L’attività seminariale del gruppo Ingegneria finanziaria si articola su diverse forme di incontro che possono avere obiettivi diversi:
- sviluppare la diffusione della ricerca su tematiche di finanza quantitativa
- diffondere studi/risultati di tipo quantiativo all’interno della comunità finanziaria
- fornire agli studenti occasioni di incontro anche di natura non tecnica su tematiche attinenti il mondo della finanza.
Le attività comprendono seminari scientifici, workshop e incontri su temi specifici, corsi di formazione.
Jun 14 2021Summer School – From Networks to Neural Networks in Finance
Lake Como School of Advanced Studies - 14-18 June 2021
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 student and young researchers interested in applications of Neural Networks and Nets to finance.
The school is organized through three different initiatives:
- Four minicourses
- Workshops with students and participants to the school on developing research ideas.
- Albert Diaz Guilera, Universitat de Barcelona (12 hours):
- Tomaso Aste UCL, London (6 hours): Information filtering networks for socio-economic systems
- Matteo Matteucci, Politecnico di Mialno (12 hours): Introduction to neural networks: from theory to practice
- Josef Teichmann, ETH Zurich (6 hours) (TBC)
- Christoffer Kok, European Central Bank, Contagion modelling at the ECB: analytical frameworks and policy usage
- Paolo Giudici, Università di Pavia, Network based credit risk models for peer to peer lending.
- Andrea Prampolini, Intesa Sanpaolo, Limit order book simulation with interactive agents
Detailed program: https://nnnf.lakecomoschool.org/program/
Dipartimento di Matematica
Politecnico di Milano
Emilio Barucci, Roberto Baviera, Daniele Marazzina.
Michele Azzone, Emilio Barucci, Roberto Baviera, Matteo Brachetta, Giancarlo Giuffra, Francesca Grassetti, Daniele Marazzina.
Jun 10 2021Conference Big Data and Machine Learning in Finance
June 10-11, 2021 - Online Conference
Big Data and Machine Learning are driving a significant transformation in the financial industry. Amazing examples include: robo-advisory; predicting frauds in payment systems; development of sophisticated algorithmic trading strategies; systemic risk assessment; rating of companies/financial products using a huge amount of information; development of chatbots for customers; nowcasting of financial time series; digital marketing; instant pricing of insurance products.
The transformation concerns the academia and the financial industry. The goal of the conference is to bring together academicians with different backgrounds (economists, finance experts, data scientists, econometricians) and representatives of the financial industry (banks, asset management, insurance companies) working in this field.
Papers on all areas dealing with Machine Learning and Big Data in finance (including Natural Language Processing and Artificial Intelligence techniques) are welcomed. The conference targets papers with different angles (methodological and applications to finance).
- Tomaso Aste (University College London)
- Emanuele Borgonovo (Università Bocconi)
- Tucker Balch (JP Morgan AI research)
- Juri Marcucci (Bank of Italy)
- Georgios Sermpinis (Adam Smith Business School, University of Glasgow)
For information: www.mate.polimi.it/fintech
Mar 22 2021Polimi Fintech Series – Valerio Potì – March 22, 2021
March 22nd, 2021 - 17.30 (CET)
Virtual room: Click here to access the Zoom Virtual Room, or insert the Meeting id on your Zoom app: 885 3010 212
Valerio Potì (University College Dublin)
COVID Narrative Risk: A Computational Linguistic Approach to the Econometric Identification of Narrative Risk During the COVID-19 Pandemic
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.
Feb 22 2021Polimi Fintech Series – Charalampos Stasinakis – February 22, 2021
February 22nd, 2021 - 17.30 (CET)
Virtual room: Click here to access the Zoom Virtual Room, or insert the Meeting id on your Zoom app: 827 9926 5984
Charalampos Stasinakis - University of Glasgow
(with G. Sermpinis)
Big Data, Artificial Intelligence and Machine Learning: A Transformative Symbiosis in Favour of Financial Technology
The financial technology revolution is a reality, as the financial world is gradually transforming into a digital domain of high-volume information and high-speed data transformation and processing. The more this transformation takes place, the more consumer and investor behaviour shifts towards a pro-technology attitude of financial services offered by market participants, financial institutions and financial technology companies. This new norm is confirming that information technology is driving innovation for financial technology. In this framework, the value of big data, artificial intelligence and machine learning techniques becomes apparent. The aim of this chapter is multi-fold. Firstly, a multidimensional descriptive analysis is shown to familiarise the reader with the extent of penetration of the above in the financial technology road-map. A short non-technical overview of the methods is then presented. Next, the impact of data analytics and relevant techniques on the evolution of financial technology is explained and discussed along with their applications’ landscape. The chapter also presents a glimpse of the shifting paradigm these techniques bring forward for several fintech related professions, while artificial intelligence and machine learning techniques are tied with the future challenges of AI ethics, regulation technology and the smart data utilisation.
Jan 18 2021Polimi Fintech Series – Michele Azzone – January 18, 2021
January 18th, 2021 - 17.30 (CET)
Virtual room: Click here to access the Zoom Virtual Room, or insert the Meeting id on your Zoom app: 824 7266 9724
M. Azzone (Politecnico di Milano)
with E. Barucci, G. Giuffra and D. Marazzina
A Machine Learning Model for Lapse Prediction in Life Insurance Contracts
In this work, we use the Random Forest methodology to predict the lapse decision of life contracts by policyholders. The methodology outperforms the classical logistic model in describing the phenomenon. We use global and local interpretability tools to investigate how the model works. We show that non economic features (time passed from the incipit of the contract and the time to expiry, as well as the insurance company) play a significant effect in determining the lapse decision while economic/financial features (except the disposable income growth rate) play a limited effect. The analysis shows that linear models, such as the logistic model, may not be adequate to capture the heterogeneity of financial decisions.