Statistical learning models to measure the impact of COVID-19 on financial fragility.

This paper investigates the effects of the economic shock produced by the COVID-19 outbreak and diffusion on households'. Through a survey administered to Italian households, without loss of generality, we investigate changes in financial and economic decisions and the households' ability to cope with daily purchases, repay their debt obligations and face unexpected expenses. The paper also applies a statistical learning model through a synthetic indicator for the financial vulnerability of households, integrating the relevant information on the financial literacy and education of the surveyed individuals.

Bayesian time-varying autoregressive models of COVID-19 epidemics.

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.

A New Interactive Tool to Visualize and Analyze COVID-19 Data: The PERISCOPE Atlas.

Since the start of the 21st century, the world has not confronted a more serious threat to global public health than the COVID-19 pandemic. While governments initially took radical actions in response to the pandemic to avoid catastrophic collapse of their health care systems, government policies have also had numerous knock-on socioeconomic, political, behavioral and economic effects. Researchers, thus, have a unique opportunity to forward our collective understanding of the modern world and to respond to the emergency situation in a way that optimizes resources and maximizes results. The PERISCOPE project, funded by the European Commission, brings together a large number of research institutions to collect data and carry out research to understand all the impacts of the pandemic, and create predictive models that can be used to optimize intervention strategies and better face possible future health emergencies. One of the main tangible outcomes of this project is the PERISCOPE Atlas: an interactive tool that allows to visualize and analyze COVID-19-related health, economic and sociopolitical data, featuring a WebGIS and several dashboards. This paper describes the first release of the Atlas, listing the data sources used, the main functionalities and the future development.

Endemic-epidemic models to understand COVID-19 spatio-temporal evolution.

We propose an endemic-epidemic model: a negative binomial space-time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterized by similar non-pharmaceutical policy interventions.

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk.

In credit risk estimation, the most important element is obtaining a probability of default as close as possible to the effective risk. This effort quickly prompted new, powerful algorithms that reach a far higher accuracy, but at the cost of losing intelligibility, such as Gradient Boosting or ensemble methods. These models are usually referred to as "black-boxes", implying that you know the inputs and the output, but there is little way to understand what is going on under the hood. As a response to that, we have seen several different Explainable AI models flourish in recent years, with the aim of letting the user see why the black-box gave a certain output. In this context, we evaluate two very popular eXplainable AI (XAI) models in their ability to discriminate observations into groups, through the application of both unsupervised and predictive modeling to the weights these XAI models assign to features locally. The evaluation is carried out on real Small and Medium Enterprises data, obtained from official italian repositories, and may form the basis for the employment of such XAI models for post-processing features extraction.

The impact of the SARS-CoV-2 pandemic on financial markets: a seismologic approach.

This work investigates financial volatility cascades generated by SARS-CoV-2 related news using concepts developed in the field of seismology. We analyze the impact of socio-economic and political announcements, as well as of financial stimulus disclosures, on the reference stock markets of the United States, United Kingdom, Spain, France, Germany and Italy. We quantify market efficiency in processing SARS-CoV-2 related news by means of the observed Omori power-law exponents and we relate these empirical regularities to investors' behavior through the lens of a stylized Agent-Based financial market model. The analysis reveals that financial markets may underreact to the announcements by taking a finite time to re-adjust prices, thus moving against the efficient market hypothesis. We observe that this empirical regularity can be related to the speculative behavior of market participants, whose willingness to switch toward better performing investment strategies, as well as their degree of reactivity to price trend or mispricing, can induce long-lasting volatility cascades.

Monitoring COVID-19 contagion growth.

We present a statistical model that can be employed to monitor the time evolution of the COVID-19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short-term and long-term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost-effective.

A Mathematical Modeling of Freezing Process in the Batch Production of Ice Cream.

This study deals with the mathematical modeling of crystallization kinetics occurring during batch production of the ice cream. The temperature decrease was recorded in-situ through a computerized wireless system. A robust pattern-recognition algorithm of the experimental cooling curves was developed to determine the initial freezing point. The theoretical freezing point was used to calibrate the whole time-temperature profile. Finally, a modified Gompertz's function was used to describe the main steps of crystallization kinetics. Derivative analysis of the Gompertz's function allowed to determine the time-temperature physical markers of dynamic nucleation, ice crystal growth and air whipping. Composition and freezing properties were used as input variables in multivariate analysis to classification purposes of the ice cream mixtures as a function of their ability to produce high-quality ice cream. The numerical analysis of the whole cooling curve was used to build predictive models of the ice cream quality indices.

