Usefulness of Z scoring models in the early detection of financial problems in bankrupt Spanish companies

Fitó, M. Àngels; Plana-Erta, Dolors; Llobet, Joan

doi:10.3926/ic.1108

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…This was corroborated byPanigrahi (2019) in his study on Indian pharmaceutical companies where he acknowledged a possible restriction of his study due to geographical limitation Prasetiyani and Sofyan (2020). concluded that calibration of the model is needed for country specific conditions on Indonesian firms Fito et al (2018). mentioned that the predictive power of the model increases when its formulation is adapted to the context of the analysis.In this respect,Altman et al conducted in 2017 a study to test the Z''-Score model in an international environment including some developing countries.…”

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confidence: 80%

Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies

Aguilar

Ramírez

Torres

et al. 2021

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Introduction: in 1968, Altman developed a multivariable predictive Z-score model to assess the probability of a public manufacturing company going to bankruptcy based on financial ratios. Later, Altman (1983) re-stated a more improved Z’’-Score model designed to apply in public or private, manufacturing, or non-manufacturing firms, but also in emerging countries. Prediction of the updated model proved to be highly efficient. This research was conducted to prove the level of accuracy of the Z’’-Score model applied to firms listed in the Mexican Stock Exchange (MSE) since there is little relevant research on this subject. Method: this research was conducted under a quantitative approach as a census and its scope was situational with a non-experimental and longitudinal research design. The period covered by this research was 2012-2019 since the data was available for those years under a somehow stable economic situation without significant economic ups and downs. This research considered the integration of a large financial database and the design of a typology to classify and analyze 155 firms based on a standard deviation and average results of 837 Z’’-scores. A second analysis was conducted to prove if the predicted situation (area) by the Z’’-Score corresponded to the real situation in the marketplace for every company. Results: the results showed that the accuracy level of the Altman model decreased when applied to Mexican firms. The error of the model applied to Mexican companies related to those classified in the bankruptcy prediction area was 75 % of misclassification cases. The total error of the model included all areas, or cases, was 18 % of misclassification cases. This model is supposed to be effective within a time frame of two years before a possible bankruptcy. Even considering a longer time frame, the companies located in the bankruptcy prediction area continued having misclassifications representing 57 % of error. The error for the model considering all cases and all areas, was 14 % of misclassification cases. This represented a high level of inefficiency of the model applied to an emerging country companies, such as Mexico. Discussion or conclusion: the model is certainly effective while predicting companies in the areas of non-bankrupt sector and grey, but it was inefficient when predicting the possibility of bankruptcy. It was also demonstrated that the time frame of two years is no longer effective when applying the model to Mexican companies. As a result, more research cases are needed to update the model to perform efficiently in emerging countries including country-specific conditions and considering a different time frame to predict bankruptcy.

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confidence: 80%

Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies

Aguilar

Ramírez

Torres

et al. 2021

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“…As demonstrated by the results of various studies, the Altman model represents a fairly good way for companies to identify the performance of their business in a relatively simple and quick way, and also to predict the potential risk of that business in the future. Due to the fact that insolvency is a problem that concerns different social actors, such as shareholders, suppliers, financial institutions and workers, it is important to know the predictive capacity of the Altman model in order to make adequate decisions (Fito et al, 2018). This model can provide any owner, investor or lender with an indicative assessment of a company's financial stability.…”

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confidence: 99%

Application of the Altman Model for the Prediction of Financial Distress in the Case of Slovenian Companies

