VERA Academy

Scientific Tutors

Antonella Basso

Martina Nardon

Abstract: Given the growing importance of green and socially responsible investments (ESG: environmental, social, governance), it is important to investigate investors' motivations for social and environmental sustainability issues, particularly for investments in mutual funds and pension funds.
Moreover it is important that this investigation is carried out, in addition to the standard finance point of view, also through the lens of behavioural finance. To this aim, the student will have to help:

• administer online questionnaires to a large number of private and institutional investors;
• administer questionnaires focused on managers who manage funds;
• update and complete the contact list for fund managers and institutional investors;
• check response consistency and analyze the results.

Internship period: September 81st – December 31st, 2020

Scientific Tutor

Luca Di Corato

Abstract: The research fellow will support a research project using stochastic dynamic programming and bargaining theory to study i) the definition of a contract for the large-scale acquisition / lease of farmland in Africa ii) the value of the agricultural project to be developed on the contract is sealed iii) the impact of taxes and subsidies on foreign direct investment in this context.
The research fellow will be asked to provide a review of the economic literature on the subject and to collect the data useful for the definition of a case study through which the predictions of the developed theoretical model can be illustrated. For some data, such as output and input prices, the fellow must test their consistency with respect to specific stochastic processes that, in the model, are assumed to illustrate their diffusion. Once calibrated the model using the collected data, the fellow is expected to perform simulations and check the sensitivity of the model’s predictions to variations in the value of the parameters.

Internship period: October 1st – December 31st, 2020

Scientific Tutor

Stefano Tonellato

Abstract: Predictive models are often required to give probabilistic forecasts, which provide more information than point forecasts about the realizations of a response variable in the future or under some prefigured scenarios. When large sets of observations are available, machine learning methods, such as random trees and random forests, can be used to cope with the high dimensionality and to define flexible predictive models. The aims of the research are:

• providing a review of random tree and random forest methods used in large dataset;
• extending, these methods and possibly coupling them with the Bayesian statistical paradigm, in order to generate probabilistic forecasts on multiple-horizon and/or on multivariate models;
• writing the code and develop an application in the domain of economics or social sciences;
• writing a final report where methods and results are presented and discussed.

Internship period: September 14th – October 8th, 2020

Scientific Tutor

Stefano Colonnello

Abstract: This project aims to study the interaction between the riskiness of corporate policies implemented by the management, managerial compensation, and the managers choice to alter their exposure to firm risk. The latter choice crucially hinges on two factors: the manager’s risk preferences and his/her personal portfolio diversification with respect to the firm.
The student will be thus involved in the collection and the analysis of data on the individual hedging choices of managers starting from annual reports of US public companies.

Internship period: July 24th – November 24th, 2020

Scientific Tutor

Andrea Minto

Abstract: Over the past few years the financial sector has undergone a structural change. New providers who combine digital technologies with financial services in an innovative manner (so-called fintechs) have been increasingly entering financial markets and taking over established financial intermediaries’ eco-nomic functions or parts of their value chain. From Google Wallet and iPay to BitCoin and decentralised ledger technologies, all these innovations have been a “wake-up call” to deal with the aspects of a new “era” of financial services and market players. Up until now, however, very little attention has been paid on whether and how technology-enabled financial innovation can be deployed to fight corruption and foster market integrity. The objective of this project is to gain a more robust understanding of the policy implication that flow from technology-enabled financial innovations vis-à-vis anti-corruption regulatory objectives.
By building upon a rich foundation of interdisciplinary literature that spans law, finance, economics and sociology, this study aims to provide a theoretical framework that adequately accounts for the nature and pace of financial innovation while attempting to mapping out the potential advantages in curbing corruption.
Disruptive innovation brings about market and/or structural impact upon society. The architecture of society itself has indeed confronted technological change. New technologies and actors caused a rapid expansion of what Lawrence Lessig referred to as “architecture”—the code, protocols, platforms and structures that determine how economic actors behave and how policy- and law- makers react —and thus raising a number of legal and societal issues that must be carefully considered. By contextualising fintech against a governance landscape that is multi-faceted and de-centred, the study intends to get a better understanding of how regulators should approach the opportunities stemming from technological advancement. In line with the aforementioned methods and techniques, the core research question(s) might be formulated
as follow:

• How can technology-enabled financial innovations (fintechs) be used to revolutionise governance and reduce global corruption?
• What kind of market failures should policy- and law-makers cope with?
• Which financial technologies are to be deployed for anti-corruption purposes?
• How will financial innovation affect the regulatory objectives relating to market integrity?
• Which legal tools could be employed to deal with the identified market failures and the prevailing competitive driving forces?

