Leveraging Deep Learning in Asset Pricing in a Multi-Factor Modelling Framework

Providing relevant quantitative trading strategies requires obtaining financial data from multiple sources to obtain market information and then use this data to model outcome. One difficulty in this process is that data entry is done by financial analysts who spend a large portion of their activities entering data from PDF to an application. This project seek to improve data collection in Canada by automating the process and focus analysts on their core competencies. We aim to directly connect our data sources with an innovative AI-powered solution that systematically reads, treats, structures and transforms raw information into standard models. Machine learning techniques will be used to automatically generate normalized financial information and ratios. In order to move from factor modeling using only individual factors we make use of deep learning algorithms, we believe that a system that will analyze historical financial market data and create a “best-worst” stock classification for specific market using multi-factor model is achievable.

Intern: 
Blaise Gauvin St-Denis
Superviseur universitaire: 
Ioannis Mitliagkas
Province: 
Quebec
Partner University: 
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