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.
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
Word2Vec embedding for Italian language developed.
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 …
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…
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…
Study predicts firm defaults using machine learning on Italian credit data.
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. …
Covid lockdown increased interest in Italian stock market, leading to new investors.
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…
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…
Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.
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…
This is my master thesis. Unfortunately it is written in Italian, but maybe somebody will find it helpful when it comes to Evans Potentials
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
QUACKIE creates a new benchmark for NLP interpretability.
This study uses NLP to detect financial risks from documents.
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…
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…
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…
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 …
TX-Ray analyzes and quantifies model knowledge transfer in NLP.
New measure corrects news bias in NLP stock return forecasting.
This paper analyzes crypto white papers under MiCAR, highlighting NLP's role.
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…
This paper improves NLP interpretability by using sentence segments instead of words.
SusGen-GPT improves financial NLP and ESG report generation.
MDA optimizer performs similarly to SGD+M in CV and Adam in NLP.
We introduce HUBERT which combines the structured-representational power of Tensor-Product Representations (TPRs) and BERT, a pre-trained bidirectional Transformer language model. We show that there is shared structure between different NLP datasets that HUBERT, but not BERT, is able to learn and leverage. We validate …
The article describes the algorithm used to define the electricity price in day-ahead and itraday energy markets in Italy. Details of Matlab implementation of one of its simplified versions, capable of producing good results in a extremely short time, are then provided and numerical results are discussed.
The paper predicts financial markets using news text and semantic network analysis.
Survey on using large models to train smaller datasets in NLP.
New pruning method retains model expressiveness for NLP tasks.
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 study uses Tsallis entropy to analyze diversification and integration in Italian stock market companies.
BERT outperforms traditional machine learning in text classification tasks.
Hungarian text processing improved with efficient, accurate NLP pipelines.
The Hype Index measures media attention to equities using NLP.
HuSpaCy offers an industrial-grade Hungarian NLP toolkit.
The paper improves cryptocurrency price forecasting using deep learning and NLP on financial, blockchain, and social media data.
Paper uses ML to predict SME defaults with interpretability.
COCKATIEL explains neural net models on NLP tasks by identifying meaningful concepts.
This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel. The work defines a novel feature inspired by Natural Language Processing (NLP) that allows existing NLP techniques to help the traffic classification. The…
This paper explores how NLP enhances insurance data analysis.
Study improves cryptocurrency price prediction using unlabeled text data.
NLP techniques improve drug discovery by analyzing chemical and protein text.
Italy's vaccine coverage fell, leading to political debates and online social media discussions.
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…
Framework quantifies financial NLP robustness under regime shifts.