Internship period: August 3th – October 11th, 2020

Scientific Tutors

Antonella Basso

Maria Bruna Zolin

Abstract: Two strategic objectives are at the base of the growing interest in renewable energy: to contribute to reducing energy dependency and counteract the effects of climate change.
In agriculture, there are other reasons. First of all, the sustainability of the agricultural development model: agro-energies represent a necessity for the sustainability of the European production model. On the other hand, they are an opportunity for the integration of incomes in agriculture, especially during periods of stagnation or reduction in commodity prices, avoiding abandonment phenomena.
The research assistance activity will follow the following phases:

• update of the bibliography previously collected and reworking of synthesis schemes on: a) renewable and non-renewable energy sources; b) relations between renewable sources and the agricultural sector;
• reconnaissance of any "new" sources and data on renewable energy sources in terms of production and consumption;
• in-depth analysis of the methods used in the literature for the processing of the collected data mentioned in the previous point;
• identification of the most appropriate method(s) for analysing and describing the relationship between renewable energy and the agricultural sector;
• construction and analyses of a case study.

Internship period: September 15th 2020 – January 3rd 2021

Scientific Tutor

Nicola Camatti

Abstract: Accurate tourist flow forecasting is always the most important issue in tourism industry. The availability of big data (such as Tripadvisor data) allows for improving destination management organization’s decision support.
The aim of the research is:

• to review the literature on the use of big data and social media-generated big data, for decision support in the tourism sector;
• to build a database with high-frequency turistic flows, incoming and outgoing;
• to extract and analyze social media-generated big data following various methods such as network analysis tools;
• to forecast tourisms flows by applying time series models to the media-generated data;
• to write a final report where methods and results are presented and discussed.

Internship period: November 15th 2020 – March 15th 2021

Scientific Tutor

Danilo Cavapozzi

Abstract: The aim of the project is to organize a dataset suited to carry out empirical analyses aimed at assessing how the academic performance of international students enrolled in the Bachelor’s degree program in Economics and Business varies with their individual characteristics, such as their country of origin and their previous studies, and how it compares with the performance of non-international students enrolled in the same bachelor’s degree program. The analysis will be based on completely anonymized data provided by the administrative offices of Ca’ Foscari. The results of the project will be useful to understand the prevailing characteristics of the international students enrolled in Economics and Business. in order to implement effective programs to sustain their stay at Ca’ Foscari. The Research Assistant can use this dataset to develop a master thesis on the topics of the project under the supervision of the professors involved.

Internship period: September 17th – December 18th, 2020

Scientific Tutor

Marco Corazza

Abstract: Recently, financial markets have been at the center of some global crises. This led to heavy- tailed stock return distributions. In this context, it is not possible to use the standard model op portfolio selection (PS). That said, first the Apprentice will have:

• to carry out a bibliographic research on recent PS models in presence of heavy tails (HTs);
• to collaborate in the development of this PS model.

Usually, such problems do not admit exact solvers, neither analytical nor numerical. So, the use of
metaheuristics (MH) has spread. In the second part of the project, the Apprentice will have:

• to carry out a bibliographic research on ME for the solution of PS problems;
• to collaborate in implementing in Matlab environment of at least a MH to solve a PS problem in presence of HTs;
• to apply the software code so developed to real financial markets and to compare the results with those coming from appropriately chosen benchmark PS problems.

Internship period: August 24th – December 18th, 2020

Scientific Tutor

Antonella Basso

Abstract: Academia and industry are actively developing new technologies and approaches for dealing with large scale and complex computational problems. In this respect, high performance computing systems (HPC) have become an essential ingredient in many academic areas of economics, finance and insurance. The project intends to investigate the advantages of using parallelization strategies in computationally demanding numerical problems.
The aim of the research is:

• to review the literature on the use of HPC in datascience and mathematics for economic and financial problems;
• to update and review the guide to HPC and parallel computing in MATLAB and R written in a previous research project of the VERA Center;
• to write a report and to implement in MATLAB (or R and Python) parallel computing algorithms with special focus on at least one of the following topic: Artificial Intelligence, Artificial Neural Networks, Deep Learning, Least Square Monte Carlo per l’Option Pricing.

