Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events

Information Sharing and Cross-border Entry in European Banking

Information asymmetries can severely limit cross-border border expansion of banks. When a bank enters a new market, it has incomplete information about potential new clients. Such asymmetries are reduced by credit registers, which distribute financial data on bank clients. We investigate the interaction of credit registers and bank entry modes (in form of branching and M&A) by using a new set of time series cross-section data for the EU-27 countries. We study how the presence of public and private credit registers and the type of information exchanged affect bank entry modes during the period 1990-2007. Our analysis shows that the existence of both types of registers increases the share of branching in the overall entries. Additionally, the establishment of public registers reduces concentration ratios, and some banking competition indicators (such as overhead costs/assets). The introduction of a private credit bureau, on the other hand, has no effect on concentration ratios, but positively contributes to competition (by decreasing interest rate margins). This suggests that credit registers facilitate direct entry through a reduction of information asymmetries, which in turn intensifies competition.credit registries, foreign entry, asymmetric information

Access to Credit Information Promotes Market Entries of European Banks

When granting credit, banks depend on reliable information about the creditworthiness and risk structure of potential borrowers. This information is typically gathered by national credit bureaus. Nationally established banks depend on information from credit bureaus more than ever, particularly when entering a foreign market. This DIW study (which is partially based upon research by the same authors for the European Credit Research Institute and data collected by the institute) investigates whether the existence of credit bureaus influences European bank competition and concludes that they facilitate market entry for foreign banks. In turn, the absence of credit bureaus can create significant disadvantages in competition. In this case, a market entry is then primarily possible via the purchase of an incumbent bank, because the entering institution has essentially no other opportunity to access debtor data. This study also shows that provision of data within the EU is not harmonized overall.Credit registers, Foreign entry asymmetric information

The luminosity--volume test for cosmological Fast Radio Bursts

We perform the luminosity--volume test, also known as $\langle V/V_{MAX}\rangle$, to Fast Radio Bursts (FRBs). We compare the 23 FRBs, recently discovered by ASKAP, with 20 of the FRBs found by Parkes. These samples have different flux limits and correspond to different explored volumes. We assume that their dispersion measure indicates their redshift and apply the appropriate cosmological corrections to the spectrum and rate in order to compute the $\langle V/V_{MAX}\rangle$ for the ASKAP and Parkes samples. For a radio spectrum of FRBs $F_\nu \propto \nu^{-1.6}$, we find $\langle V/V_{MAX}\rangle=0.66\pm 0.05$ for the ASKAP sample, that includes FRBs up to $z=0.7$, and 0.52$\pm 0.04$ for Parkes, that extends up to $z=2.1$. The ASKAP value suggests that the population of FRB progenitors evolves faster than the star formation rate, while the Parkes value is consistent with it. Even a delayed (as a power law or gaussian) star formation rate cannot reproduce the $\langle V/V_{MAX}\rangle$ of both samples. If FRBs do not evolve in luminosity, the $\langle V/V_{MAX}\rangle$ values of ASKAP and Parkes sample are consistent with a population of progenitors whose density strongly evolves with redshift as $\sim z^{2.8}$ up to $z \sim 0.7$. We discuss possible scenarios accounting for our results.Comment: 10 pages, 5 figures, 3 tables, accepted by A&A on 2019/04/0

Polarization of Cosmic Microwave Background

In this work we present an extension of the ROMA map-making code for data analysis of Cosmic Microwave Background polarization, with particular attention given to the inflationary polarization B-modes. The new algorithm takes into account a possible cross-correlated noise component among the different detectors of a CMB experiment. We tested the code on the observational data of the BOOMERanG (2003) experiment and we show that we are provided with a better estimate of the power spectra, in particular the error bars of the BB spectrum are smaller up to 20% for low multipoles. We point out the general validity of the new method. A possible future application is the LSPE balloon experiment, devoted to the observation of polarization at large angular scales.Comment: 6 pages, 4 figures, proceedings of the 6th Young Researchers Meeting, L'Aquila, Oct 12th-14th 201

I lieviti del Vino Fiano di Avellino DOCG: la tipicità attraverso le biotecnologie

I microrganismi caratteristici di un prodotto tipico fermentato come il vino, dai quali dipendono molte delle proprietà organolettiche, riflettono, in molti casi, le caratteristiche dell’ambiente e dei sistemi di produzione. Infatti, i prodotti alimentari di nicchia legano la loro storia alla zona di produzione non solo per le tradizioni che si tramandano nel tempo, ma anche e soprattutto per la presenza di specie e ceppi di microrganismi che colonizzano la materia prima, nonché gli ambienti e le attrezzature di lavorazione, contribuendo in maniera decisiva a caratterizzare la tipicità del prodotto finale. Nello spirito delle “Denominazioni d’Origine”, particolare importanza deve essere ancora attribuita a tutti quegli elementi che creano il “legame” tra un determinato prodotto e una particolare zona geografica, la cultura della popolazione e tutti quegli aspetti che possono essere sintetizzati nell’insieme dei fattori storico-sociali nei quali risiedono i motivi dello sviluppo di un prodotto in una data area antropizzata. I microrganismi rientrano sicuramente tra questi fattori e costituiscono, anzi, uno dei “segreti” dei produttori artigianali che, nel tempo, sono stati svelati e convertiti in moderna tecnologia di trasformazione. Anche l’impiego di ceppi “autoctoni”, selezionati durante la trasformazione di prodotti tradizionali e legati allo sviluppo di componenti aromatiche desiderate, al prolungamento della shelf-life, agli aspetti salutistici dei prodotti finiti e cosìvia, può richiedere l’adattamento di protocolli produttivi già esistenti per migliorare le performance del(i) microrganismo(i). Tuttavia, tale innovazione non può essere “selvaggia”, bensì dovrebbe essere rispettosa degli aspetti essenziali e peculiari della tradizione di un prodotto. In questo lavoro svolto nell'areale del Fiano di Avellino DOCG si sono selezionati dall'ambiente vigneto 2 ceppi di Saccharomyces cerevisiae da utilizzare come starter nella produzione di qualità del vino Fian

In the Defense of Ontological Foundations for Conceptual Modeling

Abstract not available

Semantics, Ontology and Explanation

The terms 'semantics' and 'ontology' are increasingly appearing together with 'explanation', not only in the scientific literature, but also in organizational communication. However, all of these terms are also being significantly overloaded. In this paper, we discuss their strong relation under particular interpretations. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their assumed truthmakers, i.e., the entities in one's ontology that make the propositions in those descriptions true. To illustrate this idea, we employ an ontological theory of relations to explain (by revealing the hidden semantics of) a very simple symbolic model encoded in the standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in properly supporting semantic interoperability tasks. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the area of Artificial Intelligence