Carlo Drago

Interlocking Directorships and Cross-Shareholdings Among Italian Blue Chips

We contribute to the literature on cross-shareholdings and board interlocks in Italy which finds possible evidence of enlarged collusion, that is, collusion established through board interlocks among companies that do not necessarily operate in the same business sector. We focus on Italian blue chips (about 80% of total market capitalisation) in 1998 and in 2008, examining the entire network of cross-shareholdings higher than 2% of total voting rights which we compare to the entire network of interlocking directorates among these blue chips. We find that in 1998 enlarged collusion only takes place among those blue chips that are also linked by cross-shareholdings (Mediobanca Galaxy), which reinforces the hypothesis that in Italy this form of collusion is functional to shareholder expropriation. In addition, we find that in 2008 new shareholders appear at the top of the Galaxy and the Galaxy extends its reach through board interlockers to the rest of the blue chips.

A Comparison of the Director Networks of the Main Listed Companies in France, Germany, Italy, the United Kingdom, and the United States

The purpose of this paper is to contribute to the literature on director interlocks by illustrating and analysing the interlocking directorships among the Italian, French, German, UK and US listed Blue Chips. The comparison of the five countries considered shows that two national models stand out. On the one hand a model made of a high number of companies linked to each other through a small number of shared directors who serve on several company boards at the time (France, Germany, and Italy). On the other hand, in the UK much fewer companies are connected to each other essentially through directors who have no more than two board positions at the time. A case in between is represented by the US, where a high number of companies are connected to each other just like Germany, France, and Italy. However, just like the UK, such connections are made through directors who tend to have just two board positions at the time, a sign that, differently from Italy, Germany, and France, the UK and US networks might not be functional to systemic collusion.

Measuring Gender Differences in Information Sharing Using Network Analysis: The Case of the Austrian Interlocking Directorship Network in 2009

In recent literature a relevant problem has been the relationship between career/personal contactnetworks and different career paths. In addition the recent advances in social capital theory have shown the way in which networks impact on personal careers. In particular women’s careers appear to be negatively affected by the informational network structure. The main contribution of this work is to propose empirical evidence of this phenomenon by considering the gendered directorship network with relation to Austria and to show the structural differences by gender in the network. By using community detection techniques we have found various communities in which females seem not to be present at all, where females show significantly fewer contacts than males in the network, and finally where the proportion of males exceeds 91%. The results show the predominant role in the network of male directors;these differences are very relevant if we consider the network as a tool of vehicle information and as a power mechanism. In this paper we wish to make an original contribution to the debate of the well-known “glass-ceiling” effect.

Board connections and management commentary readability: the role of information sharing in Italy

Purpose This study aims to determine the impact of information-sharing disseminated through the firms’ board connections on the readability of the management discussion and analysis (MD&A). Design/methodology/approach The investigation conducted in this study is performed by using a regression analysis. The readability of the MD&A is measured by the Flesch reading ease. The level of information-sharing is determined by the degree centrality index. The sample is composed of 83 Italian-listed firms that comprise over 4,000 directors for the period 2008-2012. Findings The main results of this study show a significant relationship between the degree centrality and MD&A readability, suggesting that board connections play a crucial role in improving the quality of external reporting. Research limitations/implications This study uses a limited sample size. Further, we do not isolate the possible effect of other reporting incentives that may affect the readability of external reporting. Practical implications This study argues that for a non-English-speaking country such as Italy, information-sharing is a vehicle for improving the quality of external reporting and the competitiveness of firms in international capital markets. Originality/value This research offers an original contribution to the existent literature by highlighting the role of the firms’ board connections in determining the level of the corporate disclosure readability. This implies the opportunity for future research to take into account the firms’ board connections when they analyze related phenomena.

Visualizing and Exploring High Frequency Financial Data: Beanplot Time Series

In this paper we deal with the problem of visualizing and exploring specific time series such as high-frequency financial data. These data present unique features, absent in classical time series, which involve the necessity of searching and analysing an aggregate behaviour. Therefore, we define peculiar aggregated time series called beanplot time series. We show the advantages of using them instead of scalar time series when the data have a complex structure. Furthermore, we underline the interpretative proprieties of beanplot time series by comparing different types of aggregated time series. In particular, with simulated and real examples, we illustrate the different statistical performances of beanplot time series respect to boxplot time series.