Internship period: September 7th – December 11th, 2020

Scientific Tutors

Luca Di Corato

Carlo Giupponi

Abstract: The fellow will support a research project using stochastic dynamic programming to evaluate i) the net economic benefit of an investment in a water reclamation system ii) the need and impact of public subsidies incentivizing investment in a water reclamation system. The fellow will be asked to collect data relative to water prices in Europe. S/he will then test their consistency with respect to specific stochastic processes that are assumed to illustrate their diffusion. Further, in order to calibrate the theoretical model set up by the applicants, s/he will be asked to collect data relative to the installation costs of the targeted reclamation technology and to the system maintenance costs. Once calibrated the model using the collected data, the fellow is expected to execute the final numerical exercise relative to the optimal investment choice.

Internship period: October 1st – December 31st, 2020

Scientific Tutor

Valeria Maggian

Abstract: The student will support a research project aiming to analyse the causes of women’s lower representation in the STEM field. The project will include the preparation and analysis of a dataset containing the data collected in a field experiment run during the current year at the “Math Olympics” an annual Math competition organized in several Italian high schools.
The student will be asked to manage a dataset containing the answers given by about 10.000 students to an on-line Qualtrics questionnaire, according to the guidelines given by the research team. The dataset needs also to be merged with another dataset so experience in managing data and a good knowledge of Stata is required.

Internship period: October 1st – December 31st, 2020

Scientific Tutor

Claudio Pizzi

Abstract: The aim of the stage is to analyze how the combination of models to forecast financial market trend and technical analysis indicators can improve the performance of a system of algorithmic trading rules.
As concern the forecasting models both artificial intelligence, in particular Neural Networks, Recurrent Neural Network,s Long Short-Term Memory Networks and kernel-based regression models will be used.
With regards to technical analysis indicators, their parametric configuration will be obtained using optimization meta-heuristics such as Particle Swarm Optimization.
The stage includes the following phases:

• Literature review on abovementioned models.
• Design of a trading system that combines forecasting models and technical analysis indicators.
• Implementation of the previous point.

Internship period: February 24th - May 12th, 2020

Scientific Tutor

Agar Brugiavini

Abstract: Support the analysis of profiles of income and pension contribution in Italy and in Europe using microeconomic data. Contribute to the drafting of research papers on the issue of intergenerational equity in pension systems and degrees of inequality, including considerations and a political economy approach.

Internship period: January 20th - March 20th, 2020

*Internship grant Valeria Solesin

Scientific Tutors

Antonella Basso

Roberto Casarin

Abstract: Academia and industry are actively developing new technologies and approaches for dealing with large scale and complex computational problems. In this respect, high performance computing systems (HPC) have become an essential ingredient in many academic areas of economics, finance and insurance. The project intends to investigate the advantages of using parallelization strategies in computationally demanding numerical problems. The aim of the research is:

• to review the literature on the use of HPC in datascience and mathematics for economic and financial problems;
• to study the computational gain of parallel computing on HPC wrt standard sequential computing for a set of benchmark problems, such as numerical optimization and integration, with financial and insurance applications;
• to write a final report which provides: i) an introduction to parallel computing in MATLAB (or R or Python); ii) the code description for a set of illustrative instances; iii) discussion of the main comparison results.

Internship period: January 29th - April 29th, 2020

Scientific Tutors

Monica Billio

Giovanni Angelini

Abstract: Enterprise risk management (ERM) has increasingly captured the attention of risk management professionals and academics. Differently from the classical approach to corporate risk management based on a “silo” setting, ERM enables firms to benefit from an integrated approach to manage corporate risks. It shifts the focus from primarily defensive to increasingly offensive and strategic management of the Enterprise risks.
This research will investigate the following areas:

• review of literature contributions on this risk management approach, considering, in particular, the “COSO Framework” (Committee of Sponsoring Organizations of the Treadway Commission);
• analysis of the various sectors, industries, and areas in which ERM is most effective and can be applied; evaluation of the effectiveness of ERM in terms of improved management of the firms adopting this technique;
• ERM considers also credit risk; the analysis will also take into account ESG criteria for the evaluation of creditworthiness, which considers also the environmental aspect;
• the ERM approach takes into consideration also the reputation risk for a firm; one of the goals of this analysis will be to explain how the climate component might influence the perception of the brand in an even more environment-friendly market.

