Disclosure of Intangible Assets: an Empirical Study of Financial Corporations in the Iberian Peninsula

The purpose of this paper is to analyse, firstly, to what extent intangible assets in the consolidated accounts of seven Portuguese banks and seven Spanish banks between 2006 and 2009 are disclosed and, secondly, to analyse what the most influential factors are in the above mentioned disclosure. In order to do this, before reviewing the existing literature and on the basis of other studies on this topic, a disclosure index has been created based on the requirements related to the intangible assets as stated in IAS 38. Then, two statistical analyses have been made: a univariate one for each of the explanatory variables and a multivariate one, in which all variables have been analysed. Both analyses led to the conclusion that the disclosure index of intangible assets is 0.96, where the bank dimension and the internationalization degree are the variables that are considered explanatory of the variation of the disclosure index in the regression analysis.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Finantial risk analysis of portuguese textile and tourism companies

Textile and tourism sectors are two important industries in the Portuguese economy. However, its high exposure to both internal and international economic volatility make the companies operating in these economic sectors particularly vulnerable to economic crises, such as the ones which have been impacting Portugal and the European Union. The objective of this paper is to evaluate and understand the impact of size and age on the financial health of textile and tourism companies, measured by economic indices. An empirical based model is proposed. Its implications are derived and tested on a sample of 4061 Portuguese companies from textile and tourism sectors, during the period 2005-2009. The findings suggest that age has a major impact on the risk of failure, rather than size. Whereas the effect of age is generally positive regarding the financial health of the company, the effect of size is less clear and ultimately depends on the age of the company.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

Analysis of the return on preventive measures in musculoskeletal disorders through the benefitecost ratio: A case study in a hospital

Work-related musculoskeletal disorders (WMSDs) are among the most costly health problems that society is facing today. Prevention involves investments and it is important for organizations to make a cost ebenefit analysis of ergonomic projects. Return on prevention is a recent concern in the domain of occupational safety and health (OSH). There are many studies concerning the return on the prevention of WMSDs, in terms of the benefits for the organization in which the preventive measures are implemented. However, it is also important to perform an analysis of the impact of each measure on society (externalities). A model to perform a financial and economic costebenefit analysis related to OSH projects was developed and it was applied in the case of the prevention of WMSDs in a Portuguese hospital. An analysis of the accidents and corresponding costs has been made in six of the services of the hospital. Financial and an economic costebenefit analysis have been made and the benefitecost ratio (B/C) has been calculated. While the B/C financial ratio, considering only the benefits to the hospital, is around 2, the economic B/C ratio, taking into account all the external benefits that have been quantified, is higher than 14. Relevance to industry: Both the economic and the financial B/C ratio are important support tools for decision makers in public and private organizations, helping them to define which preventive measures should be implemented, taking into account the costs involved and the resulting quantified benefits, for the organization, for the workers and for the society.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

ANTECEDENTS AND CONSEQUENCES OF HOUSEHOLD FINANCIAL MANAGEMENT IN BRAZILIAN LOWER-MIDDLE-CLASS

Through two complementary and exploratory studies – one qualitative and one quantitative – this research aims to understand the ways in which lower-middle-class families in Brazil manage their household finances. The study proposes an integrated framework that brings together various previously disconnected theoretical fragments. Based on a survey with a sample of 165 lower-middle-class female consumers of a retail company in São Paulo, we explored and tested, via a quantitative study, how antecedents such as personal characteristics affect the financial management process, as well as its consequences, either negatively as defaults or positively as savings. The model calibration and analysis were derived from a series of regression analyses. The results revealed the mediator role that financial management plays in the relationship between personal characteristics and defaults and savings. Compared to previous studies with consumers of more affluent countries, we identified peculiar findings among Brazilian lower-middle-class consumers: inadequate attention to control, weak or no focus on short- or long-range planning, widespread absence of budget surplus, and influence of critical events on episodes of default.

The risk-return trade-off in Europe: A temporal and cross-sectional analysis

This paper analyzes the risk-return trade-off in European equities considering both temporal and cross-sectional dimensions. In our analysis, we introduce not only the market portfolio but also 15 industry portfolios comprising the entire market. Several bivariate GARCH models are estimated to obtain the covariance matrix between excess market returns and the industrial portfolios and the existence of a risk-return trade-off is analyzed through a cross-sectional approach using the information in all portfolios. It is obtained evidence for a positive and significant risk-return trade-off in the European market. This conclusion is robust for different GARCH specifications and is even more evident after controlling for the main financial crisis during the sample period.

