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

• 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

• Nov 09 2020 Polimi Fintech Series QFinLab, the research group on Quantitative Finance - Politecnico di Milano, is pleased to announce Polimi Fintech Series.   Featuring contributions from both leading academics and practitioners, the series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, will explore challenges facing Fintech today. Guided by the expertise of QFinLab, this seminar series will provide a forum for discussion over technology applied to financial industry. To stay updated with latest news on QFinLab seminars subscribe here: https://bit.ly/2TNtC6e  The first appointment with Polimi Fintech Series, on November 9th 2020 at 17.30 Italian time,  will host Emilio Barucci (Politecnico di Milano) presenting E. Barucci (with M. Bonollo, F. Poli, E. Rroji) A machine learning algorithm for stock picking built on information based outliers  Abstract: We build an algorithm for stock selection based on indicators of time series of stocks (return, volume, volatility, bid-ask spread) that should be associated with the dissemination of private information in financial markets. We use a machine learning algorithm for the identification of the most relevant indicators for the prediction of stock returns and to define a trading strategy. The procedure combines a sequential inclusion of predictors with a classification algorithm for the trading signal. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when 5-10% transaction costs are inserted. More details about the first seminar will be announced soon. Events are open to any interested parties. Due to Covid-19 emergency, seminars will be delivered online.   Stay updated with latest news on QFinLab seminars subscribing here: https://bit.ly/2TNtC6e

• Nov 09 2020 Polimi Fintech Series – Emilio Barucci – November 09, 2020 The Polimi Fintech Series, under the fintech-ho2020.eu and the Cost Fin-AI.eu project, presents   November 9th, 2020 - 17.30 (CET)   Virtual room: Click here to access the Zoom Virtual Room, or insert the Meeting id on your Zoom app: 872 7241 8663    E. Barucci (with M. Bonollo, F. Poli, E. Rroji) A machine learning algorithm for stock picking built on information based outliers  Abstract: We build an algorithm for stock selection based on indicators of time series of stocks (return, volume, volatility, bid-ask spread) that should be associated with the dissemination of private information in financial markets. We use a machine learning algorithm for the identification of the most relevant indicators for the prediction of stock returns and to define a trading strategy. The procedure combines a sequential inclusion of predictors with a classification algorithm for the trading signal. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when 5-10% transaction costs are inserted.   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

• May 12 2020 Seminar Niklas Wagner – May 12, 2020     Niklas Wagner (Passau University) Give Me a Break: Is the Equity Premium a Trading Break Premium?   May 12, 2020 - 12.30   Abstract This paper addresses the relation between market risk and expected market returns under periodic trading breaks. We propose a model where asset prices are driven by a diffusion process that operates during the trading day and a separate process that captures overnight price changes. Our empirical analysis shows that both components are important in explaining the equity market risk premium. Trading breaks entail a lack of market functionality and liquidity and our results reveal that investors ask for a premium to hold the market portfolio overnight. Including additional state variables into the model, we find that uncertainty risk and illiquidity risk are both priced as well.