Internship period: January 8th - February 14th, 2020

Scientific Tutor

Claudio Pizzi

Abstract: When we face the problem of global optimization, and we are not usually able to obtain an analytic solution, we are forced to resort to numerical methods. Several metaheuristics have been proposed in the literature and the main approaches could be connected with biology and physics. The biology-inspired algorithms mimic the evolution of species (Genetic Algorithm) or the behavior of large group of animals (for instance the Particle Swarm and Ant Colony algorithms). On the other hand the “physics” metaheuristics are linked to physical laws (for instance the gravitational law or electromagnetism-like algorithm).
The objectives of the internship can be summarized in the following four steps:

• exhaustive research on the metaheuristic algorithms proposed in the literature;
• review of the R libraries for the global optimization algorithms and their functions;
• implementation of one or more optimization algorithms not yet implemented in R;
• application of the implemented algorithms in money and financial market analysis.

Internship period: February 2nd - May 4th 2020

Scientific Tutors

Antonella Basso

Martina Nardon

Abstract: Given the increasing importance of ESG (environmental, social, governance) investments, it is important to investigate the motivations of investors with respect to social and environmental sustainability, especially for investments in mutual and pension funds.
We intend to carry out this investigation not only from the viewpoint of standard finance, but also through the lens of behavioral finance.
To this aim, the student will have to help:

• define and implement questionnaires administered online to a wide number of investors and others focused on fund managers;
• search for contacts (e.g. concerning fund managers);
• test the consistency of the responses and results and carry out a first analysis of the responses.

Internship period: February 18th - May 31st, 2020

Scientific Tutors

Stefano Tonellato

Roberto Casarin

Abstract: Forecastings of time series analysis are often given as probabilistic forecast, which provides more information than point forecasts about the future realizations of the variable of interest. When large set of observations are available machine learning methods, such as random tree and random forests, can be used to cope with the dimensionality and to define flexible forecasting models. The aims of the research are:

• to provide a review of random tree and random forest methods used in large dataset of time series analysis;
• to extend the methods to generate probabilistic forecast on multiple-horizon and on multivariate models;
• to write the code and develop an application to time series;
• to write a final report where methods and results are presented and discussed.

Internship period: February 19th - May 22nd, 2020

Scientific Tutor

Marco Corazza

Abstract: The objectives of the requested scholarship are the implementation and application of automated financial trading sistems to portfolio management based on the machine learning technique known as Reinforcement Learning. In particular, one means to address the research on the reward function, that is on the function which gives a negative or a positive reward. The most used in the literature is the well known Sharpe ratio. Although such a ratio is a celebrated risk-adjusted performance measure, it is not able to capture both some aspects characterizing the current academic researches on these measures and the practices and rules taken into account by the portfolio management industry. Some simple reward functions alternative to the Sharpe ratio have been proposed, but they are insufficient. Because of all this, we intend to focus on the specification of reward functions which are jointly theoretically founded and operatively effective.

Internship period: February 10th - September 9th, 2020

Scientific Tutor

Jan Van der Borg

Abstract: Accurate tourist flow forecasting is always the most important issue in tourism industry. The availability of big data (such as Tripadvisor data) allows for improving destination management organization’s decision support.
The aim of the research is:

• to review the literature on the use of big data and social media-generated big data, for decision support in the tourism sector;
• to extract and analyze social media-generated big data following various methods such as network analysis tools;
• to forecast tourisms flows by applying time series models to the media-generated data;
• to write a final report where methods and results are presented and discussed.

Internship period: February 3th - May 19th, 2020

Scientific Tutors

Danilo Cavapozzi

Francesca Zantomio

Abstract: The research assistant is asked to review the economic literature studying the effect of income and wealth on mental health in order to describe the state of the art in this field. The review should place particular attention on the effect on income and wealth trajectories over the life cycle on mental health inequalities in the short and long run.
The output of this research will be a literature survey that clearly summarizes the theoretical economic models motivating the research question of interest and discusses the main results coming from empirical contributions by describing the country-specific institutional contexts considered (e.g. presence and structure of income support programs) as well as the data and the econometric specifications used in the papers.

