Financial Distress Prediction Using Distress Score as a Predictor

Sheikhi, Maryam; Shams, Mirfeiz Fallah; Sheikhi, Zeinab

doi:10.5539/ijbm.v7n1p169

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Financial distress also occurs due to the low ability of companies to generate profits from its operations [3]. This is caused by external deterioration or the failure of internal control, thereby leading to bankruptcy and loss on large and small scale industries [4,5]. It has significant consequences capable of reducing a company's economy, hence, investors and creditors tend to lose their monies [6].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Leverage, Cash Flow, Tax, R & D, Economic Growth and Inflation on the Financial Distress in the Sub-Sector of Property and Real Estate Companies

Curry¹

2020

Proceedings of the International Conference on Management, Accounting, and Economy (ICMAE 2020)

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This study aims to estimate and analyse the influence of leverage, cash flow, tax, R&D, economic growth, and inflation on financial distress in the property and real estate companies' sub-sector. The first four attributes form the internal factors of the company, while the remaining is external with the research data obtained from a 2014 to 2018 using the panel data regression as a method. Only a few study uses company internal and external factors simultaneously. The results showed that leverage and cash flow are internal factors with adverse influences on the occurrence of financial distress. Furthermore, economic growth is an external factor that influences the financial distress in the sub-sector of property and real estate companies listed on the IDX. In conclusion, Tax, R&D, and inflation do not influence financial distress.

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Section: Introductionmentioning

confidence: 99%

The Influence of Leverage, Cash Flow, Tax, R & D, Economic Growth and Inflation on the Financial Distress in the Sub-Sector of Property and Real Estate Companies

Curry¹

2020

Proceedings of the International Conference on Management, Accounting, and Economy (ICMAE 2020)

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“…The first were made by Beaver (1966) and Altmam (1968) and focused on accounting issues, whereas later studies moved on to the analyzing and forecasting of financial crises using new methodologies, as for example, artificial intelligence and neural networks (Fanning & Cogger, 1994;Etheridge & Sriram, 1997;Brockett et al, 2006;Wu, Liang, & Yang, 2008). For a more extensive review of the literature see Ward (1994), Ward and Foster (1997), Hillegeist et al (2004), Campbell, Hilscher & Szilagyi (2010, Sheikhi et al (2012).…”

Section: Data and Sample Selectionmentioning

confidence: 99%

Preference Order of Responses to Economic Distress in Italian Listed Firms

Santosuosso¹

2013

IBR

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This paper examines a sample of 72 firms listed on the Italian Stock Exchange between 2007 and 2011 which were in serious economic difficulty. These companies adopted a series of measures to find a way out of the crisis, including management changes, divestment of assets, debt restructuring and the issuing of new shares. The paper aims to verify the existence of a preference order for responses to the economic crisis. Unlike previous research, a methodology has been adopted which analyses the content of the Management Commentary in order to identify both the proposals presented by managers and the measures actually adopted. The result of the analysis shows that the different types of measures to solve the crisis are often proposed and/or realized in a combined manner. Although the existence of a pecking order is not immediately obvious, a preference for management changes seems to emerge, whereas debt renegotiation and the issuing of new shares appear to be used only as secondary responses. Furthermore, based on the results of the logistic regression model, this paper suggests that the reasons for choosing the types of restructuring measures appears to be mainly related to their profitability. Managers of low profitability firms intervene most frequently on the capital structure rather than adopting general management changes and divestment plans. This result is consistent with the Pecking order theory which deals with the hierarchy of funding sources followed by firms.

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Data Mining for Municipal Financial Distress Prediction

Alaminos

Fernández

García

et al. 2018

Advances in Data Mining. Applications and Theoretical Aspects

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Data mining techniques are capable of extracting valuable knowledge from large and variable databases. This work proposes a data mining method for municipal financial distress prediction. Using a new proxy of municipal financial situation and a sample of 128 Spanish municipalities, the empirical experiment obtained satisfactory results, which testifies to the viability and validity of the data mining method proposed for municipal financial distress prediction.

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Financial Distress Prediction Using Distress Score as a Predictor

Cited by 5 publications

References 26 publications

The Influence of Leverage, Cash Flow, Tax, R & D, Economic Growth and Inflation on the Financial Distress in the Sub-Sector of Property and Real Estate Companies

The Influence of Leverage, Cash Flow, Tax, R & D, Economic Growth and Inflation on the Financial Distress in the Sub-Sector of Property and Real Estate Companies

Preference Order of Responses to Economic Distress in Italian Listed Firms

Data Mining for Municipal Financial Distress Prediction

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