Predicting Bankruptcy After The Sarbanes-Oxley Act Using Logit Analysis

Our study proposes firm bankruptcy prediction using logit analysis after the passage of the Sarbanes-Oxley (SOX) Act using 2008-2009 U.S. data. The results of our logit analysis show an 80% (90% with one year before bankruptcy data) prediction accuracy rate using financial and other data from the 10-K report in the post-SOX period. This prediction rate is comparable to other data mining tools. Overall, our results show that, as compared to the prediction rates documented by other bankruptcy studies before SOX, firm bankruptcy prediction rates have improved since the passage of SOX. Our findings shed light on the benefits of SOX by providing evidence that legislation makes the financial reporting more informative. This study is important for regulators to implement public policy. Investors may be interested in our findings to better assess company risk when making portfolio decisions

Monitoring credit risk in the social economy sector by means of a binary goal programming model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. Service Business. 7(3):483-495. doi:10.1007/s11628-012-0173-7S48349573Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54Andenmatten A (1995) Evaluation du risque de défaillance des emetteurs d’obligations: Une approche par l’aide multicritère á la décision. Presses Polytechniques et Univertitaires Romandes, LausanneBeaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111Boritz JE, Kennedey DB (1995) Effectiveness of neural network types for prediction of business failure. 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Comput Oper Res 38:409–419Luoma M, Laitinen EK (1991) Survival analysis as a tool for firm failure prediction. Omega-Int J Manage S 19:673–678March I, Yagüe RM (2009) Desempeño en empresas de economía social. Un modelo para su medición. CIRIEC 64:105–131Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1:249–276Mateos A, Marín M, Marí S, Seguí E (2011) Los modelos de predicción del fracaso empresarial y su aplicabilidad en cooperativas agrarias. CIRIEC 70:179–208McKee T (2000) Developing a bankruptcy prediction model via rough sets theory. Int J Intell Syst Account Finan Manage 9:159–173Messier WF, Hansen JV (1988) Inducing rules for expert system development: an example using default and bankruptcy data. Manage Sci 34:1403–1415Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131Peel MJ (1987) Timeliness of private firm reports predicting corporate failure. 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Probability of deafault using the logit model: The impact of explanatory variable and data base selection

2nd INTERNATIONAL SCIENTIFIC CONFERENCE WHITHER OUR ECONOMIES – 2012 Conference ProceedingsThe Spanish economy is suffering a severe financial crisis which is affecting all Spanish savings banks as well as some major banks. One of the triggers of the crisis is the high companies’ default rate experienced in the last years due to a deficient credit risk management by financial institutions. Credit risk analysis is mainly undertaken using the logit model to calculate the probability of default of the companies. In this work we describe some problems that arise when using this model and that can have a negative impact on the quality of the results obtained.Bartual Sanfeliu, C.; García García, F.; Guijarro, F.; Romero Civera, A. (2012). Probability of deafault using the logit model: The impact of explanatory variable and data base selection. International Scientific Conference: Whither our Economics. 118-124. http://hdl.handle.net/10251/61415S11812

Factores determinantes de las quiebras en microempresas

La pequeña y mediana empresa es uno de los principales motores de las economías europeas. De entre ellas, un alto porcentaje son microempresas las cuales generan la mayor parte del empleo. En la actualidad este segmento empresarial está sufriendo en mayor medida la situación de crisis financiera, con la consecuente elevación de la tasa de destrucción de las mismas. En este contexto, desarrollar modelos de quiebra específicos para este tamaño empresarial e identificar las variables con mayor poder explicativo constituye un reto. Aquí se aborda la cuestión llegando a ser un trabajo pionero en este campo, en tanto la metodología utilizada como en el sector al que se aplica, caracterizado por una elevada opacidad informativa. Partiendo de variables financieras y no financieras que han sido utilizadas con relativo éxito en el pronóstico de quiebra empresarial en general, tratamos de determinar cuáles de ellas están afectando en mayor medida a la microempresa. Para ello utilizamos una técnica no paramétrica de aprendizaje basada en los rough set, que aplicamos a una muestra de empresas del Reino Unido, con iguales porcentajes respecto a su situación de fallida y a su carácter familiar, por ser esta última característica un factor condicionante de los resultados.The small and medium enterprises are one of the main drivers of European economies. A large percentage consists of microenterprises that generate the most part of employment. Today this business segment is suffering the financial crisis, with the consequent increase in the rate of destruction of the same. So develop specific models for these bankruptcy and identify variables with greater explanatory power is challenging. So this study is becoming a pioneering work in this field in both the methodology used and the sector to which it applies, which has a higher opacity. Based on financial and non-financial variables that have been used with relative success in predicting bankruptcy in general, we try to determine which ones are affecting more to microfirms. We use a nonparametric learning technique based on the rough sets, which apply to a sample of UK firms, balanced on its failed situation and its familiar character, which determines the results