COVID-19 contagion and digital finance.

Digital finance is going to be heavily affected by the COVID-19 outbreak. We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19, so that its impact on finance can possibly be anticipated, and digitally monitored. The model is a Poisson autoregression of the daily new observed cases, and considers both short-term and long-term dependence in the infections counts. Model results are presented for the observed time series of China, the first affected country, but can be easily reproduced for all countries.

Monitoring Covid-19 Policy Interventions.

A very key point in the process of the Covid-19 contagion control is the introduction of effective policy measures, whose results have to be continuously monitored through accurate statistical analysis. To this aim we propose an innovative statistical tool, based on the Gini-Lorenz concentration approach, which can reveal how well a country is doing in reducing the growth of contagion, and its speed.

Network Models to Enhance Automated Cryptocurrency Portfolio Management.

The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.

Explainable AI in Fintech Risk Management.

The paper proposes an explainable AI model that can be used in fintech risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model employs Shapley values, so that AI predictions are interpreted according to the underlying explanatory variables. The empirical analysis of 15,000 small and medium companies asking for peer to peer lending credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain and understand their credit score and, therefore, to predict their future behavior.

Exploring K2G30 Genome: A High Bacterial Cellulose Producing Strain in Glucose and Mannitol Based Media.

Demands for renewable and sustainable biopolymers have rapidly increased in the last decades along with environmental issues. In this context, bacterial cellulose, as renewable and biodegradable biopolymer has received considerable attention. Particularly, acetic acid bacteria of the Komagataeibacter xylinus species can produce bacterial cellulose from several carbon sources. To fully exploit metabolic potential of cellulose producing acetic acid bacteria, an understanding of the ability of producing bacterial cellulose from different carbon sources and the characterization of the genes involved in the synthesis is required. Here, K2G30 (UMCC 2756) was studied with respect to bacterial cellulose production in mannitol, xylitol and glucose media. Moreover, the draft genome sequence with a focus on cellulose related genes was produced. A pH reduction and gluconic acid formation was observed in glucose medium which allowed to produce 6.14 ± 0.02 g/L of bacterial cellulose; the highest bacterial cellulose production obtained was in 1.5% (w/v) mannitol medium (8.77 ± 0.04 g/L), while xylitol provided the lowest (1.35 ± 0.05 g/L) yield. Genomic analysis of K2G30 revealed a peculiar gene sets of cellulose synthase; three bcs operons and a fourth copy of bcsAB gene, that encodes the catalytic core of cellulose synthase. These features can explain the high amount of bacterial cellulose produced by K2G30 strain. Results of this study provide valuable information to industrially exploit acetic acid bacteria in producing bacterial cellulose from different carbon sources including vegetable waste feedstocks containing mannitol.

Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.

Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.

Spatial Regression Models to Improve P2P Credit Risk Management.

Calabrese et al. (2017) have shown how binary spatial regression models can be exploited to measure contagion effects in credit risk arising from bank failures. To illustrate their methodology, the authors have employed the Bank for International Settlements' data on flows between country banking systems. Here we apply a binary spatial regression model to measure contagion effects arising from corporate failures. To derive interconnectedness measures, we use the World Input-Output Trade (WIOT) statistics between economic sectors. Our application is based on a sample of 1,185 Italian companies. We provide evidence of high levels of contagion risk, which increases the individual credit risk of each company.

Factorial Network Models to Improve P2P Credit Risk Management.

This paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.

Biotechnological production of cellulose by acetic acid bacteria: current state and perspectives.