Dolinšek,

Kovač

2024

Organizacija

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Background/Purpose The aim of this paper is to verify the applicability and accuracy of the Altman model in the case of Slovenian companies. The use of the Altman model is hugely popular and widespread among financiers, analysts and other stakeholders who want to determine the creditworthiness of a company’s operations and the likelihood of it running into financial difficulties in the coming years. Methods The study was conducted on a sample of 66 Slovenian companies, which were divided into two equal groups: bankruptcy and non-bankruptcy companies. Based on accounting data for the last five years, the authors of this paper calculated the Z-Score, which is based on the Multiple Discriminant Analysis (MDA). By calculating the statistical error of the estimate (type I and II), the authors verified the extent (in percentage terms) to which the companies had been correctly classified by the model. The Mann-Whitney U test was used to check whether there was a difference in the average Z-Score between the two groups of companies. Results The authors determined that the reliability of the Altman model was 71.21% when tested at the upper bound (the threshold value of the Z-Score was 2.6) and 80.30% when tested at the lower bound (the threshold value of the Z-Score was 1.1). This is similar to other countries, where the reliability was found to be over 70% in most cases. Despite the lower reliability of the model, the Z-Score proved to be an important factor in differentiating between the two groups of companies, as bankruptcy companies had a lower value of this indicator than non-bankruptcy companies. Conclusion Based on the results of this study, as well as those of other studies, it can be summarized that the Altman model is a fairly good way for companies to determine the success of their business in a relatively simple and quick way and also to predict the potential risk of their operations in the future. However, since the reliability of the model is not 100%, it is important to be careful when making business predictions and carry out additional in-depth analyses or use other methods.

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“…En este sentido, el Análisis de Discriminante Múltiple mediante la aplicación del modelo de Z de Altman permite realizar una comparativa entre varios grupos, se determina dos tipos de empresas: insolventes y solventes a través de una ecuación lineal 𝑍 = 1,2𝑋 1 + 1,4𝑋 2 + 3,3𝑋 3 + 0,6𝑋 4 + 0.999 𝑋 5 (Fitó, Angels, Plana, y Llobe, 2018).…”

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El Pronóstico de insolvencia financiera mediante el análisis discriminante múltiple para el sector automotriz de Tungurahua

Urco

Carrillo

2022

revista

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El objetivo principal de la presente investigación fue evaluar el desempeño financiero que permitió proyectar la insolvencia, financiera mediante el análisis de discriminante múltiple en el sector automotriz de Tungurahua, los resultados fueron distribuidos en: área saludable, área gris (incerteza), área de quiebra. Se destacan 3 objetivos planteados: 1. Determinar los principales componentes del análisis de discriminante múltiple, 2. Diagnosticar la situación financiera del sector automotriz y 3. Adaptar las variables del modelo de riesgo de insolvencia. Se ha desarrollado una metodología basada en la combinación de investigaciones de tipo descriptiva y predictiva con enfoque cuantitativo a través del uso de indicadores. La población fue conformada por 29 empresas del sector, la muestra seleccionada fue fueron las empresas reguladas por la Superintendencia de Compañías, Valores y Seguros del Ecuador (SUPERCIAS) que en total son 5. Para el análisis cuantitativo, se utilizó 5 indicadores obtenidos de los balances financieros disponibles con acceso libre y gratuito en la página web de la SUPERCIAS con el fin de construir la ecuación . El pronóstico se realizó mediante regresión polinomial de segundo orden, los principales resultados obtenidos determinaron que tan solo 1 empresa se encuentra en zona saludable, pese a la crisis económica provocada por la pandemia del COVID-19. La principal conclusión fue que las empresas del sector automotriz de Tungurahua precisan de la utilización de herramientas financieras prácticas, a través del modelo Z de Altman,profundizando en el uso de las razones financieras dentro de la gestión administrativa, para ello, se debe orientar el análisis y toma de decisiones continuas en base a estas razones financieras reales y objetivos, con el fin de mitigar o reducir la exposición a los riesgos y retos del entorno interno y externo que enfrenta diariamente la industria en la actualidad.

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Usefulness of Z scoring models in the early detection of financial problems in bankrupt Spanish companies

Cited by 10 publications

References 12 publications

Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies

Testing Altman’s Z’’-Score to assess the level of accuracy of the model in Mexican companies

Application of the Altman Model for the Prediction of Financial Distress in the Case of Slovenian Companies

El Pronóstico de insolvencia financiera mediante el análisis discriminante múltiple para el sector automotriz de Tungurahua

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