Prediction of Banks Financial Distress

In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies

The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors

Credit Risk Analysis: Reflections on the Use of the Logit Model

Creative Commons Attribution License (CCAL). Attribution 2.5 Generic (CC BY 2.5)The present economic and financial crisis has underlined the importance to financial institutions and investors of having access to efficient methods of quantifying credit risk, or the probability of default. The logit models are among the techniques commonly used by large organizations and rating agencies for predicting insolvency. However, it should be borne in mind that some problems arise when using these models, such as the selection of the explanatory variables or the composition of the sample from which the model is obtained. These aspects have a decisive influence on the prediction models used to quantify companies’ credit risk. The present study describes the problems that arise with logit on a sample of Spanish companies and shows that the estimated prediction models are indeed modified by changes in the sample on which they are based.Bartual Sanfeliu, C.; García García, F.; Giménez Molina, V.; Romero Civera, A. (2012). Credit Risk Analysis: Reflections on the Use of the Logit Model. Journal of Applied Finance & Banking. 2(6):1-13. http://hdl.handle.net/10251/60119S1132

The problem of bankruptcy in listed companies

Purpose: The paper presents an investigation of the bankruptcy of companies listed on the Warsaw Stock Exchange using the Fundamental Power Index in dynamic terms (FPI). Design/Methodology/Approach: The methodology of the Fundamental Power Index (FPI) was used to assess bankruptcy. In general, the essence of the indicator is a synthetic assessment of the company's fundamental strength. The indicator can take high and low values. The appearance of low levels of the ratio for the company is not favourable and indicates a problem in the financial standing area. As a consequence, the level of the ratio may signal a risk of bankruptcy. The article also discusses the legal grounds for bankruptcy of companies in Poland and selected EU countries. Findings: The results of the conducted research indicate that FPI may be a useful tool of early warning against bankruptcy. The dynamic approach to the index allowed for the assessment of the fundamental strength of the companies in the period of five years. At the same time, the level of the index indicated the risk of bankruptcy. The basis for the construction of the ratio was the financial data from the financial statements of the examined entities. In particular, information on financial ratios from the following groups was used: liquidity, profitability, debt and operational efficiency. Practical Implications: The implementation of the indicator concerns many areas, including investing, assessment of companies or the stock market. In the event of bankruptcy, information about the level of the ratio may support the management process of the company and early response of managers to avoid bankruptcy (e.g. by introducing recovery or restructuring programs). For the investor, the information about the low level of the ratio is a signal for actions related to risk management. Originality/Value: The results of the study reflect the applicability and effectiveness of the proposed indicator. Consequently, the fundamental strength index may constitute an alternative to the existing methods of assessing the bankruptcy process in enterprises.peer-reviewe

A revision of Altman’s Z- Score for SMEs: suggestions from the Italian Bankruptcy Law and pandemic perspectives

As the pandemic urged further investigations on the prediction of firms’ financial distress, this study develops and tests an alternative measure to the alert system elaborated by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant model with the background employed to develop the NCCAAE’ predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the NCCAAE’s alert system to that of the traditional Z-score over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align the traditional Z-score to the current socio-economic conditions and provide an alternative measure to the NCCAAE’s alert system which embeds a Z-score calculated using the ratios elaborated by the NCCAAE for the alert system. The analysis of the two baseline approaches showed complementary results as the Z-score overperformed the alert system when predicting the status of non-viable firms whereas the opposite emerged as regards viable firms. The revised version of both approaches pointed out an enhanced predictive accuracy with respect to baseline models. In particular, the complementary role of the Z-score has been integrated into the new alert system as major contribute to its enhancement which pointed it out as the best measure employed. We, therefore, contribute to the literature studying the financial distress prediction developments by elaborating an alternative measure to the alert system developed by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant function with the background employed to develop the NCCAAE’ predictors. Our analysis enriches the post-pandemic debate on refined financial distressed prediction methods by pointing out the limits of the alert system as designed by the NCCAAE and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress