Abu Dhabi Machine Learning Season 2 Episode 2

 09.11.2021 -  Abu Dhabi Machine Learning -  ~2 Minutes


  • Tuesday, November 9, 2021 from 7:00 PM to 9:00 PM (Abu Dhabi Time)


  • At your home, on zoom. We are looking at hosting in-person events (and broadcasting online on zoom). Any companies or institutions interested in hosting us?


Talk 1: Systematic Pricing and Trading of Municipal Bonds


  • Petter N. Kolm, New York University (NYU) - Courant Institute of Mathematical Sciences, Quant of the Year (The Journal of Portfolio Management)

  • Sudar Purushothaman, Credit Portfolio Manager at Foundation Credit

Abstract: In this presentation, the authors propose a systematic approach for pricing and trading municipal bonds, leveraging the feature-rich information available at the individual bond level. Based on the proposed pricing framework, they estimate several models using ridge regression and Kalman filtering. In their empirical work, they show that the models compare favorably in pricing accuracy to those available in the literature. Additionally, the models are able to quickly adapt to changing market conditions. Incorporating the pricing models into relative value trading strategies, the authors demonstrate that the resulting portfolios generate significant excess returns and positive alpha relative to the Vanguard Long-Term Tax-Exempt Fund (VWLTX), one of the largest mutual funds in the municipal space.

Keywords: Algorithmic trading, Factor models, Fixed income, Machine learning, Municipal bonds, Pricing models, Relative value, Systematic trading

Paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3899133

Their slides.

Talk 2: Graph Convolutional Networks

by Kevin O’Brien, ex-Senior Data Scientist at Careem, Head of Data Science at Finvault

Abstract: In this talk, we explore what Graph Convolutional Networks (GCNs) are, how they are derived from traditional CNNs to apply similar theory to graphs, rather than images, how they aggregate graph entities and their neighbours to produce embeddings for powerful learning, and, finally, how Kevin and his team applied them practically and successfully in Careem for fraud detection.

The slides can be found here.