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Polimi Fintech Series – Jeremy D. Turiel – December 09, 2020

Dec 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
 
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Information about future seminars: www.qfinlab.polimi.it/all-news

Polimi Fintech Series – Emilio Barucci – November 09, 2020

Nov 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
 

Polimi Fintech Series

Nov 09 2020
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.

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 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.
 
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Conference Big Data and Machine Learning in Finance

Jun 10 2021

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

Seminar Niklas Wagner – May 12, 2020

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.

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