Investing in Global Equity Markets with Particular Emphasis on Chinese Stocks

Abstract

In this analysis of the risk and return of stocks in global markets, we build several models of stock selection and create optimized portfolios to outperform a global benchmark. We apply several applications of robust regression techniques in producing stock selection models and several Markowitz-based optimization techniques in portfolio construction in various global stock universes. We test separate Japanese and Chinese stock selection models because they are large markets, with large global benchmark weights or are frequently in the news. We find that (1) that robust regression applications are appropriate for modeling stock returns in global markets; and (2) mean-variance techniques continue to produce portfolios capable of generating excess returns above transactions costs; and (3) our models pass data mining tests such that the models produce statistically significant asset selection. We estimate expected return models in a global equity markets using a given stock selection model and generate statistically significant active returns from various portfolio construction techniques.