By Alex Chen, associate director, research & analytics
Over the years, China has grown to be the second largest economy and the host of the second largest stock market, second only to the United States. The China A-Shares market, which was previously only available to domestic investors, continues to open up to international investors through a series of reforms and increased access channels.
Following the September 2018 annual country classification review, FTSE Russell announced that the China A-Shares market will be promoted to Secondary Emerging market status and will be included in its global equity benchmarks from June 2019. With these positive developments, increasing interest and participation in the China A-Shares market by international investors can be reasonably anticipated.
Generally speaking, a stock market is driven by economic growth, company fundamentals and investor sentiment, and the China A-Shares market is no exception. Nevertheless, one of its unique features is high retail participation rate. In the China A-Shares market, over 80% of the trading volume is contributed by retail investors. The dominance of retail investors has profound impact on market movement, particularly reflecting their investing sentiments (retail investors tend to have shorter investment horizons and often overreact to new information).
This behavior pattern brings in potential investment opportunities. However, it also comes with increased market volatility. To address this, a minimum variance approach could be one of the options for accessing the China A-Shares market, in addition to the traditional market-cap-weighted approach.
This study—written for The Journal for Portfolio Management by myself, my colleague Yang Wang, director of research & analytics, and Eddie Pang, our former head of Asia Pacific research—serves as an informational guide for minimum variance portfolios in the China A-Shares market. Our methodology takes into account the special feature of stock suspensions, and the simulation results show that the minimum variance portfolio reduced volatility over the sample period between 2007 and 2017. In addition, we apply two methods to overlay factor characteristics on the minimum variance portfolio.
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