In an increasingly complex market, it is crucial to determine the exposure of funds to risk factors to better anticipate the impact of market risks.

Runtian Zhang, a graduate of Ecole Polytechnique, worked on stock price prediction at CNRS before joining the EnvestBoard Data Scientist team. He is currently working on factor analysis and allocation assistance, two key topics to facilitate understanding, selection and investment in funds.

1- Factor analysis: analysis of fund or portfolio exposures

Factor analysis is a technique for measuring potential exposures of funds or fund portfolios to sources of risk, also called risk factors.” explains Runtian. 

This analysis is based on our machine learning algorithms. It allows us to identify and measure the sources (risk factors) of fund over- or under-performance in different market scenarios and therefore to better anticipate the impact of a potential risk. 

For example, detecting and measuring the exposure of a convertible fund to the technology sector will help anticipate the impact of a potential technology crash on this fund and the portfolios that invest in it.

2- Allocation support for building fund portfolios

Runtian draws on advanced analytical techniques to develop our selection and allocation modules:

– For fund selection, our goal is to simplify it with more intuitive and in-depth analyses in order to strengthen the quality of each investor’s “buy list”. As an example, the factor analysis mentioned above is one of the methods used by Runtian

– For allocation support, factor analysis is used to determine in depth portfolio exposures and diversification to ensure robustness

These features are designed to enhance the robustness of the portfolio through a simple process. 

As an investor, I want to ensure the quality and diversification of my portfolios or buy lists. With EnvestBoard’s factor analysis, I determine the risk exposures of the funds in my list. 

From this list, I build a robust, diversified allocation that matches my client’s risk profile over time.