A FINancial supervision and TECHnology compliance training programme

Rezultaty

UNIPV through ABI Lab will provide an intermediate evaluation report on the risk management methodologies developed in the project, based on all received feedbacks, from supervisors, fintechs and banks.

Financial output

Financial reporting to the partners and to the EC.

Technical output

Integration of project deliverables: slides, use cases, feedback and evaluation reports.

Final Evaluation report

ASE Bucuresti, based on the information received from all partners, will provide a final evaluation report on the risk management methodologies developed in the project, based on all received feedbacks, from supervisors, fintechs and banks. This includes the feedbacks collected from the participants to SupTech and RegTech workshops.

Advisory Board report

The Advisory Board composed by five nonEuropean experts after receiving all the project deliverables as well as the feedbacks given by the participants to the project events will provide a final evaluation report

Network Establishment

Establishment of the FIN-TECH network and of the Advisory Board;

Repository of use cases and slides in blockchain

Repository of use cases (including paper, data and code) and slides in blockchain shared during the Suptech and RegTech workshops.

Repository of use cases and slides in artificial intelligence

Repository of use cases and slides (including paper, data and code) in artificial intelligence shared during the Suptech and RegTech workshops.

Repository of research consortium papers (BDA)

Repository of research consortium papers from the Big Data Analytics research

Event feedback repository

Firamis (M1-M15) and ASE Bucuresti (M16-end of the project) is responsible for collecting and sharing feedbacks from the participants to SupTech and RegTech workshops.

Repository of research consortium papers (AI)

Repository of research consortium papers from the Artificial Intelligence research

Research and development environment

The creation of a coding technical infrastructure that is scalable and extendable in a modular approach. The basis for the infrastructure will be open-source projects like R which gives access to developed machine learning projects like Tensorflow, PyTorch, MXNet and H2O. These research and development environments will be made available in a dedicated cloud server environment to manage the code, scripts, GUIs, models, users’ access rights, software interaction and workflows.

Repository of use cases and slides in big data analytics

Repository of use cases (including paper, data and code) and slides in big data analytics shared during the Suptech and RegTech workshops.

Establishment of website and social media channels.
Repository of research consortium papers (BC)

Repository of research consortium papers from Blockchain research

Event participation repository

In this task the work package leader (Firamis M1-M15, ASE Bucuresti M16-end of the project) will promote and monitor the participation of all project participants to conference, workshops and professional events, on the project topics, and the related publications in international scientific journals. Specifically, Firamis is responsible for collecting and sharing updates on participations to conferences and research papers by the project network participants.

Publikacje

The Cost of Bitcoin Mining Has Never Really Increased

Autorzy: Yo-Der Song, Tomaso Aste
Opublikowane w: Frontiers in Blockchain, Numer 3, 2020, ISSN 2624-7852
Wydawca: Frontiers
DOI: 10.3389/fbloc.2020.565497

Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Autorzy: Z. Keskin, T. Aste
Opublikowane w: Royal Society Open Science, Numer 7/9, 2020, Strona(/y) 200863, ISSN 2054-5703
Wydawca: Royal Society
DOI: 10.1098/rsos.200863

COVID-19 contagion and digital finance

Autorzy: Arianna Agosto, Paolo Giudici
Opublikowane w: Digital Finance, Numer 2/1-2, 2020, Strona(/y) 159-167, ISSN 2524-6984
Wydawca: Springer Pub. Co.
DOI: 10.1007/s42521-020-00021-3

Interpretable Machine Learning for Diversified Portfolio Construction

Autorzy: Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, Peter Schwendner
Opublikowane w: The Journal of Financial Data Science, 2020, Strona(/y) jfds.2021.1.066, ISSN 2640-3943
Wydawca: Institutional Investor Journals Umbrella
DOI: 10.3905/jfds.2021.1.066

Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

Autorzy: Andreas G. F. Hoepner, David McMillan, Andrew Vivian, Chardin Wese Simen
Opublikowane w: The European Journal of Finance, Numer 27/1-2, 2021, Strona(/y) 1-7, ISSN 1351-847X
Wydawca: Chapman & Hall
DOI: 10.1080/1351847x.2020.1847725

Latent factor models for credit scoring in P2P systems

Autorzy: Daniel Felix Ahelegbey, Paolo Giudici, Branka Hadji-Misheva
Opublikowane w: Physica A: Statistical Mechanics and its Applications, Numer 522, 2019, Strona(/y) 112-121, ISSN 0378-4371
Wydawca: Elsevier BV
DOI: 10.1016/j.physa.2019.01.130

Cyber risk ordering with rank-based statistical models

Autorzy: Paolo Giudici, Emanuela Raffinetti
Opublikowane w: AStA Advances in Statistical Analysis, 2020, ISSN 1863-8171
Wydawca: Springer Pub. Co.
DOI: 10.1007/s10182-020-00387-0

Neural networks and arbitrage in the VIX

Autorzy: Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer
Opublikowane w: Digital Finance, Numer 2/1-2, 2020, Strona(/y) 97-115, ISSN 2524-6984
Wydawca: Springer Pub. Co.
DOI: 10.1007/s42521-020-00026-y

