Camden Wang is a Quantitative Analyst for the Disciplined Alpha Team at Loomis, Sayles & Company. He is responsible for assisting with the integration of research and technology for the team’s investment process as well as the development and implementation of tools for attribution and risk management. Camden joined Loomis Sayles in 2020. Previously, he was a quantitative summer associate in the wholesale credit group at JP Morgan, where he implemented a wide range of machine learning and neural network methods to model defaults and credit downgrades for institutional loans. Camden began his investment industry career in 2019. He earned a BS from the University of Science and Technology of China and a PhD from the University of Pittsburgh.
We use Portfolio Impact (PI), a transparent and relatively straightforward risk measure, to help understand portfolio-level sector risk and issue-specific (idiosyncratic) risk.
Disciplined Alpha
About the Disciplined Alpha Team
Using rigorous research and consistent systematic risk management to identify relative value opportunities across bonds and sectors to drive alpha generation.
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