Abstract: 

The complexity of global supply chain dataset renders itself a rich minefield for quantitative analysis and data science. Investors are looking to systematically integrate supply chain information into their decision process as the data becomes more comprehensive in coverage of historical global relationships. We focus on a quantitative examination of the structure of the supply chain network and its combinations with market dynamics. Specifically, we propose a variety of company metrics from Bloomberg supply chain data and illustrate that the derived signals can lead to profitable trading strategies using a quantitative back-testing framework.

This session will cover the following topics:
- A brief introduction to Quantamental factor investing;
- Quantitative factors derived from supply chain graph: domination, concentration, centrality, etc.;
- Company metrics from supply chain graph combined with market dynamics;
- A study in global markets: supply chain momentum.

Bio: 

Dr. ShengQuan Zhou joined Bloomberg in 2012. Prior to that, he earned his Ph.D. in physics from University of Illinois at Urbana-Champaign. At Bloomberg, ShengQuan's work initially focused on commodities pricing. More recently, he has been working on deriving equity factors from alternative data and various theoretical problems in derivatives pricing.