Incorporating Trading Strategies in the Black‐Litterman Framework

It is well known that applying classical portfolio optimization in practice may lead to problems; in fact, the “optimal” portfolio may not be optimal at all. The problems encountered in real-world portfolio optimization include issues such as unstable portfolio weights, corner solutions, and poor performance. Some portfolio managers are using Bayesian estimation techniques and robust portfolio optimization to mitigate some of these problems. The Black-Litterman framework has become more popular among practitioners as it provides a flexible yet robust quantitative portfolio management tool, into which different trading strategies are easily incorporated.