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Autorzy: Yanka Aleksandrova
Opublikowane w: TEM Journal, 2021, Strona(/y) 133-143, ISSN 2217-8333
Wydawca: UIKTEN - Association for Information Communication Technology Education and Science,Serbia.
DOI: 10.18421/tem101-16

Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions

Autorzy: Miller-Janny Ariza-Garzón, María-Del-Mar Camacho-Miñano, María-Jesús Segovia-Vargas, Javier Arroyo
Opublikowane w: Electronic Commerce Research and Applications, Numer 49, 2021, Strona(/y) 101079, ISSN 1567-4223
Wydawca: Elsevier BV
DOI: 10.1016/j.elerap.2021.101079

Libra or Librae? Basket based stablecoins to mitigate foreign exchange volatility spillovers

Autorzy: Paolo Giudici, Thomas Leach, Paolo Pagnottoni
Opublikowane w: Finance Research Letters, 2021, Strona(/y) 102054, ISSN 1544-6123
Wydawca: Elsevier BV
DOI: 10.1016/j.frl.2021.102054

Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

Autorzy: by Audrius Kabašinskas and Kristina Šutienė
Opublikowane w: Entropy, Numer 23(4), 2021, ISSN 1099-4300
Wydawca: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/e23040455

Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Autorzy: Jochen Papenbrock, Peter Schwendner, Markus Jaeger, Stephan Krügel
Opublikowane w: The Journal of Financial Data Science, Numer 3/2, 2021, Strona(/y) 51-69, ISSN 2640-3943
Wydawca: Institutional Investor Journals Umbrella}
DOI: 10.3905/jfds.2021.1.056

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

Autorzy: Paolo Giudici, Branka Hadji-Misheva, Alessandro Spelta
Opublikowane w: Frontiers in Artificial Intelligence, Numer 2, 2019, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2019.00003

High Frequency Price Change Spillovers in Bitcoin Markets

Autorzy: Giudici, Pagnottoni
Opublikowane w: Risks, Numer 7/4, 2019, Strona(/y) 111, ISSN 2227-9091
Wydawca: MDPI
DOI: 10.3390/risks7040111

Sentiment Analysis of European Bonds 2016–2018

Autorzy: Peter Schwendner, Martin Schüle, Martin Hillebrand
Opublikowane w: Frontiers in Artificial Intelligence, Numer 2, 2019, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2019.00020

Fintech Risk Management: A Research Challenge for Artificial Intelligence in Finance

Autorzy: Paolo Giudici
Opublikowane w: Frontiers in Artificial Intelligence, Numer 1, 2018, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2018.00001

Lead Behaviour in Bitcoin Markets

Autorzy: Ying Chen, Paolo Giudici, Branka Hadji Misheva, Simon Trimborn
Opublikowane w: Risks, Numer 8/1, 2020, Strona(/y) 4, ISSN 2227-9091
Wydawca: MDPI
DOI: 10.3390/risks8010004

Spatial Regression Models to Improve P2P Credit Risk Management

Autorzy: Arianna Agosto, Paolo Giudici, Tom Leach
Opublikowane w: Frontiers in Artificial Intelligence, Numer 2, 2019, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2019.00006

Can Cryptocurrencies Preserve Privacy and Comply With Regulations?

Autorzy: Geoff Goodell, Tomaso Aste
Opublikowane w: Frontiers in Blockchain, Numer 2, 2019, ISSN 2624-7852
Wydawca: Frontiers
DOI: 10.3389/fbloc.2019.00004

Cryptocurrency market structure: connecting emotions and economics

Autorzy: Tomaso Aste
Opublikowane w: Digital Finance, Numer 1/1-4, 2019, Strona(/y) 5-21, ISSN 2524-6984
Wydawca: Springer Verlag
DOI: 10.1007/s42521-019-00008-9

A Decentralised Digital Identity Architecture

Autorzy: Goodell, Geoff; Aste, Tomaso
Opublikowane w: Frontiers in Blockchain , 2 , Article 17. (2019), Numer 1, 2019, ISSN 2624-7852
Wydawca: Frontiers
DOI: 10.3389/fbloc.2019.00017

Crypto price discovery through correlation networks

Autorzy: Paolo Giudici, Gloria Polinesi
Opublikowane w: Annals of Operations Research, 2019, ISSN 0254-5330
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s10479-019-03282-3

Analysing Social Media Forums to Discover Potential Causes of Phasic Shifts in Cryptocurrency Price Series

Autorzy: Andrew Burnie, Emine Yilmaz, Tomaso Aste
Opublikowane w: Frontiers in Blockchain, Numer 3, 2020, ISSN 2624-7852
Wydawca: Frontiers
DOI: 10.3389/fbloc.2020.00001

Initial Coin Offerings: Risk or Opportunity?

