Developing, Validating and Using Internal Ratings

Laurentis, Giacomo De; Maino, Renato; Molteni, Luca

doi:10.1002/9780470971901

Cited by 16 publications

(9 citation statements)

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“…Multiple verifications can eliminate the randomness discussed previously. It is essential to realize that models predicting financial distress are designed for a quick evaluation of the corporate financial situation, they work on empirical bases, and they never function as natural law (De Laurentis et al 2010). It should also be respected that economics belongs to the social sciences, although many processes can be quantified, and the behavior of economic entities can be described systematically.…”

Section: Discussionmentioning

confidence: 99%

“…These models provide a controlled description of a particular economic reality. It should not be neglected that these models are never 100% accurate as they work on probability roots based on empirical observations (De Laurentis et al 2010). The most popular statistical techniques applied are multivariate discriminant analysis and logistic regression (Balcaen and Ooghe 2006;Ohlson 1980).…”

Section: Introductionmentioning

confidence: 99%

“…De Laurentis et al (2010) point out that prediction models are part of a broader framework: their limits have to be perfectly understood, and their general application should be avoided. The current modelling approaches make it difficult to fulfill the conditions mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Prediction Models Applied in Economic Recession and Expansion

Čámská

Klečka

2020

JRFM

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As a rule, the economy regularly undergoes various phases, from a recession up to expansion. This paper is focused on models predicting corporate financial distress. Its aim is to analyze impact of individual phases of the economic cycle on final scores of the prediction models. The prediction models may be used for quick, inexpensive evaluation of a corporate financial situation leading to business risk mitigation. The research conducted is drawn from accounting data extracted from the prepaid corporate database, Albertina. The carried-out analysis also highlights and examines industry specifics; therefore, three industry branches are under examination. Enterprises falling under Manufacture of metal products, Machinery, and Construction are categorized into insolvent and healthy entities. In this study, 18 models are selected and then applied to the business data describing recession and expansion. The final scores achieved are summarized by the main descriptive statistics, such as mean, median, and trimmed mean, followed by the absolute difference comparing expansion and recession. The results confirm the expectations, assuming that final scores with higher values describe better corporate financial standing during the expansion phase. Similar results are achieved for both healthy and insolvent enterprises. The paper highlights exceptions and offers possible interpretations. As a conclusion, it is recommended that users need to respect the current phase of the economic cycle when interpreting particular results of the prediction models.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Prediction Models Applied in Economic Recession and Expansion

Čámská

Klečka

2020

JRFM

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“…In situations where the conditions for the company are fiscally poor, the use of default indicators, such as bankruptcy models, are used and they combine several financial indicators focused on different financial health issues. The default models were constructed as a result of empirical observations and their functioning is based on probabilities (De Laurentis et al, 2010). The oldest complex model was published first in 1968 and called the Altman Z-Score (Altman, 1968).…”

Section: Financial Characteristicsmentioning

confidence: 99%

Financial Situation of Pre-Packed Insolvencies

Schönfeld

2020

Journal of Business Economics and Management

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This paper is focused on the financial situation of companies entering insolvency proceedings. It does not work with all kinds of the insolvent companies, but this paper concentrates on one specific issue. The issue is pre-pack insolvencies. The main aim is to show if the financial situation is an important factor for consent to pre-pack. The pre-pack insolvencies are insolvency cases which start with an insolvency proposal which is accompanied by a reorganization plan already approved by creditors. Prepacks should help make the insolvency process quicker and enable enterprise financial rehabilitation and sustain the going concern principle. On the other hand, the procedure can hardly be successful when the financial situation of the company is extremely poor. Therefore this paper evaluates the financial situation of the companies with pre-packed insolvency in the Czech Republic. The analysis of companies was conducted over one, two or three year periods prior to the companies entering an insolvency proceeding. According to the literature, financial indicators used for evaluation are commonly EBITDA, cash liquidity, debt ratio, ROA and the Altman Z-Score prediction model. Results for the individual enterprises are summed up in this paper using basic descriptive and variable statistics. Conclusions have especially practical implications because they show financial inability of majority pre-packed cases.

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“…The smaller the value of Wilks' lambda for a variable, the more it contributes to DF. Wilks' lambda follows the equation [20,21]:…”

Section: A Pre-tests Before Building Dfmentioning

confidence: 99%

Statistical discriminant analysis of high voltage feeders in Western Australia distribution networks

Wolfs

2011

2011 IEEE Power and Energy Society General Meeting

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The distribution network impact assessment of changes in load behaviors or new technology deployments requires a rigorous understanding of the features of the network itself. In a typical distribution network, there will be hundreds of high voltage (HV) feeders and ten thousands of low voltage (LV) feeders. This paper presents a method to combine cluster and discriminant analysis techniques to identify a small number of statistically representative or prototypical feeders that capture the key features of a distribution network. The proposed method is readily transferable to other systems. As an illustration the paper presents a representative HV feeder set for an existing network and a feeder classifier based upon quadratic discriminant functions. Representative feeder data will be typically used as an input to Monte Carlo models to assess the impact of technology changes driven by Smart Grid deployments or load changes due to distributed renewables, air conditioning or electric vehicle uptake.

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Developing, Validating and Using Internal Ratings

Cited by 16 publications

References 0 publications

Comparison of Prediction Models Applied in Economic Recession and Expansion

Comparison of Prediction Models Applied in Economic Recession and Expansion

Financial Situation of Pre-Packed Insolvencies

Statistical discriminant analysis of high voltage feeders in Western Australia distribution networks

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