1 code implementation • 28 Mar 2024 • Thomas Niedermayer, Pietro Saggese, Bernhard Haslhofer
In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform.
no code implementations • 23 Mar 2024 • Junliang Luo, Stefan Kitzler, Pietro Saggese
To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label.
no code implementations • 28 Sep 2023 • Pietro Saggese, Esther Segalla, Michael Sigmund, Burkhard Raunig, Felix Zangerl, Bernhard Haslhofer
In this paper, we propose an approach to assess the solvency of a VASP by cross-referencing data from three distinct sources: cryptoasset wallets, balance sheets from the commercial register, and data from supervisory entities.
no code implementations • 22 Sep 2021 • Pietro Saggese, Alessandro Belmonte, Nicola Dimitri, Angelo Facchini, Rainer Böhme
We begin by showing that a considerable difference appears between arbitrageurs when indicators of their expertise are taken into account.
no code implementations • 30 Mar 2021 • Viktorija Dudjak, Diana Neves, Tarek Alskaif, Shafi Khadem, Alejandro Pena-Bello, Pietro Saggese, Benjamin Bowler, Merlinda Andoni, Marina Bertolini, Yue Zhou, Blanche Lormeteau, Mustafa A. Mustafa, Yingjie Wang, Christina Francis, Fairouz Zobiri, David Parra, Antonios Papaemmanouil
In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources.