Financial distress pre-warning indicators: a case study on Italian listed companies

Luca, Francesco De; Meschieri, Enrica

doi:10.21314/jcr.2017.220

Cited by 7 publications

(20 citation statements)

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“…Sun and Lei proposed to develop a logistic regression and multiple discriminant analysis-based financial risk early-warning model using the company's financial data as variable indicators. According to empirical studies, the model is very capable of differentiating the company's financial status [ 14 ]. On the basis of earlier research, De proposed that the risk early warning model be upgraded and that 28 index data from two groups of companies be chosen.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Li¹,

Yan²,

Lu³

et al. 2022

Journal of Environmental and Public Health

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Currently, the level of economic globalisation is expanding, which gives organizations more room to grow while also subjecting them to an increasing amount of pressure from the market. Companies are forced to deal with an increasing number of unclear aspects due to the unstable internal and external environments, which also increases the risks they confront. A management system for corporate financial risk is according to studies on early warning systems for financial risks. Its goals are to raise the standard of corporate financial management and boost economic advantages, identify concerns and potential hazards in the corporate financial management process, stop corporate financial crises in their tracks, and lessen the losses brought on by such crises. The financial risk management of the organization is predicted and examined in this research using the logistic regression model. The use of a logistic regression model allows for the simultaneous analysis of various risk factors, such as discrete and continuous variables, as well as the analysis of external variables’ interactions and confounding. This method is suited for widespread usage in practice because it has shown exceptional outcomes in study that are 16.24% better than those of the conventional method.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Li¹,

Yan²,

Lu³

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…James believed that shared services can integrate all the resources of the company, reduce operating costs, and provide high-quality and professional services for different regions and internal partners, so as to realize enterprise value [ 11 ]. Luca and Meschieri believed that in the case of organizational failure, enterprises can gather many basic businesses in a semimarket organizational entity [ 12 ]. Tinoco et al explained the mode of financial sharing service mainly from three aspects: entry management, outflow management, and comprehensive control [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model

Zeng

2022

Computational Intelligence and Neuroscience

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Traditional financial accounting has gradually evolved into management accounting in order to adapt to changing times and developments. To avoid being obliterated by the times, accountants must gradually improve their professional and comprehensive abilities in order to create greater value for businesses in the AI (Artificial Intelligence) era. This article presents an AI-based financial management optimization design and proposes an AI-based accounts receivable management optimization framework based on the existing information system. A typical financial distress early-warning model is built using the BPNN (BP Neural Network) model, and the training samples of listed companies’ financial data are processed iteratively using the neural network algorithm to realize the visual modeling of the object-oriented neural network and learn the training samples. Finally, the network’s ability to provide early warning is put to the test. The results show that BPNN’s prediction accuracy is significantly higher than that of other types, especially after years of data, with prediction results exceeding 90%. The results show that the BPNN-based financial early-warning method is feasible.

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“…Climent et al [23] identified 25 annual financial ratio series for commercial banks in the Eurozone that may help anticipate banks' financial distress. De Luca and Meschieri [24] focused on accounting ratios to predict the financial distress status of a company based on linear discriminant analysis. Jiang and Jones [25] used 90 predictor variables, including financial ratios, market returns, macro-economic indicators, valuation multiples, audit quality factors, shareholder ownership/control, executive compensation variables, corporate social responsibility metrics, and others, to predict corporate distress with TreeNet ® .…”

Section: Fraud Detectionmentioning

confidence: 99%

Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

Sawangarreerak

Thanathamathee

2021

Journal of Open Innovation: Technology, Market, and Complexity

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Identifying fraudulent financial statements is important in open innovation to help users analyze financial statements and make investment decisions. It also helps users be aware of the occurrence of fraud in financial statements by considering the associated pattern. This study aimed to find associated fraud patterns in financial ratios from financial statements on the Stock Exchange of Thailand using discretization of the financial ratios and frequent pattern growth (FP-Growth) association rule mining to find associated patterns. We found nine associated patterns in financial ratios related to fraudulent financial statements. This study is different from others that have analyzed the occurrence of fraud by using mathematics for each financial item. Moreover, this study discovered six financial items related to fraud: (1) gross profit, (2) primary business income, (3) ratio of primary business income to total assets, (4) ratio of capitals and reserves to total debt, (5) ratio of long-term debt to total capital and reserves, and (6) ratio of accounts receivable to primary business income. The three other financial items that were different from other studies to be focused on were (1) ratio of gross profit to primary business profit, (2) ratio of long-term debt to total assets, and (3) total assets.

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Financial distress pre-warning indicators: a case study on Italian listed companies

Cited by 7 publications

References 0 publications

[Retracted] Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

[Retracted] Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model

Detecting and Analyzing Fraudulent Patterns of Financial Statement for Open Innovation Using Discretization and Association Rule Mining

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