Autorzy: Anca Mirela Toma, Paola Cerchiello
Opublikowane w: Frontiers in Artificial Intelligence, Numer 3, 2020, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2020.00018

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Autorzy: Miller Janny Ariza-Garzon, Javier Arroyo, Antonio Caparrini, Maria-Jesus Segovia-Vargas
Opublikowane w: IEEE Access, Numer 8, 2020, Strona(/y) 64873-64890, ISSN 2169-3536
Wydawca: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2020.2984412

Assessment of Machine Learning Performance for Decision Support in Venture Capital Investments

Autorzy: Javier Arroyo, Francesco Corea, Guillermo Jimenez-Diaz, Juan A. Recio-Garcia
Opublikowane w: IEEE Access, Numer 7, 2019, Strona(/y) 124233-124243, ISSN 2169-3536
Wydawca: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2019.2938659

A Statistical Classification of Cryptocurrencies

Autorzy: Pele, D.T., Wesselhöfft, N., Härdle, W.K., Kolossiatis, M., Yatracos, Y.
Opublikowane w: Journal of Empirical Finance, Numer 5 per year, 2020, Strona(/y) under review to this journal, ISSN 0927-5398
Wydawca: Elsevier BV

Will they repay their debt? Identification of borrowers likely to be charged off

Autorzy: Caplescu, RD., Panaite, AM., Pele, DT, Strat, VA.
Opublikowane w: Management & Marketing. Challenges for the Knowledge Society, Numer 4 per year, 2020, Strona(/y) is under review to the mentioned journal, ISSN 2069-8887
Wydawca: Editura Economica

Fin vs. tech: are trust and knowledge creation key ingredients in fintech start-up emergence and financing?

Autorzy: Theodor Florian Cojoianu, Gordon L. Clark, Andreas G. F. Hoepner, Vladimir Pažitka, Dariusz Wójcik
Opublikowane w: Small Business Economics, 2020, ISSN 0921-898X
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s11187-020-00367-3

Peer-to-peer loan acceptance and default prediction with artificial intelligence

Autorzy: J. D. Turiel, T. Aste
Opublikowane w: Royal Society Open Science, Numer 7/6, 2020, Strona(/y) 191649, ISSN 2054-5703
Wydawca: The Royal Society
DOI: 10.1098/rsos.191649

On the Improvement of Default Forecast Through Textual Analysis

Autorzy: Paola Cerchiello, Roberta Scaramozzino
Opublikowane w: Frontiers in Artificial Intelligence, Numer 3, 2020, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2020.00016

Network Models to Enhance Automated Cryptocurrency Portfolio Management

Autorzy: Paolo Giudici, Paolo Pagnottoni, Gloria Polinesi
Opublikowane w: Frontiers in Artificial Intelligence, Numer 3, 2020, ISSN 2624-8212
Wydawca: Frontiers
DOI: 10.3389/frai.2020.00022

Investing with cryptocurrencies – evaluating their potential for portfolio allocation strategies

Autorzy: Alla Petukhina, Simon Trimborn, Wolfgang Karl Härdle, Hermann Elendner
Opublikowane w: Quantitative Finance, 2021, Strona(/y) 1-29, ISSN 1469-7688
Wydawca: Institute of Physics Publishing
DOI: 10.1080/14697688.2021.1880023

Shapley-Lorenz eXplainable Artificial Intelligence

Autorzy: Paolo Giudici, Emanuela Raffinetti
Opublikowane w: Expert Systems with Applications, Numer 167, 2021, Strona(/y) 114104, ISSN 0957-4174
Wydawca: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2020.114104

Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies

Autorzy: Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl Härdle
Opublikowane w: The European Journal of Finance, Numer 27/1-2, 2021, Strona(/y) 8-30, ISSN 1351-847X
Wydawca: Chapman & Hall
DOI: 10.1080/1351847x.2020.1789684

Predictability and pricing efficiency in forward and spot, developed and emerging currency markets

Autorzy: Valerio Potì, Richard Levich, Thomas Conlon
Opublikowane w: Journal of International Money and Finance, Numer 107, 2020, Strona(/y) 102223, ISSN 0261-5606
Wydawca: Pergamon Press Ltd.
DOI: 10.1016/j.jimonfin.2020.102223

Evaluation of multi-asset investment strategies with digital assets

Autorzy: Alla Petukhina, Erin Sprünken
Opublikowane w: Digital Finance, Numer 3/1, 2021, Strona(/y) 45-79, ISSN 2524-6984
Wydawca: Springer Verlag
DOI: 10.1007/s42521-021-00031-9

Explainable Machine Learning in Credit Risk Management

Autorzy: Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock
Opublikowane w: Computational Economics, 2020, ISSN 0927-7099
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s10614-020-10042-0

Default count-based network models for credit contagion

Autorzy: Arianna Agosto, Daniel Felix Ahelegbey
Opublikowane w: Journal of the Operational Research Society, 2020, Strona(/y) 1-14, ISSN 0160-5682
Wydawca: Palgrave Macmillan Ltd.
DOI: 10.1080/01605682.2020.1776169

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