Internship period: March 2nd - July 24th, 2020

Scientific Tutor

Agar Brugiavini

Abstract: Support the analysis of profiles of income and pension contribution in Italy and in Europe using microeconomic data. Contribute to the drafting of research papers on the issue of intergenerational equity in pension systems and degrees of inequality, including considerations and a political economy approach.

Internship period: February 4th – May 4th, 2020

*Internship grant Valeria Solesin

Scientific Tutor

Luca Di Corato

Abstract: The fellow will support a research project aiming to evaluate i) the economic benefit that a prosumer of solar energy can derive from the exchange of energy with other agents present in the network and ii) its impact on his investment choices (plant capacity, investment timing, etc.).
The fellow will be asked to collect data relative to buying and selling energy prices in the Italian electricity market. S/he will then test their consistency with respect to specific stochastic processes that are assumed to illustrate their diffusion. Further, in order to calibrate the theoretical model set up by the applicant, s/he will be asked to collect data relative to the installation costs of the technology to be purchased (photovoltaic system, devices for the smart node control unit, etc.).
Once calibrated the model using the collected data, the fellow is expected to execute the final numerical exercise relative to the optimal investment choice.

Internship period: June 8th – September 30th, 2020

Scientific Tutors

Stefano Tonellato

Roberto Casarin

Abstract: Complex models often have intractable likelihoods, so methods that involve evaluation of the likelihood function are infeasible. The aims of the research are:

• to provide a review of the likelihood free methods (e.g., ABC or synthetic likelihood) used in fitting complex models large dataset;
• to use likelihood free methods to make inference on complex models such as random networks models;
• to develop the code for the analysis;
• to apply the model and methods for networks data from economics and finance such as trade, financial flows networks, financial contagion networks;
• to write a final report where methods and results are presented and discussed.

Internship period: October 14th 2019 - January 10th 2020

Scientific Tutors

Stefano Tonellato

Roberto Casarin

Abstract: The aims of the research are:

• to use Bayesian inference methods making inference and model selection for network models in the class of exponential random graphs;
• to develop the code for the analysis;
• to apply the model and methods for networks data from economics and finance such as trade, financial flows networks, financial contagion networks;
• to write a final report where method and results are presented and discussed

Internship period: October 10th 2019 - January 31st 2020

Scientific Tutor

Valeria Maggian

Abstract: The student will support a research project aiming to analyse the causes of women’s representation in science. The project will include a field experiment to be run during the next year at the “Math Olympics” an annual competition organized by the Unione Matematica Italiana in several high schools. The student will be asked to design some questionnaire in Qualtrics, according to the guidelines given by the research team. The questionnaires will then be sent to the students enrolled in the schools participating in our research project. The student will have to manage relationships with the actors involved in the research project and provide a first basic analysis of the filled questionnaires. Depending on the advancement of the research project, the student will be asked to support the organization and management of interventions aimed to close the gender gap in women’s participation to the Italian Math Olympiad (as, for example, the implementation of monetary incentives for female students).

Internship period: September 16th 2019 - January 16th 2020

Scientific Tutors

Paola Ferretti

Maria Bruna Zolin

Abstract: In an increasingly frequent way, climate changes consequences, exacerbating the concerns of different regions and countries as an example within the European Union, require a deeper knowledge of existing local natural resources, of their strengths and weakness.
The aim of the research work is the reconstruction of the cognitive framework at different territorial levels through indicators (environmental, social, economic), necessary for identifying suitable sustainable strategies in a bottom-up perspective.
The research activity is developed in different steps:

• collection and analysis of the existing bibliography on natural resources and construction of a summary scheme;
• recognition of databases and collection of existing data of natural resources in different territorial areas;
• critical analysis of the methods used in the literature for the processing of the collected data (see the previous step);
• identification of the method(s) for processing the collected data and application hypotheses.

Internship period: October 1st - December 20th, 2019

Scientific Tutors

Roberto Casarin

Monica Billio

Abstract: Entropy measures are widely used in various fields. The spanning length of the graph can be used to estimate entropy and information divergence. The application of this estimator on large datasets can be challenging.
The aims of this research are:

• to review the minimum spanning tree algorithms and the application to entropy estimation;
• to develop dimensionality reduction techniques to make feasible the application of this estimator to large datasets;
• to study the properties and effectiveness of the estimator on simulated datasets and on large financial and economic data;
• to write a final report where methods and results are presented and discussed.

