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 quantitativo 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.

List

I seminari confluiscono nelle liste di seminari dipartimentali.

I seminari su tematiche Fintech confluiscono anche nelle iniziative della Fintech Research Network.

Workshops

• Jun 10 2021 Conference Big Data and Machine Learning in Finance www.mate.polimi.it/fintech 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). Invited speakers: 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 2021 Polimi Fintech Series – Valerio Potì – March 22, 2021 The Polimi Fintech Series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, presents   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 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.   Stay updated with latest news on QFinLab seminars subscribing here: https://bit.ly/2TNtC6e   Information about future seminars: www.qfinlab.polimi.it/all-news

• Feb 22 2021 Polimi Fintech Series – Charalampos Stasinakis – February 22, 2021 The Polimi Fintech Series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, presents   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 Abstract 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.   Stay updated with latest news on QFinLab seminars subscribing here: https://bit.ly/2TNtC6e   Information about future seminars: www.qfinlab.polimi.it/all-news

• Jan 18 2021 Polimi Fintech Series – Michele Azzone – January 18, 2021 The Polimi Fintech Series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, presents   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 Abstract: 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.   Stay updated with latest news on QFinLab seminars subscribing here: https://bit.ly/2TNtC6e   Information about future seminars: www.qfinlab.polimi.it/all-news

• Dec 09 2020 Polimi Fintech Series – Jeremy D. Turiel – December 09, 2020 The Polimi Fintech Series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, presents   December 9th, 2020 - 17.30 (CET)   Virtual room: Click here to access the Zoom Virtual Room, or insert the Meeting id on your Zoom app: 826 4084 3401   J. D. Turiel (UCL-ICL, Barclays Investment Bank) with A. Briola and T. Aste   DEEP LEARNING MODELLING OF THE LIMIT ORDER BOOK: A COMPARATIVE PERSPECTIVE  Abstract: We address theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space, and dataset and clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modelling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book’s dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB’s dynamics, but not necessarily the true underlying dimensions.   Click here to download the paper   Stay updated with latest news on QFinLab seminars subscribing here: https://bit.ly/2TNtC6e   Information about future seminars: www.qfinlab.polimi.it/all-news