Application of Deep Learning techniques in stock ranking for different horizon returns

One of the approaches portfolio managers commonly use to build portfolios, is to rank the underlying assets based on the prediction for the stock returns, as well as other aspects of the portfolio such as the portfolio risk. In this project we aim to apply different deep learning techniques to the problem of stock ranking. The features we want to use to train our models are mainly derived from fundamental company data including quarterly and annual filings of the publicly traded companies. We are planning to investigate the effectiveness of different methods and potentially try Auto Encoder + 1D CNN and different types of RNNS and LSTMS.

Intern: 
Arnold Mo
Superviseur universitaire: 
Ioannis Mitliagkas
Province: 
Quebec
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