Maximizing upgrading and downgrading margins for ordinal regression

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since upgrading and downgrading errors may not have the same importance, they should be considered as two different criteria to be taken into account when measuring the quality of a classifier. In Support Vector Machines, margin maximization is used as an effective and computationally tractable surrogate of the minimization of misclassification errors. As an extension, we consider in this paper the maximization of upgrading and downgrading margins as a surrogate of the minimization of upgrading and downgrading errors, and we address the biobjective problem of finding a classifier maximizing simultaneously the two margins. The whole set of Pareto-optimal solutions of such biobjective problem is described as translations of the optimal solutions of a scalar optimization problem. For the most popular case in which the Euclidean norm is considered, the scalar problem has a unique solution, yielding that all the Pareto-optimal solutions of the biobjective problem are translations of each other. Hence, the Pareto-optimal solutions can easily be provided to the analyst, who, after inspection of the misclassification errors caused, should choose in a later stage the most convenient classifier. The consequence of this analysis is that it provides a theoretical foundation for a popular strategy among practitioners, based on the so-called ROC curve, which is shown here to equal the set of Pareto-optimal solutions of maximizing simultaneously the downgrading and upgrading margins

Ordinal regression methods: survey and experimental study

Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if the use of the order information improves the performance of the models obtained, considering some of the approaches within the taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the predictions to actual targets in the ordinal scal

Global value chains and the impact of COVID-19 crisis: the case of the Italian gold jewellery

openThis thesis aim consists in analysing, throughout the international lenses of Global Value Chains, the reactions and the responses of the three main Italian gold jewellery industrial districts - namely, Arezzo, Valenza Po and Vicenza - against Covid-19 crisis. Thanks to the blend of two important frameworks such as GVCs and IDs, the contribution that the thesis wants to offer consists in an empirical quantitative analysis that embrace the local and the global dimensions of jewellery IDs. After a review of the related literature and on the main effects of Covid-19 crisis on the Italian economy and on the jewellery industry, the research proceeds with the definition of three hypotheses. The first is related to the ability of firms pertaining to IDs of being more performing against crises with respect to other non-district firms: in order to test this hypothesis, a statistical model was built. The second hypothesis refers to the ability of IDs embedded within GVCs suffered more the initial phase of Covid-19 crisis of being more able to quickly recover from recession periods. To verify this hypothesis, a deep analysis of import and export official data (found on Coeweb, ISTAT) has been done. The third hypothesis wants to make a comparison between the current crisis and other past crises that IDs faced. What has emerged, referring to the first hypothesis, is that a "district effect" across cluster firms and non-district firms is not statistically significant within the model utilized. What resulted for the second hypothesis and also according to second sources, is that the jewellery IDs showed a considerable degree of resilience and adaptation during crisis periods. For the last hypothesis, some interesting points have emerged: even though the performance, import and export trends of 2020 show similarities with data referring to the Great Recession and the globalization crisis (De Marchi et al., 2014), the reactions of IDs have been different and mainly related to higher diversification of export markets. The most adaptive district, Arezzo, also re-focused its core business concentrating its production during crisis in gold bars in order to mitigate the negative effects of the economic downturn, reducing at the same time the production of jewels.This thesis aim consists in analysing, throughout the international lenses of Global Value Chains, the reactions and the responses of the three main Italian gold jewellery industrial districts - namely, Arezzo, Valenza Po and Vicenza - against Covid-19 crisis. Thanks to the blend of two important frameworks such as GVCs and IDs, the contribution that the thesis wants to offer consists in an empirical quantitative analysis that embrace the local and the global dimensions of jewellery IDs. After a review of the related literature and on the main effects of Covid-19 crisis on the Italian economy and on the jewellery industry, the research proceeds with the definition of three hypotheses. The first is related to the ability of firms pertaining to IDs of being more performing against crises with respect to other non-district firms: in order to test this hypothesis, a statistical model was built. The second hypothesis refers to the ability of IDs embedded within GVCs suffered more the initial phase of Covid-19 crisis of being more able to quickly recover from recession periods. To verify this hypothesis, a deep analysis of import and export official data (found on Coeweb, ISTAT) has been done. The third hypothesis wants to make a comparison between the current crisis and other past crises that IDs faced. What has emerged, referring to the first hypothesis, is that a "district effect" across cluster firms and non-district firms is not statistically significant within the model utilized. What resulted for the second hypothesis and also according to second sources, is that the jewellery IDs showed a considerable degree of resilience and adaptation during crisis periods. For the last hypothesis, some interesting points have emerged: even though the performance, import and export trends of 2020 show similarities with data referring to the Great Recession and the globalization crisis (De Marchi et al., 2014), the reactions of IDs have been different and mainly related to higher diversification of export markets. The most adaptive district, Arezzo, also re-focused its core business concentrating its production during crisis in gold bars in order to mitigate the negative effects of the economic downturn, reducing at the same time the production of jewels

THE DARK SIDE OF ETHICS IN FINANCE: THE VALUE RELEVANCE OF ESG RATING FOR ITALIAN LISTED COMPANIES.

This research embraces themes of Management Sciences, Empirical Corporate Finance and Behavioral Finance, investigating the correspondence between socially responsible strategies of major Italian listed companies and the willingness of institutional and not investors to undertake sustainable corporate investments. It analyzes how the issuance of ratings involving the Environmental, Social and Governance dimensions (ESG Rating since now) of Italian listed companies may influence their financial performances on stock exchange markets. A marked focus will also be addressed to the attention that investors have paid in recent years to the usage of more than mere technical variables in investment portfolio building. The study highlights on one hand how Italian listed companies have reacted to bearing wave of the subprime mortgages crisis and Italian Sovereign Debt crisis, opting for a socially responsible and sustainable investment policy; on the other hand how institutional investment funds or investors so-called outsiders have adopted the ESG paradigm in their capital allocation. A lot of empirical works have been developed to study the potential relationship between corporate social responsibility activities and other traditional measures of a firm’s success. Moreover, various groups such as the Global Reporting Initiative (GRI) , have paid an increasing attention to the corporate social performances (CSP) of organizations since the 1990s, regardless of their size and location. Hence, a socially responsible and sustainable strategic orientation could both on one hand reduce the risk profile of a company, and on the other hand allow a better fundraising on stock exchange markets. These advantages have created a substrate meant to develop standardized business tools of social reporting as well as the emergence of social stock exchange for “future proofed” bonds’ dealing (e.g. Social Stock Exchange - London). In fact, it would be enough to check the profile of major Italian companies, also medium and small, to understand how they have drawn over time voluntary statements such as Ethical Codes and Social Reports aimed at informing the market about not-accounting information. Such investor behavior, better known as Socially Responsible Investing, has upset the principles of Investment Theory, introducing a new paradigm that takes into account the social and environmental impact of capital allocation as well as the governance aspect if an investment is undertaken by an enterprise

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data