Nov 09 2020
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
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.
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