EMFI
Deadline extended – Polimi Summer School
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 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.
Digital Euro Seminars
QFinLab is pleased to announce:
Speaker
Prof. Emilio Barucci
Politecnico di Milano
Toward the digital euro
technology, banks and monetary policy
Two appointments:
APRIL 8th 2021, 17.30 CET
Zoom Virtual Room:
https://polimi-it.zoom.us/j/82532398177
ID: 825 3239 8177
APRIL 14th 2021, 17.30 CET
Zoom Virtual Room:
https://polimi-it.zoom.us/j/83251333953
ID: 832 5133 3953
Polimi Fintech Series – Valerio Potì – March 22, 2021
Valerio Potì (University College Dublin)
COVID Narrative Risk: A Computational Linguistic Approach to the Econometric Identification of Narrative Risk During the COVID-19 Pandemic
Abstract
In this paper, we study the role in financial markets of narratives related to the ongoing COVID-19 pandemic. The pandemic represents a natural setting for the development of narratives that may effects or be affected by financial markets. We thus treat the pandemic as a natural experiment on the relation between prevailing narratives and financial markets. We adopt the SIR model and natural language processing on financial newspaper news and Twitter tweets that deal at the same time with financial market topics and COVID-19 to study the dynamics and determinants of coronavirus narrative epidemics. Our aim is to establish whether there an “infodemic” develops, and whether the prevailing narrative, whether resemnbling an infodemic or otherwise, drives or is driven by financial markets developments, controlling for developments regarding the COVID-19 pandemic. We find associations between narratives about the epidemic, stock market dynamics (both regarding returns and volatility) and government responses to COVID-19. We also find that the narrative spread does resemble an infodemic, since it is well described by the SIR epidemic model. Our estimates of the shape of the narrative infodemic curves in different countries depend on whether the COVID-19 outbreak occurred early and on its severity. Negative tones and tones communicating uncertainty are prevalent in the growing stage of the infodemic. We find preliminary evidence of a causality relation between the negative and uncertainty tones of coronavirus tweets and both market return and volatility change.