A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study predicts firm defaults using machine learning on Italian credit data.
Paper uses ML to predict SME defaults with interpretability.
Meta-learning framework for credit risk assessment of SMEs, aligning financial statement dates with evaluation dates.
This dataset contains the annual aggregated income taxes of all the Italian municipalities over the years 2007-2011. Data are clustered over the Italian regions and provinces. The source of the data is the Italian Ministry of Economics and Finance. The administrative variations in Italy over the quinquennium have been …
We propose a novel approach and an empirical procedure to test direct contagion of growth rate in a trade credit network of firms. Our hypotheses are that the use of trade credit contributes to contagion (from many customers to a single supplier - "many to one" contagion) and amplification (through their interaction wi…
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two propert…
Develops a framework to assess systemic risk in the economy using bank-firm network data.
Using a data set which includes all transactions among banks in the Italian money market, we study their trading strategies and the dependence among them. We use the Fourier method to compute the variance-covariance matrix of trading strategies. Our results indicate that well defined patterns arise. Two main communitie…
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.
This paper explores a real-world fundamental theme under a data science perspective. It specifically discusses whether fraud or manipulation can be observed in and from municipality income tax size distributions, through their aggregation from citizen fiscal reports. The study case pertains to official data obtained fr…
In this paper, we empirically study models for pricing Italian sovereign bonds under a reduced form framework, by assuming different dynamics for the short-rate process. We analyze classical Cox-Ingersoll-Ross and Vasicek multi-factor models, with a focus on optimization algorithms applied in the calibration exercise. …
The yearly aggregated tax income data of all, more than 8000, Italian municipalities are analyzed for a period of five years, from 2007 to 2011, to search for conformity or not with Benford's law, a counter-intuitive phenomenon observed in large tabulated data where the occurrence of numbers having smaller initial digi…
Covid lockdown increased interest in Italian stock market, leading to new investors.
In recent years, the interest in Big Data sources has been steadily growing within the Official Statistic community. The Italian National Institute of Statistics (Istat) is currently carrying out several Big Data pilot studies. One of these studies, the ICT Big Data pilot, aims at exploiting massive amounts of textual …
We investigate the shape of the Italian personal income distribution using microdata from the Survey on Household Income and Wealth, made publicly available by the Bank of Italy for the years 1977--2002. We find that the upper tail of the distribution is consistent with a Pareto-power law type distribution, while the r…
In this paper we describe three stochastic models based on a semi-Markov chains approach and its generalizations to study the high frequency price dynamics of traded stocks. The three models are: a simple semi-Markov chain model, an indexed semi-Markov chain model and a weighted indexed semi-Markov chain model. We show…
The paper predicts financial markets using news text and semantic network analysis.
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In …
The present work constitutes the second part of a two-paper project that, in particular, deals with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Veron…
This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are consid…
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterparties based on a network characterization of clearing members, whose links correspond to direct credits …
Method debiases alternative data for fair credit underwriting.
This is my master thesis. Unfortunately it is written in Italian, but maybe somebody will find it helpful when it comes to Evans Potentials
Large corporate credit models may be adapted for small business risk assessment.
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
CCR-CNN uses CNN to predict corporate credit ratings from financial data.
Framework integrates financial and annual report data for better corporate credit ratings.
Background. In Italy, in recent years, vaccination coverage for key immunizations as MMR has been declining to worryingly low levels. In 2017, the Italian Gov't expanded the number of mandatory immunizations introducing penalties to unvaccinated children's families. During the 2018 general elections campaign, immunizat…
A new algorithm improves credit scoring accuracy for imbalanced data.
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
AI improves MSME credit scoring using bank statement data.
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
We study the gap between the state pension provided by the Italian pension system pre-Dini reform and post-Dini reform. The goal is to fill the gap between the old and the new pension by joining a defined contribution pension scheme and adopting an optimal investment strategy that is target-based. We find that it is po…
We analyze the data of the Italian and U.S. futures on the stock markets and we test the validity of the Continuous Time Random Walk assumption for the survival probability of the returns time series via a renewal aging experiment. We also study the survival probability of returns sign and apply a coarse graining proce…
The paper analyzes Lending Club's loan applicants to predict default risk.
Study integrates climate and text data to improve credit default prediction.
Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
Paper uses LightGBM for mobile user credit assessment.
Big data from phone calls improves credit scoring models and profits.
Synthetic data improves credit scoring models' performance without compromising borrower privacy.
This paper builds a machine learning model to predict credit defaults for unsecured lending.
Paper proposes an intelligent credit limit management system using causal inference.
NetDP predicts loan defaults using network data, addressing cold-start issues.