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Big Data and Machine Learning in Finance Conference

Politecnico di Milano

June 10, 2021 – June 11, 2021

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

EMFI

EmFi – a special purpose virtual currency

Deadline extended – Polimi Summer School

We are glad to inform you that the summer school

From Networks to Neural Networks in Finance

14-18 June, 2021

organized by the QFinLab research group at Politecnico di Milano, jointly with the Lake Como School of Advanced Studies and Fondazione Alessandro Volta,

has extended the application deadline to May 24th 2021

Because of Covid-19 restrictions, the School will be held online

Registration fees: 150 EUR, VAT 22% included

The School aims to present the state of the art on methodologies and applications of Neural Networks and Nets to finance. The expected audience of the school is provided by PhD students and young researchers interested in applications of ML techniques to finance.People from industry are also welcome.

The school is organized through mini-courses, lectures and workshops and aims at introducing complex networks, neural networks, information filtering networks and their applications to socio-economic systems.

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

For detailed information visit the Summer School website.

OCF in collaborazione con QFinLab lancia InformarsiConviene

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.