THE DENSITY VALUED DATA ANALYSIS IN A TEMPORAL FRAMEWORK: THE DATA MODEL APPROACH

High Frequency Data are data characterized by an overwhelming number of observations in the period of reference, often a single day. Typically, these data are synthesized by their average or by the variation of the observed values in terms of the upper and lower values (or suitable quantiles). Usually, this interval or range provides interesting information on the data for the representation of the data variability. Recently, histograms and boxplots have been employed in order to obtain a more informative representation of high frequency data. Anomalies and casual or systematic errors can affect such high frequency data representation and consequent interpretation and use. In order to face such problems assuming the classical decomposition of data as the sum of a model plus an error, we propose to represent intra-period high frequency data by density models such as the beanplots, based on a suitable mixture of distributions. The location, size and shape of such models are summarized in the estimated model coefficients and visualized by means of classical beanplot silhouettes. On this modeling based approach we build a beanplots time series consisting of a vectorial time series whose elements are the estimated coefficients of each bean plot. In this way we can solve the problem of the storage of high frequency data through few coefficients: in fact, only one beanplot and the generating matrix are required. But the main advantage of using this kind of representation and the corresponding visualization is in their capacity to highlight anomalies or anticipate structural pattern changes in a beanplot time series, as well as to provide useful tools for short period forecasting. In this respect, it is fruitful to use multivariate control chart techniques to provide signals of anomalous observations or early warnings for structural changes. At the same time, these models are useful to study the evolution in the mid and long run by considering classical approaches developed for multivariate time series or approaches based on a time series factor analysis for multivariate successions of vectors of coefficients. These modelizations of single or multiple beanplot time series over the chosen period interval are also useful in forecasting problems. In the case of multiple beanplot time series based on different sets of high frequency data observed simultaneously, or of the same set observed in different occasions, cluster analysis methods can be used to search for suitable prototypes in building composite indicators or to discover homogeneous (and contiguous) time segments corresponding to pattern changes. The tools considered through this thesis are useful in various financial applications such as Trading, Stock Picking, Statistical Arbitrage and Risk Management. The Thesis is structured as follows: Chapter 1 The Analysis of Massive Data Sets Chapter 2 Complex Data in a Temporal Framework Chapter 3 Foundations of Interval Data Representations Chapter 4 Foundations of Boxplot and Histogram Data Representations Chapter 5 Foundations of Density Valued Data: Representations Chapter 6 Visualization and Exploratory Analysis of Beanplot Data Chapter 7 Beanplot Modelling Chapter 8 Beanplot Time Series Forecasting Chapter 9 Beanplot Time Series Clustering Chapter 10 Beanplot Model Evaluation Chapter 11 Case Studies: Market Monitoring, Asset Allocation, Statistical Arbitrage and Risk Management The Thesis is accompanied by a library of programs in R built on the presented methods.

Time Series Clustering from High Dimensional Data

Due to technological advances there is the possibility to collect datasets of growing size and dimension. On the other hand, standard techniques do not allow the easy management of large dimensional data and new techniques need to be considered in order to find useful results. Another relevant problem is the information loss due to the aggregation in large data sets. We need to take into account this information richness present in the data which could be hidden in the data visualization process. Our proposal - which contributes to the literature on temporal data mining - is to use some new types of time series defined as the beanplot time series in order to avoid the aggregation and to cluster original high dimensional time series effectively. In particular we consider the case of high dimensional time series and a clustering approach based on the statistical features of the beanplot time series.

Interval-Based Gender Diversity Composite Indicators in Gender Studies

This study aims to construct an original interval-based composite indicator of the gender diversity considering different assumptions on the development of the composite indicator. In this way the composite indicator built can be considered more robust than a classical version of the same indicator. Composite indicators are a very important tool to analyse and evaluate policies and sectors. The problem in using composite indicators is that the results which can be obtained can be dependent to the assumptions given on their construction. In this sense we have already considered an initial approach in the construction of composite indicator (Paoloni et al. (2016) Towards a new architecture of knowledge: Big data, culture and creativity. Proceedings, 15–17 June 2016 Dresden Germany (pp. 1944–1958)). We take into account different assumptions, and we are able to construct an interval-based composite indicator. In this way we can consider a value which is useful for the comparison (the chosen assumption), a centre of the composite indicator, and the range which is related to the variability due to the different assumptions. Our work contributes to the existing literature on composite indicators in gender studies. In particular our work is addressed on providing a composite indicator of gender diversity for the listed European companies. This study is useful to policy purposes because it helps the process of decision-making which can be based on the different rankings which are derived from the interval-based composite indicators.

An Attempt to Disperse the Italian Interlocking Directorship Network: Analyzing the Effects of the 2011 Reform

The purpose of this paper is to analyze the effects on the Italian directorship network of the corporate governance reform that was introduced in Italy in 2011 to prevent interlocking directorships in the financial sector. Interlocking directorships are important communication channels among companies and may have anticompetitive effect. We apply community detection techniques to the analysis of the networks in 2009 and 2012 to ascertain the effect of the reform. We find that, although the number of interlocking directorships decreases in 2012, the reduction takes place mainly at the periphery of the network whereas the network core is stable, allowing the most connected companies to keep their strategic position.

A Comparison of the Director Networks in the Main Companies Listed in Italy, France and the United Kingdom

The purpose of the present paper is to contribute to the empirical literature on country interlocks by illustrating and analysing the interlocking directorships in the first 40 Italian, French and British Blue Chips as of December 2007 (Italy/March 2008 (France and UK). The theoretical literature identifies among the possible explanations for interlocking directorships the reduction of information asymmetries between debtors and their creditors and suppliers and the collusion among players in the same market. Our findings show two different national models, the British one and the French-Italian one. The first model is characterised by a limited number of companies linked with each other through just one director at the time; the second model by a higher number of companies that often share several directors at the time and seem to operate under mutual scrutiny.