Giovedì 7 Ottobre 2021, alle ore 10, il Prof. Emilio Barucci presenterà i risultati della ricerca Assetti proprietari e corporate governance nel nuovo millennio durante il convegno “Consigli di amministrazione e assetti proprietari delle società quotate” organizzato dal Consiglio Nazionale dell’Economia e del Lavoro (CNEL).
Il seminario potrà essere seguito in diretta al link https://youtu.be/DXsdcRbtFSk .
| Session 1 | 09:00 | Stefano Polo, Roberto Daluiso, Emanuele Nastasi and Andrea Pallavicini | Reinforcement Learning for Options on Target Volatility Funds |
| 09:25 | Edoardo Vittori, Michele Trapletti and Marcello Restelli | Option Hedging with Risk Averse Reinforcement Learning | |
| 09:50 | Carlo Sala, Tristan Cazenave and Timothee Sohm-Queron | Neural Network for Volatility Surfaces under Convex Constraint | |
| Break | 10:15 | ||
| Invited | 10:30 | TOMASO ASTE | Deep learning the limit order book: what machines can learn and what can we learn from them? |
| Session 2 | 11:30 | Riccardo Aiolfi, Nicola Moreni, Marco Bianchetti, Marco Scaringi and Filippo Fogliani | Learning Bermudans |
| 11:55 | Pietro Rossi, Marco Bianchetti, Giovanni Amici, Alessio Peroni, Federico Brina and Matteo Mezzetti | Learning Derivatives Without Calibration | |
| 12:20 | Roberto Daluiso, Emanuele Nastasi, Andrea Pallavicini and Giulio Sartorelli | Pricing Swing Options by Reinforcement Learning | |
| Lunch | 12:45 | ||
| Invited | 14:00 | GEORGIOS SERMPINIS | Functional False Discovery Rate in Mutual Fund Performance |
| Session 3 | 15:00 | Fabio Verona and Gonçalo Faria | Frequency-domain information for active portfolio management |
| 15:25 | Edoardo Vittori, Amarildo Likmeta and Marcello Restelli | Monte Carlo Tree Search for Trading and Hedging | |
| 15:50 | Karoline Bax, Özge Sahin, Claudia Czado and Sandra Paterlini | ESG, Risk and dependence: an empirical investigation | |
| Break | 16:15 | ||
| Session 4 | 16:45 | Paul Hager, Christian Bayer, Sebastian Riedel and John Schoenmakers | Optimal Stopping with Signatures |
| 17:10 | Niklas Bussmann, Roman Enzmann, Paolo Giudici and Emanuela Raffinetti | An extension of the Shapley-Lorenz decomposition to risk management | |
| 17:35 | Angela De Martiis, Thomas Heil and Franziska Peter | Are you a Zombie? A Supervised Learning Method to Classify Unviable Firms and Identify the Determinants | |
| 18:00 | Martino Bernasconi de Luca, Edoardo Vittori, Francesco Trovò and Marcello Restelli | Dealer Markets: a Reinforcement Learning Mean Field Approach |
| Session 5 | 09:00 | Francesco Colasanto, Luca Grilli, Domenico Santoro and Giovanni Villani | Fine-tuned AlBERTo for Stock Price Prediction: a Gibbs Sampling Approach |
| 09:25 | Luca Barbaglia, Sergio Consoli and Sebastiano Manzan | Nowcasting the economy with news during the pandemic | |
| 09:50 | Lucia Alessi, Eric Ghysels, Marco Petracco and Zhe Wang | Bitcoin and News Around the World in Twenty-Six Languages | |
| Break | 10:15 | ||
| Invited | 10:30 | EMANUELE BORGONOVO | Interpretability and Explainability Methods: Can They Help Financial Machine Learning? |
| Session 6 | 11:30 | Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini and Haitz Saez de Ocariz Borde | Interpretability in deep learning for finance: a case study for the Heston model |
| 11:55 | Leandro Sánchez-Betancourt, Álvaro Cartea and Imanol Perez Aribas | Optimal Execution of Foreign Securities: A Double-Execution Problem with Signatures and Machine Learning | |
| 12:20 | Giovanni Rabitti, Emanuele Borgonovo and Elmar Plischke | Higher order Shapley effects for global sensitivity analysis of extremes | |
| Lunch | 12:45 | ||
| Invited | 14:00 | JURI MARCUCCI | Using Twitter for Macroeconomic Indicators |
| Session 7 | 15:00 | Piotr Wójcik and Bedil Karimov | Identification of scams in Initial Coin Offerings with machine learning |
| 15:25 | Alessandro Bitetto, Paola Cerchiello, Stefano Filomeni, Alessandra Tanda and Barbara Tarantino | Machine Learning and Credit Risk: Empirical Evidence from SMEs | |
| 15:50 | Ajit Desai and James Chapman | Macroeconomic Predictions using Payments Data and Machine Learning | |
| Break | 16:15 | ||
| Session 8 | 16:45 | Emilio Barucci, Michele Bonollo, Federico Poli and Edit Rroji | A machine learning algorithm for stock picking built on information based outliers |
| 17:10 | Luca Sitzia, Roberto Baccaglini, Vittorio Malacchia and Federico Cozzi | A Neural Network Approach for the Estimation of Mortgage Prepayment Rates | |
| 17:35 | Michele Azzone, Roberto Baviera and Pietro Manzoni | Neural Network Middle-Term Probabilistic Forecasting of Daily and hourly Power Consumption | |
| 18:00 | Michele Azzone, Emilio Barucci, Giancarlo Giuffra and Daniele Marazzina | A Machine Learning Model for Lapse Prediction in Life Insurance Contracts |
Minicourses:
Albert Diaz Guilera, Universitat de Barcelona (10 hours): Complex networks foundations
Tomaso Aste UCL, London (6 hours): Information filtering networks for socio-economic systems
Matteo Matteucci, Politecnico di Milano (10 hours): Introduction to neural networks: from theory to practice
Josef Teichmann, ETH Zurich (6 hours) Provable Machine Learning Techniques in Finance.
Lectures:
Christoffer Kok, European Central Bank, Contagion modelling at the ECB: analytical frameworks and policy usage
Paolo Giudici, Università di Pavia, Explanable AI credit risk models for peer to peer lending.
Andrea Prampolini, Intesa Sanpaolo, Limit order book simulation with interactive agents
Giuseppe Bruno, Banca d’Italia, Anomaly Detection in RTGS Systems: Performance Comparisons Between Shallow and Deep Neural Networks
Michele Tumminello, Università di Palermo, Insurance fraud detection: a statistically validated network approach
Daniele Marazzina, Politecnico di Milano, A machine learning model for lapse prediction in the life insurance contracts
Marcello Restelli, Politecnico di Milano, Reinforcement Learning for Automated Trading
OCF, l’Organismo di vigilanza e tenuta dell’albo unico dei Consulenti Finanziari, in collaborazione con QFinLab – Politecnico di Milano, lancia il progetto di informazione “Informarsi Conviene”, mirato a diffondere nozioni basilari, considerazioni generali e qualche consiglio pratico, per agevolare un avvicinamento ampio ai servizi di investimento da parte dei risparmiatori.