Internship period: October 1st - December 21st, 2019

Scientific Tutor

Andrea Minto

Abstract: The main contribution of this project is twofold. First, it strives to measure cross-national and intertemporal variations in the content of legal rules, thereby facilitating statistical analysis of legal systems and their social and economic impacts concerning personal pension products. Second, it investigates the opportunity for efficiency gains relating to the portability of social and pension rights (i.e. through opportunities for risk diversification as well as for competition and innovation). Such analysis eventually examines how a European harmonised pension product relates to the overall creation of the capital markets union.
We intend to develop a savers’ protection index for four countries (Germany, France, Italy and Spain) and code the development of the law for over three decades. This quantification of legal rules (“leximetrics” approach) allows for comparing variations across time series and across legal systems. The index traces how savers’ protection in the selected countries has developed over a period of three decades. Using such index, various interesting questions can be addressed: i.e., it can be asked which country scores the maximum on our savers’ protection index; how much these legal systems have changed over the years; and whether the laws of the four countries are converging or diverging.
The index will include variables which are used as proxies for savers’ protection. The proxies are the following:

• Information provision
• Investment choices and options (link between accumulation and decumulation strategies)
• Guarantees (i.e. regulated, flexible, biometric and financial)
• Caps on cost and charges (i.e. regulated, flexible, …)
• Switching and transfer of funds options

Those variables have been chosen as meaningful by elaborating upon previous literature and recasting and developing proxies on savers’ and consumer protection. In examining and coding the legal rules we will take into account the fact that different legal instruments can be used to achieve a similar function. Therefore, the introduction of additional detailed sub-variables might be considered at a later stage, if needed to adequately account for variations within and across the selected legal systems.
This comparative quantitative analysis of legal instruments will be accompanied by using EUROSTAT statistical data and the EIOPA Database of pension plans and products in the EEA.

Internship period: September 16th  - December 30th, 2019

Scientific Tutor

Paolo Pellizzari

Abstract: We plan to analyse some articles of Sole 24 Ore from 2009 to 2016 to study texts covering the bank failures occurred in Veneto. In some detail, we aim at investigating the intensity of media coverage and the language that was used (presumably, “mismanagement” at inception and “fraud” at the end of the period). We may also analyse the official reports on the crisis that were produced by the Italian parliament or Regione Veneto.

Internship period: September 9th  - November 30th, 2019

Scientific Tutors

Jérôme Massiani

Monica Billio

Abstract: Monte Carlo has emerged as one of the most straightforward and powerful tools for probabilistic simulation in many fields. Socio economic studies have been touched by this phenomenon so that nowadays one can find a number of published applications that defies precise quantification but can probably be counted in hundreds. Yet, to our best knowledge, no reflection on the use, and possible misuse, of this technique has been performed to day at least in the economics field (a somehow similar attempts exist for environmental sciences: (Ferson 1996). A possible cause is that the numerosity of the applications defies any reasonable attempt of exhaustive metaanalysis. The purpose of the proposed activity is to address this gap based on a selection of 20 papers that cover the various typologies of documents (published articles, reports) and a large set of topics. Our purpose is to investigate the use of Monte Carlo by economists, and to identify possible misuses, linked for instance to arbitrariness in the selection of deterministic vs. stochastic variables, in the selection of given distributions or distribution parameters.

Internship period: September 25th  - December 29th, 2019

Scientific Tutor

Diana Barro

Abstract: ESG criteria are used by socially responsible investors (individual as well as institutional) to evaluate and select investment opportunities that match their own values. The increasing demand for socially responsible investment products that are compliant with ESG criteria, highlights the need to go deep into the analysis of the relations among return, risk and sustainability criteria. Different contributions in the literature provide discordant evidences. As a result, the inclusions of investment goals along these dimensions into a portfolio selection and management model is not trivial and require a deeper understanding of the interaction of asset behavior along these dimensions.
To this aim the research will tackle the following steps:

• review of literature contributions on the analysis of the relations among return/risk/sustainability criteria with specific reference to the most recent contributions;
• analysis of the risk profile of ESG compliant investment products with specific reference to ETFs and Equity Indexes;
• analysis of the interaction between risk and ESG performance for the considered investment products;
• analysis of the interaction between return and ESG performance for the considered investment products.

