Investing in Global Equity Markets. Issue 106 (23rd March 2020)

• Record Type: Journal Article
• Authors: Guerard, John B.; Deng, Shijie; Gilliam, Robert A.; Markowitz, Harry; Xu, Ganlin
• Is Part Of: Wilmott. Volume 2020:Issue 106(2020)
• Page Start: 56
• Page End: 75
• Publication Date: 2020-03-23
• Subjects: portfolio optimization -- data mining -- earnings forecasting; Finance -- Periodicals; Financial services industry -- Periodicals; 332
• DOI: 10.1002/wilm.10832
• Languages: English
• ISSNs: 1540-6962

Abstract

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 updated US, Non‐US, and emerging markets stock models during the 2003 through November 2018 time period, and report a continued highly statistically significant risk‐return trade‐off.