In finance, the covariance matrix is the most used tool to assess the risk of a portfolio. There are however, well-known shortcomings to solely use the covariance matrix to construct portfolios that have a similar risk in-sample and out-of-sample .
Addressing these shortcomings, investors have turned to factor-based risk models e.g. including other similarity measures like asset industry, country and, region. While these have developed into the standard market practice nowadays, they have not proven flawless as well. In this internship topic, we therefore want to make the next step, and investigate the potential of graph theory and machine learning techniques such as clustering .
The goal of this internship is to:
Ideally, the project results in several insights that will help Robeco to better translate our alpha predictions into solutions accustomed perfectly to clients’ risk profiles. The topic calls for a lot of creativity in exploring the latest techniques in machine learning and clustering, as well as a practical mindset to solve real-world problems.
 Ledoit, Olivier, and Michael Wolf. "Honey, I shrunk the sample covariance matrix." The Journal of Portfolio Management 30.4 (2004): 110-119.
 Michaud, Richard O., and Robert Michaud. "Portfolio optimization by means of resampled efficient frontiers." U.S. Patent No. 6,003,018. 14 Dec. 1999.
 Chan, Louis KC, Jason Karceski, and Josef Lakonishok. "On portfolio optimization: Forecasting covariances and choosing the risk model." The review of Financial studies 12.5 (1999): 937-974
 López de Prado, Marcos, Building Diversified Portfolios that Outperform Out-of-Sample (May 23, 2016). Journal of Portfolio Management, 2016.