Internship period: July 7th - November 22nd, 2019

Scientific Tutors

Antonella Basso

Maria Bruna Zolin

Abstract: Two strategic objectives are at the base of the growing interest in renewable energy: to contribute to reducing energy dependency and counteract the effects of climate change.
In agriculture, there are other reasons. First of all, the sustainability of the agricultural development model: agro-energies represent a necessity for the sustainability of the European production model. On the other hand, they are an opportunity for the integration of incomes in agriculture, especially during periods of stagnation or reduction in commodity prices, avoiding abandonment phenomena.
The research assistance activity follows the following phases:

• Collection and analysis of the existing bibliography on renewable and non-renewable energy sources, elaboration of a summary scheme;
• Collection and analysis of the existing bibliography on relations between renewable sources and the agricultural sector and preparation of a summary scheme;
• Recognition of existing sources and collection of data on energy from renewable sources in terms of production and consumption;
• Critical analysis of the methods used in the literature for the processing of the collected data referred to in the previous point;
• Verification of the possible quantification (or identification of any indicators) of strengths and weaknesses for each single component of renewable energy (solar, wind, biomass ...);
• Identification of the most appropriate analysis method(s) for the description of the relationships between renewable energies and the agricultural sector;
• Construction of a case study

Internship period: November 10th 2019 - February 29th  2020

Scientific Tutors

Diana Barro

Marco Corazza

Abstract: Recently technological advancement and large availability of data pathed the way the development of new resources in the personal wealth management. Risk profiling is a key element of the process and has a specific relevance, also, from a regulatory point of view. Indeed, the inadequate assessment of the client’s risk profile can be a weakness of robo-advisory. On the other hand, the large availability of data represents a rich source of information on the client that can improve the service. The aim of the research is to study how the use of artificial intelligence and robo-advisory platforms can affect and modify risk profiling.
To this aim the research will tackle the following steps:

• Analysis of the on-line risk profiling and matching tools implemented by robo-advisory platforms;
• Review of literature contributions on risk profiling with specific reference to risk appetite and risk capacity;
• Development of a goal-based tailored decision model that allows to include a variable risk profile with respect to market conditions and life-time cycle

Internship period: July 9th - October 9th,  2019

*Grant funded by Ca’ Foscari Alumni

Scientific Tutor

Roberto Casarin

Abstract: In the interlocking directorate network an edge between two firms emerges when one or more CEOs of two different firms sit on each other's boards.
The aims of this research are:

• to review the literature on interlocking directorates from different fields with emphasis on the more recent modelling and empirical findings;
• to extract the interlocking directorate networks for Italian firms and to provide network analysis;
• to make inference on random network models using interlocking directorate data and develop further empirical investigations on the relationship between the network topology and the balance sheet indicators;
• to write a final report where methods and results are presented and discussed.

Internship period: July 18th - November 18th, 2019

*Grant funded by Ca’ Foscari Alumni

Student

Irene Bitto

Scientific Tutor

Claudio Pizzi

Abstract: The objective of the internship is to define a trading system data- driven based on a set of indicators and oscillators obtained from the technical analysis. The main issues is the selection of indicators to combine for defining a trading system. Indicators and oscillators depend on one or more parameters that are usually selected on the base of subjective evaluations. The work is based on the use of evolutionary algorithms for the estimation of these parameters.

Internship period: March 11^th - June 28^th, 2019

Student

Enrico Fornasiero

Scientific Tutor

Claudio Pizzi

Abstract: The aim of the internship is the analysis of the social inequalities and poverty in the current economies characterized by a globalization process. After a review of the indicators for measuring these phenomena, proposed in the literature, the student carries out a short empirical elaboration based on real data from the Ministry of Economy and Finance.

Internship period: March 19^th - June 30^th, 2019

Paper

E. Fornasiero, Disuguglianza e povertà. Stato dell'arte

845 K

Student

Bulat Gaifeev

Scientific Tutor

Roberto Casarin

Abstract: Accurate tourist flow forecasting is always the most important issue in tourism industry. The availability of big data (such as Tripadvisor data) allows for improving destination management organization’s decision support.

The aim of the research is:

• to review the literature on the use of big data and social media-generated big data, for decision support in the tourism sector;
• to extract and analyze social media-generated big data following various methods such as network analysis tools;
• to forecast tourisms flows by applying time series models to the media-generated data;
• to write a final report where methods and results are presented and discussed.

