Black-Litterman Portfolio Optimization Using Gaussian Process Regression.

The Black-Litterman portfolios based on the predictions provided by Gaussian Process are constructed in this study. Besides the expert views generated by the Gaussian Process, an customized algorithm quantifying the confidence level of the given investor opinions is also designed, which can be inputted into the Black-Litterman framework to revise the posterior parameters estimations. Low-risk anomaly is observed from the numerical experiments through the grouping method base on stock β, demonstrating the potential irrationality for even giant companies and brands on the advanced market. Empirical analysis shows that Gaussian Process is able to model the low β stock effectively, while not feasible for stocks with high volatility. Thus, the proposed BLGPlo portfolio outperform the benchmarks in terms of cumulative excess return and Sharpe ratio. Moreover, the BLGPlo performance can be further improved by allocating higher confidence level for the Gaussian Process-derived investor opinions. [ABSTRACT FROM AUTHOR]