July was a strong month for Chinese equities, with the MSCI China Index soaring 8.3%, up the seventh consecutive month in 2017. The last time such a long winning streak occurred was due to the short-lived bull market in 2015.
A weak US dollar and an improving macro trend attracted foreign inflows to China. The result season kicked off this month with better than expected corporate earnings and profit alerts, which further fuelled the buoyant sentiment. Commodities and basic material prices spiked, thanks to stronger than expected macro data in China. Property, auto, internet, insurance and education stocks also performed very well.
Following a first quarter GDP growth of 6.9%, GDP growth in the second quarter was also 6.9%. This was not a surprise to the market. Macro data released in June was generally good. Retail sales and industrial output continued its upward growth trend, while inflation remained in check. The Investment Adviser expects economic growth to moderate in the second half of the year as tightening measures start to impact the real economy. The team thinks that GDP will grow in a tight range (between 6.5 – 6.8%) over the next few quarters, given the Chinese government’s actions.
The Fund was up 9.0% in July, roughly in line with its benchmark (up 8.3%). The Fund has a mandatory underweight in Tencent and Alibaba (together accounting for 28% of the MSCI China Index), both of which outperformed in July; however the Fund’s active sector allocation offset the resulting slack: The Fund is overweight in downstream gas, packaging paper, tech, auto and education stocks, which outperformed the benchmark. Auto and property names were added to the portfolio, at the expense of HK retailing stocks and exporters.
The market is likely to be subject to a pullback after a non-stop rally year to date, in which case the Investment Adviser will look for buying opportunities.
The views and statements contained herein are those of the LBN Advisers Limited in their capacity as Investment Advisers to the Fund as of 14/08/2017 and are based on internal research and modelling.