Student

Elisa Orlandi

Scientific Tutor

Francesca Zantomio

Abstract: The research project aims at exploring the gender gap in out-of-pocket healthcare expenditures among partners in couples of older people. Controlling for a comprehensive battery of objective and subjective health measures, indicative of healthcare needs, a first aim is that of measuring horizontal and vertical equity in household budget share allocation to each partner healthcare. The second aim is to explain how the registered gap reflects partners’ bargaining power in relation to their fertility and respective employment life history. The research assistant will provide support with respect to:

• systematic literature review;
• exploratory analysis of internationally available data sources and application procedures for possible non-EU data sources;
• possibly, data cleaning and preliminary analysis. 

Internship period: March 25^th - June 25^th, 2019

Student

Yuliya Patsyuk

Scientific Tutor

Danilo Cavapozzi

Abstract: This project is part of a line of research that we have developed in the last years. We are studying the effect of divorce and separation on a battery of socioeconomic and health outcomes at the individual level. In order to identify the causal effect of family dissolution, we would like to exploit the heterogeneity across countries and over time in the divorce and separation legislation. This project consists of:

• Collecting country-specific information about current and previous legislation on divorce and separation (from 1960 onwards, at least)
• Defining variables comparable across countries and individuals summarizing the main features of these legislations (for instance, years of separation needed to get a divorce, alimony calculation rules)
• Analysis of the correlation between this contextual information and the probability of divorce and separation of individuals

Internship period: Aprile 17^th - August 17^th, 2019

Student

Aiste Petkeviciute

Scientific Tutors

Paola Ferretti

Maria Bruna Zolin

Abstract: This research aims at understanding the consumer behaviour of IPM (Integrated Pest Management) food products. With population’s growth pressure on limited natural resources, food security and safety have become strategic goals at the global level. The IPM food products’ consumption has been little explored by the literature. Hence, during this internship project it will be studied in deep. Another goal of this internship is to define consumer behaviour. Primary data will be collected with structured questionnaires and analysed through quantitative tools proposed in Data Mining and Multicriteria Analysis. We will try to understand how attitude and perception of consumers differ when they are presented with conventional, IPM or organic food products. The analysis will consist as well of studying whether cultural, demographic, social, economic and other differences influence their consumption patterns. The study will lead to recommendations for policy makers or international organizations.

Student

Francesco Ratto

Scientific Tutor

Monica Billio

Abstract: In many different fields, the large dimension and complex structure of the data gathered can make simple statistical methods difficult to apply. Dimensionality reduction techniques allows the researcher to deal with the dimensionality issues. Among these techniques, tensor methods are gaining popularity. The aims of the research are:

• to provide a review of tensor algebra and tensors models applied in dimension reduction approaches with focus on low rank decomposition methods, regression and factor models;
• to build new models and inference methods for the analysis of high dimensional data;
• to develop the code for the analysis;
• to check the effectiveness of the proposed methods on both simulated data and real data;
• to write a final report where methods and results are presented and discussed.

Student

Elisabetta Tonet

Scientific Tutors

Paola Ferretti

Maria Bruna Zolin

Abstract: The frequent extreme events caused by natural hazards, that recently occurred in Italy and more in general in the European Union, stress the importance to define policies able to guarantee the maintenance of population in rural areas and consequently to promote a sustainable development and to valorise natural resources in a perspective of environmental protection. The project focus its analysis on the agritourism sector in the Veneto Region. The aim of the internship is to underline on the supply side if these activities provide satisfactory and long-lasting revenues for companies. On the demand side, the objective is to understand how some economic, social and environmental factors influence tourists’ choices also for addressing policies to promote territorial development and sustainable tourism.

Student

Chiara Zanardo

Scientific Tutor

Danilo Cavapozzi

Abstract: This research is part of a broader project analyzing the consequences of pension system reforms on household savings in Europe. The research assistant uses SHARE (Survey of Health, Ageing and Retirement in Europe) data to assess the differences across countries and over time in the workers’ expectations concerning replacement rate and retirement age and to understand their relationship with the pension reforms implemented in Europe in the last decades. Moreover, she assess how these expectations vary with individual characteristics such as gender, birth cohort and education.

Internship period: March 11^th - June 30^th, 2019