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Showing results. However, reforms enacted since the …. It examines this approach in the important but seldom studied context …. Ed The aim of this book is twofold: Firstly to focus on the development of new instruments and topics in the financial industry. Book Banking in Europe Borroni, M. More specifically, it ….

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Reflecting on 40 years of banking experiences, the book will open new avenues to understanding …. Ed This book offers insights into the contemporary issues in banking with a special focus on the recent European regulatory reforms, governance and the performance of firms. In the present study, we adapt and use the methodology of statistically validated networks Tumminello et al. Statistically validated networks have been introduced in network science to highlight over-expressed relationships observed between pair of agents of a complex system when repeated interactions are performed among them.

A specific property of this approach is that the methodology is designed to minimize false positives related to heterogeneity of the actors or to familywise error. In the present study, agents are Nokia investors and repeated interactions are buying, selling or buying-selling trading decisions of Nokia shares. With this approach, for each calendar year we detect groups of heterogeneous investors that are presenting similar timing and profile of trading decisions and we investigate their time evolution over the years.

We discover that the market of Nokia shares presents an ecology of groups of investors that are active over the years and are characterized by different attributes. The time scales of the profile of their trading decisions are ranging from a few months to twelve years. The different groups of investors evolving over time often present an over-expression of investors belonging to a specific category.

Our empirical results show the presence of an ecology of investors characterized by dynamics with time scales ranging from several months to a decade. The paper is organized as follows. In section Dataset and methodology we describe the database and our methodology of statistically validated networks. We discuss how the methodology is based on concepts and tools in network science. In section Results , we present the obtained results, discuss the statistical power of the test, and visualize the obtained results.

The Dynamics of statistically validated networks of investors is investigated in the next section. The section on Long term ecology of clusters investigates the time evolution of clusters and characterizes chains of clusters in terms of four attributes of investors. The role of market volatility is also discussed in this section.

The last section presents conclusions. In this paper we investigate the daily trading decisions of investors corporations, organizations, and individuals trading the Nokia stock during the time period from January to December Our data source is the financial asset ownership database collected by Euroclear Finland previously Nordic Central Securities Depository Finland.

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This database is obtained from the central register of shareholdings for Finnish financial assets recorded at the Finnish Central Securities Depository. The register records the shareholdings both institutional and retail of all Finnish investors and of those foreign investors exercising their vote right. The database is updated on a daily basis. Recorded transactions cover the Nordic Stock Exchange and worldwide stock exchanges where Nokia stock was traded in the considered period of time.

In our study, each investor is identified by a unique legal entity. Euroclear Finland classifies investors into six broad categories; they are: non-financial corporations, financial and insurance corporations, general governmental organizations, non-profit institutions, households, and foreign organizations.


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The starting date of the database is January 1st, The database was updated from the starting date until by considering each market transaction done by investors. At the end of technical changes in recording the date of transactions and aggregation of market transactions of the same investors limit the usability of the data.

Due to this technical change, even though the data is available up to the current date, we study the period — Several studies have used this database to investigate characteristics of the investment decisions of distinct categories of investors. A series of studies has been performed by Grinblatt and Keloharju Grinblatt and Keloharju, ; Grinblatt and Keloharju, primarily focusing on the trading style of individual and institutional investors, and on behavioral aspects in investors of the households category.

Other studies have considered the synchronicity of trading decisions of investors Tumminello et al. Legal considerations impact the way the database is designed and information is stored. For example, information about Finnish domestic investors or foreign investors asking to exercise their vote right and foreign investors can be stored in a quite different way.

In fact, foreign investors can choose to use nominee registration. Footnote 1. One key aspect of the trading activity of investors is heterogeneity. For example, there are investors which are acting only a few times during the considered time period and investors trading continuously. We are performing our analysis yearly. To quantify similarity in the trading decisions we require that an investor has performed a minimum number of trading decisions in each analyzed calendar year. A comparison between Table I and Table II of SI shows that the number of active investors is a rather limited fraction of the set of investors.

On average active investors are approximately only However this percentage primarily reflects the percentage of active households When we consider the average percentage of active investors for other categories we obtain In other words corporations, institutions and organizations are on average more active than households and among the institutions and organizations, financial corporations and governmental organizations are the most active. In this paper, we analyze the synchronous trading decisions of investors by using concepts and tools of complex networks Newman, ; Barabasi, In our approach, for each calendar year, we define a bipartite network where one set of the nodes are investors and the other set of nodes are three types of trading decisions for each trading day.

As frequently done in network science Newman, ; Barabasi, from the bipartite network one obtains a projected network with nodes of just one type. Here we are considering the projected network of investors having performed the same trading decision of buying, selling, and buying and selling see below for a quantitative definition in at least one trading day. For such a liquid stock as Nokia, the projected network is rather dense. Many active investors are making the same trading decision in at least one day.

These networks are therefore not especially informative regarding the high similarity of trading decision of groups of investors connected in the projected networks. To highlight pairs of investors that are characterized by a number of co-occurences of trading decisions that cannot be explained in terms of a random null hypothesis, we use the method of statistically validated networks Tumminello et al. With this methodology, introduced in Tumminello et al. For the sake of completeness, we briefly sketch the statistical procedure below.

In statistics, a categorical variable can take a limited number of values describing a qualitative property. Specifically, we use a categorical variable describing the daily trading decisions of an investor. Our categorical variable is defined as follows: let us call V s i , t the volume sold and V b i , t the volume purchased of Nokia stock by the investor i at day t.

We convert these quantities into a categorical variable with 3 possible states: investor primarily buying b , investor primarily selling s , and investor buying and selling bs during the trading day.