Interest rate exposure of Spanish banks: A nonparametric analysis

Interest rate risk is one of the major financial risks faced by banks due to the very nature of the banking business. The most common approach in the literature has been to estimate the impact of interest rate risk on banks using a simple linear regression model. However, the relationship between interest rate changes and bank stock returns does not need to be exclusively linear. This article provides a comprehensive analysis of the interest rate exposure of the Spanish banking industry employing both parametric and non parametric estimation methods. Its main contribution is to use, for the first time in the context of banks’ interest rate risk, a nonparametric regression technique that avoids the assumption of a specific functional form. One the one hand, it is found that the Spanish banking sector exhibits a remarkable degree of interest rate exposure, although the impact of interest rate changes on bank stock returns has significantly declined following the introduction of the euro. Further, a pattern of positive exposure emerges during the post-euro period. On the other hand, the results corresponding to the nonparametric model support the expansion of the conventional linear model in an attempt to gain a greater insight into the actual degree of exposure.

A model to forecast financial failure, in non financial galician SMES

We are concerned with providing more empirical evidence on forecast failure, developing forecast models, and examining the impact of events such as audit reports. A joint consideration of classic financial ratios and relevant external indicators leads us to build a basic prediction model focused in non-financial Galician SMEs. Explanatory variables are relevant financial indicators from the viewpoint of the financial logic and financial failure theory. The paper explores three mathematical models: discriminant analysis, Logit, and linear multivariate regression. We conclude that, even though they both offer high explanatory and predictive abilities, Logit and MDA models should be used and interpreted jointly.

Multidimensional scaling analysis of the dynamics of a country economy

This paper analyzes the Portuguese short-run business cycles over the last 150 years and presents the multidimensional scaling (MDS) for visualizing the results. The analytical and numerical assessment of this long-run perspective reveals periods with close connections between the macroeconomic variables related to government accounts equilibrium, balance of payments equilibrium, and economic growth. The MDS method is adopted for a quantitative statistical analysis. In this way, similarity clusters of several historical periods emerge in the MDS maps, namely, in identifying similarities and dissimilarities that identify periods of prosperity and crises, growth, and stagnation. Such features are major aspects of collective national achievement, to which can be associated the impact of international problems such as the World Wars, the Great Depression, or the current global financial crisis, as well as national events in the context of broad political blueprints for the Portuguese society in the rising globalization process.

Stressful working conditions and poor self-rated health among financial services employees

OBJECTIVE: To assess the association between exposure to adverse psychosocial working conditions and poor self-rated health among bank employees. METHODS: A cross-sectional study including a sample of 2,054 employees of a government bank was conducted in 2008. Self-rated health was assessed by a single question: "In general, would you say your health is (...)." Exposure to adverse psychosocial working conditions was evaluated by the effort-reward imbalance model and the demand-control model. Information on other independent variables was obtained through a self-administered semi-structured questionnaire. A multiple logistic regression analysis was performed and odds ratio calculated to assess independent associations between adverse psychosocial working conditions and poor self-rated health. RESULTS: The overall prevalence of poor self-rated health was 9%, with no significant gender difference. Exposure to high demand and low control environment at work was associated with poor self-rated health. Employees with high effort-reward imbalance and overcommitment also reported poor self-rated health, with a dose-response relationship. Social support at work was inversely related to poor self-rated health, with a dose-response relationship. CONCLUSIONS: Exposure to adverse psychosocial work factors assessed based on the effort-reward imbalance model and the demand-control model is independently associated with poor self-rated health among the workers studied.

Analysis of stock market indices with multidimensional scaling and wavelets

Stock market indices SMIs are important measures of financial and economical performance. Considerable research efforts during the last years demonstrated that these signals have a chaotic nature and require sophisticated mathematical tools for analyzing their characteristics. Classical methods, such as the Fourier transform, reveal considerable limitations in discriminating different periods of time. This paper studies the dynamics of SMI by combining the wavelet transform and the multidimensional scaling MDS . Six continuous wavelets are tested for analyzing the information content of the stock signals. In a first phase, the real Shannon wavelet is adopted for performing the evaluation of the SMI dynamics, while their comparison is visualized by means of the MDS. In a second phase, the other wavelets are also tested, and the corresponding MDS plots are analyzed.

Fractional dynamics in financial indexes

The goal of this study is the analysis of the dynamical properties of financial data series from 32 worldwide stock market indices during the period 2000–2009 at a daily time horizon. Stock market indices are examples of complex interacting systems for which a huge amount of data exists. The methods and algorithms that have been explored for the description of physical phenomena become an effective background in the analysis of economical data. In this perspective are applied the classical concepts of signal analysis, Fourier transform and methods of fractional calculus. The results reveal classification patterns typical of fractional dynamical systems.

Multidimensional scaling analysis of electricity market prices

This paper studies the impact of energy and stock markets upon electricity markets using Multidimensional Scaling (MDS). Historical values from major energy, stock and electricity markets are adopted. To analyze the data several graphs produced by MDS are presented and discussed. This method is useful to have a deeper insight into the behavior and the correlation of the markets. The results may also guide the construction models, helping electricity markets agents hedging against Market Clearing Price (MCP) volatility and, simultaneously, to achieve better financial results.