Bacterial cellulose is an attractive biopolymer for a number of applications including food, biomedical, cosmetics, and engineering fields. In addition to renewability and biodegradability, its unique structure and properties such as chemical purity, nanoscale fibrous 3D network, high water-holding capacity, high degree of polymerization, high crystallinity index, light transparency, biocompatibility, and mechanical features offer several advantages when it is used as native polymer or in composite materials. Structure and properties play a functional role in both the biofilm life cycle and biotechnological applications. Among all the cellulose-producing bacteria, acetic acid bacteria of the Komagataeibacter xylinus species play the most important role because they are considered the highest producers. Bacterial cellulose from acetic acid bacteria is widely investigated as native and modified biopolymer in functionalized materials, as well as in terms of differences arising from the static or submerged production system. In this paper, the huge amount of knowledge on basic and applied aspects of bacterial cellulose is reviewed to the aim to provide a comprehensive viewpoint on the intriguing interplay between the biological machinery of synthesis, the native structure, and the factors determining its nanostructure and applications. Since in acetic acid bacteria biofilm and cellulose production are two main phenotypes with industrial impact, new insights into biofilm production are provided.

Evaluation of fingerprinting techniques to assess genotype variation among Zygosaccharomyces strains.

Molecular typing techniques are key tools in surveillance of food spoilage yeasts, in investigations on intra-species population diversity, and in tracing selected starters during fermentation. Unlike previous works on strain typing of Zygosaccharomyces spoilage species, here Zygosaccharomyces mellis and the Zygosaccharoymces rouxii complex yeasts, which include Z. rouxii, Zygosaccharomyces sapae, and a mosaic lineage (ML) of putatively hybrids, were evaluated by three typing methods for intra- and inter-species resolution. Overall these yeasts are relevant for food fermentation and spoilage, but are quite difficult to discriminate at strain and species level as they evolved by reticulation. A pool of 76 strains from different sources were typed by M13 and (GTG)5 MSP-PCR fingerprinting and PCR-RFLP of ribosomal intergenic spacer region (IGS). We demonstrated that M13 overcame (GTG)5 fingerprinting to group Z. sapae, Z. rouxii, Z. mellis and the ML isolates in congruent distinct clusters. Even if (GTG)5 primer yielded a number of DNA fingerprints comparable with those obtained by M13 primer, it failed to discriminate Z. sapae, Z. mellis and Z. rouxii at species level. Clustering of IGS RFLP patterns obtained with three endonucleases produced groups congruent with species assignment and highlighted intra-species diversity similar to that observed by M13 fingerprinting. However, IGS PCR amplification failed for 14 ML and 6 Z. mellis strains under the experimental conditions tested here, indicating that this marker could be less easy to use in fast typing protocol. Finally, our results posit that the genetic diversity within Z. sapae and Z. mellis could be shaped by isolation source. The information generated in this study would facilitate the monitoring of these yeasts during food processing and storage, and provides preliminary evidences about Z. sapae and Z. mellis intra-species diversity.

A multi-phase approach to select new wine yeast strains with enhanced fermentative fitness and glutathione production.

The genetic improvement of winemaking yeasts is a virtually infinite process, as the design of new strains must always cope with varied and ever-evolving production contexts. Good wine yeasts must feature both good primary traits, which are related to the overall fermentative fitness of the strain, and secondary traits, which provide accessory features augmenting its technological value. In this context, the superiority of "blind," genetic improvement techniques, as those based on the direct selection of the desired phenotype without prior knowledge of the genotype, was widely proven. Blind techniques such as adaptive evolution strategies were implemented for the enhancement of many traits of interest in the winemaking field. However, these strategies usually focus on single traits: this possibly leads to genetic tradeoff phenomena, where the selection of enhanced secondary traits might lead to sub-optimal primary fermentation traits. To circumvent this phenomenon, we applied a multi-step and strongly directed genetic improvement strategy aimed at combining a strong fermentative aptitude (primary trait) with an enhanced production of glutathione (secondary trait). We exploited the random genetic recombination associated to a library of 69 monosporic clones of strain UMCC 855 (Saccharomyces cerevisiae) to search for new candidates possessing both traits. This was achieved by consecutively applying three directional selective criteria: molybdate resistance (1), fermentative aptitude (2), and glutathione production (3). The strategy brought to the selection of strain 21T2-D58, which produces a high concentration of glutathione, comparable to that of other glutathione high-producers, still with a much greater fermentative aptitude.