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
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.