Maria Carapeto

Testing for heteroscedasticity in regression models

A new test for heteroscedasticity in regression models is presented based on the Goldfeld-Quandt methodology. Its appeal derives from the fact that no further regressions are required, enabling widespread use across all types of regression models. The distribution of the test is computed using the Imhof method and its power is assessed by performing a Monte Carlo simulation. We compare our results with those of Griffiths & Surekha (1986) and show that our test is more powerful than the wide range of tests they examined. We introduce an estimation procedure using a neural network to correct the heteroscedastic disturbances.

EMERGING PATTERNS IN DEVIATIONS FROM ABSOLUTE PRIORITY RULES IN BANKRUPTCY

This paper documents emerging patterns in deviations from absolute priority (DAPs) in Chapter 11 bankruptcies. Priority rules are violated in at least two-thirds of all cases, with equityholders benefiting in most situations of violation. However, DAPs have been decreasing over time, with the index of deviations from absolute priority rules more than halving over a 12-year period. This fact is best explained by the reduced bargaining power of the debtor, smaller complexity of the cases, and choice of bankruptcy venue. Interestingly, both unsecured creditors and equityholders now benefit from DAPs to the detriment of secured creditors.

The determinants of gender pay gap in Portuguese private firms

Purpose – The paper aims to investigate the existence of gender segregation and to analyse the determinants of gender income disparity in Portuguese private firms.Design/methodology/approach – The main research instrument is a qualitative and quantitative questionnaire with 3,906 individuals from 75 Portuguese private firms. This paper uses separate multivariate ordered tobit models for men and women.Findings – It is found that job segregation is one of the major sources of gender inequality in the labour market but does not contribute for a considerable gender pay gap. In fact only scant and nuanced evidence of a negative gap of 2.2 percent against women is found. However, no support for the contention that women are systematically underpaid if they work in occupations where females are predominant is found. When it comes to variable pay, differences between men and women are less significant than with base pay.Practical implications – The paper reveals that the under‐representation of women in high‐payi...

On model complexity and selection

In the past, practitioners and researchers have compared the performance of neural networks with other model classes based on the multiple correlation coefficient or empirical validation. Such comparisons are biased towards neural networks as such performance metrics do not account for model complexity. Model complexity metrics are essential for parameter significance ( e.g. , F -test) and model mis-specification tests ( e.g. , autocorrelation). The estimation of degrees of freedom from the projection matrix of regression is therefore vitally important in all phases of the model building process for neural regression models. Degrees of freedom are used to measure model complexity and thus adjust statistics so that they may be meaningfully applied to regression models. In this paper we derive expressions for the influence matrix in linear and non-linear regression models and non-linear models with regularization, including neural networks. We show that they can be obtained within the same framework. In particular, we demonstrate that previous results obtained for neural networks hold for models without regularization terms or large sample sizes. We show how these results are used to adjust the multiple correlation coefficient by the degrees of freedom. The methodology is demonstrated using simulated data from a Cobb-Douglas type function.

Assessing Market Attractiveness for Mergers and Acquisitions: The MARC M&A Maturity Index

This paper develops a new scoring methodology to determine a country’s capability to develop and sustain mergers and acquisitions (M&A) activity on the basis of publicly available and continuously updated information. The study computes a theoretically grounded maturity index for M&A purposes (MARC M&A Maturity Index) using 36 factors in total which capture key legal, economic, financial, political, technological, and socio-cultural characteristics from a total of 175 countries based on information available at the end of 2009. The index is then used to classify different maturity stages of development in M&A activity, i.e., mature, transitional, and emerging markets. The difference in score between the stages of maturity is found to be highest for the political and technological environments, suggesting that these areas of a country’s development stage are prerequisites for M&A maturity. Tests show that it is only the socio-cultural environment that acts as a determinant of M&A activity within the mature markets group, whereas the economic, financial, political, and technological environments determine differences in M&A activity amongst countries in the transitional development stage. Interestingly, political factors appear to be inversely related to M&A activity in transitional markets, while technological and socio-cultural factors seem to slightly explain the scores obtained by emerging economies.

M&A Maturity Index: Evidence from Seven Emerging Markets

This paper develops a useful, robust, and reliable scoring methodology to determine a country’s capability to develop and sustain mergers and acquisitions (M&A) activity on the basis of publically available and continuously updated information. Due to the global economic downturn in 2008-2009, many Western companies are now looking to focus their business expansion in emerging markets where growth possibilities are perceived as better. The focus of this paper is therefore on emerging markets as illustrated by the seven countries selected as case studies (representing key emerging markets), however the scoring methodology could be applied to any country. This study analyses 31 factors in total which capture key legal, economic, financial, political, technological, and socio-cultural characteristics of countries. Each factor has been given a score of 1 to 5, where 1 represents fully open to M&A and 5 represents closed to M&A activity. The findings show an overall score of 2.70 for the seven countries compared to scores of 1.61 and 1.65 for the US and UK, respectively. The lower scores for the US and the UK signify the validity of the scoring methodology since these two countries are regarded as the most developed and open to M&A, and have the highest M&A activity. Interestingly, South Korea has the lowest score out of the seven selected case study countries (1.84), i.e., it is the country found to be best equipped to attract and sustain M&A, owing to the presence of a developed regulatory system, political stability, and high technical innovation. Russia has the worst score (3.26) mainly due to economic and political instability, making it a less attractive market.

Distress resolution strategies in the banking sector: Implications for global financial crises

This chapter investigates the effectiveness and the motivation behind the choice of different types of distress resolution strategies in the banking sector. This is a global study that analyzes key financial characteristics of distressed banks that were either acquired by other banks, divested assets, or were subject to government intervention, as well as the change in the financial profile of those distressed institutions from one year pre-deal to three years post-deal. The results show that governments intervene in the (relatively) best performers that only underperform in liquidity ratios, an indication of critical short-term flow problems. Distressed sellers, the underperformers of the three groups, enjoy much improved performance, in particular in cross-border deals. There is some evidence of foreign acquirers “cherry picking” the least distressed banks, though no significant differences in target performance remain post-deal between cross-border and domestic deals. These findings provide some useful guidance for policy makers globally and for future financial crises that impact the banking sector.