Agenda
Machine Learning for Finance
Aula Informatica, Plesso C2 Campus San Giobbe, Venezia
Programme:
10:00 Opening addresses
Monica Billio, Head of the Department of Economics (Ca' Foscari University of Venice, Italy)
Marco Corazza (Ca’ Foscari University of Venice, Italy)
10:15 A comparison of Reinforcement Learning algorithms performances in financial trading systems
Corazza Marco, Fasano Giovanni, Gusso Riccardo, Pesenti Raffaele (Ca' Foscari University of Venice, Italy)
10:45 Machine Learning, pricing and risk measures
Cocco Flavio, Rossi Pietro (Prometeia, Italy)
- coffee break -
11:45 Forecasting benchmarks of long-term stock returns via Machine Learning
Kyriakou Ioannis, Parastoo Mousavi, Jens Perch Nielsen (Cass Business School, University Of London, United Kingdom), Michael Scholz (University of Graz, Austria)
12:15 Stock price forecasting over adaptive timescale using Supervised Learning and Receptive Fields
Cimino Mario, Monaco Manilo, Vaglini Gigliola (University of Pisa, Italy)
- lunch –
14:00 Artificial Intelligence, data, ethics: an holistic approach for risks and regulation
Bogroff Alexis, Guegan Dominique (Université Paris 1 Panthéon-Sorbonne, France)
14:30 An investigation of money laundering determinants with Object Oriented Bayesian Networks
De Giuli Maria Elena (University of Pavia, Italy), Resta Marina (University Of Genoa, Italy)
- coffee break –
15:30 Bayesian Global Optimization for Automated Machine Learning in Finance
Archetti Francesco (Consorzio Milano Ricerche, Italy), Candelieri Antonio (University of Milan, Italy)
16:00 Machine Learning for pricing American options in high dimension
Goudenège Ludovic, Molent Andrea, Zanette Antonino (University of Udine, Italy)
16:30 Closing remarks
^^^
The attendance is free, but for organizational and space reasons, it is absolutely necessary to communicate the participation to the organizer: prof. Marco Corazza – corazza@unive.it
Lingua
L'evento si terrà in italiano
Organizzatore
Dipartimento di Economia, Centro VERA
Allegati
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Abstracts of papers | 65 KB |
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Locandina | 1757 KB |