I am excited to be awarded and to share my work at Saint John's Newfoundland; what a wonderful place! Another must-go place.

First Prize - Open Category

"Portfolio Selection under Model Uncertainty: A Penalized Moment-Based Optimization Approach"
Jonathan Y. Li, University of Toronto

Uncertainty on the choice of a financial model leads to additional "model risk" in portfolio selection. This level of risk becomes ever pronounced in recent financial markets and results in substantial deterioration of portfolio performance. In practice, portfolio managers face difficulty in seeking a balance between relying on their knowledge of a reference financial model and taking into account possible ambiguity of the model. We present a new approach for portfolio selection when the underlying distribution of asset returns is uncertain or ambiguous to investors. In particular, we consider the case that an investor can formulate some reference financial models based on his/her prior beliefs or information but is concerned about misspecification of the reference models and the associated loss. Based on the concept of Distributionally Robust Optimization (DRO), we introduce a new penalty framework that provides investors flexibility to define prior reference models using moment information and accounts for model ambiguity in terms of moment uncertainty. We further provide efficient solution methods for the penalized moment based problem. Computational experiments show that our penalized moment-based approach outperforms classical DRO approaches using real-market data. Furthermore, the approach can be extended to incorporate alternative uncertainty structures and factor models.