accounting fraud achieved performance comparable to those achieved by models built on data from other accounting standards. Moreover, the developed models also significantly outperformed (accuracy 10.5%, F-measure 16.1%, type I error 12.2% and type II error 15.2%) existing studies based on US GAAP financial data. Furthermore, this study provides an extensive literature review encompassing recent accounting fraud theory. It enhances the existing US fraud data mining literature with a performance comparison of studies based on other accounting standards.
... Such professionals are considered to possess and are adequately informed cum knowledgeable in the appropriate use of diverse digital investigative approaches and techniques capable of exposing/deterring cyber related financial crimes/fraudulent activities and unearthing fictitious financial disclosures and possible manipulations in electronic and non-electronic financial reporting practices of corporate organization. Oyebisi, Wisdom, Olusogo and Ifeoluwa (2018) cited in Papík and Papíková (2019) observe that studies from less developed countries as Nigeria affirm that a positive correlation relationship exist between forensic accounting practices and fraud prevention. ...
Amid stiff competition currently playing out at the international capital market in response to unfolding oil price crash at the international oil market, high technological advancement and possible global economic recession with far reaching consequences on businesses, the quality of financial reporting across the globe despite due compliance with disclosure guidelines of the International Financial Reporting Standards (IFRS) in many reporting jurisdictions across the globe may once again witness undue exposures to the rough handles of management executives of corporate organizations. In bid to proactively forestall feared speculations among terrified Investors whose value of investment dwindles on daily basis, there is urgent need for professional Accountants such as Internal Auditors and Forensic Accountants to upgrade digitally on their existing forensic accounting skills so as to be able to ease effectively, the increasing tension in the corporate financial reporting environment. To this end, the study intends to determine whether the application of forensic digital techniques effectively predicts tendencies of material misstatement in pre and post IFRS financial regimes in Nigeria. Being a secondary data wholly sourced research, a total of 50 manufacturing companies in Nigeria were purposively sampled with pre and post IFRS annual reports for the years 2006-2016 assessed using digital forensic technique such as Probit Model e-enabled spreadsheet. Relevant hypotheses were tested using Multiple Regression Analytical tool and the Mann Whitney U test. Result of the analyses showed that appropriate application of digital forensic technique deployed effectively predicts tendencies of material misstatement in the pre and post IFRS Financial Statements of selected manufacturing companies sampled in Nigeria. This sensitive observation readily indicates that the attainment of material misstatement free financial reporting atmosphere in any corporate environment goes beyond entities sound compliance to prevailing regulatory disclosure guidelines. It was recommended that timely appropriate steps be taken for the establishment of solid digital infrastructures in developing countries like Nigeria towards JOURNAL OF ACADEMIC RESEARCH IN ECONOMICS VOLUME 13 NUMBER 3 NOVEMBER 2021 442 ensuring that the promotion of transparency and faithful representation of disclosed financial information are not grossly undermined in the near future.
... In fact, most financial statement fraud is implemented with the awareness or consent of management [30]. Ill-intended managers seek to manipulate earnings by committing financial statement fraud [31]. ...
Information asymmetry is everywhere in financial status, financial information, and financial reports due to agency problems and thus may seriously jeopardize the sustainability of corporate operations and the proper functioning of capital markets. In this era of big data and artificial intelligence, deep learning is being applied to many different domains. This study examines both the financial data and non-financial data of TWSE/TEPx listed companies in 2001–2019 by sampling a total of 153 companies, consisting of 51 companies reporting financial statement fraud and 102 companies not reporting financial statement fraud. Two powerful deep learning algorithms (i.e., recurrent neural network (RNN) and long short-term memory (LSTM)) are used to construct financial statement fraud detection models. The empirical results suggest that the LSTM model outperforms the RNN model in all performance indicators. The LSTM model exhibits accuracy as high as 94.88%, the most frequently used performance indicator.
... First of all, the authors focused on fraudulent financial behaviour. Papik and Papikova (2020; tested the well-known Beneish (2001) model, on the basis of which they developed two new models based on discriminant analysis and logit regression with a predictive power of more than 70% and 60%, respectively. Svabova et al. (2020) proceeded similarly, however, their model achieved up to 84% classification accuracy. ...
Research background: Deteriorating economic conditions and a negative outlook increase the pressure on financial management and the need to show high financial performance. According to Positive Accounting Theory, the growing risk of bankruptcy is associated with the phenomenon of earnings management. Bankruptcy risk and the quality of reported profits, along with other aspects of financial performance, vary throughout the company's life cycle. Nevertheless, these factors or their interactions are investigated only to a very small extent. Purpose of the article: The aim of this study is to clarify the impact of corporate life cycle and bankruptcy on earnings management, in order to describe behaviour of companies at different stages of corporate life cycle. Methods: A hierarchical mixed model with a random time and industry effect was chosen as appropriate because it allows the investigation of multilevel data that is not independent. The sample covers the financial indicators of more than 33,000 Central European companies from 2015?2019. The non-sequential Dickinson model, company age, and three models of accrual earnings management were used as proxies for the company's life cycle and quality of reported profit. Findings & value added: Earnings management and bankruptcy risk have a U-shape, indicating that financially distressed firms reduce reported accounting profit at the Introduction, Decline and, to a lesser extent, at the Growth stage. Slovak and Czech companies manipulate profits to a similar extent, Hungarian companies increase accounting profit to a greatest extent than the surveyed countries by controlling bankruptcy ? life cycle effect; however, the variability of accounting manipulations across industries has not been demonstrated. These findings imply that start-ups and declining businesses provide crooked financial statements to obtain more favourable debt covenants, and estimating discretionary accruals using life-cycle subsamples can improve the predictive power of accrual earnings management models.
