What kind of patterns can we visualize in China-A vs Hong Kong-H Pairs?

Posted on Jul 29, 2018

A common strategy that Hedge Funds employ is the Relative Value strategy. This strategy seeks to exploit the differences in price of the same or similar security. It is a form of pairs trading where there is a discrepancy in pricing when buying one security and selling the other. The methodology is to take a position when the gap between the prices has considerably widened or when the price as reached its peak. That's when the timing is right to trade when it is anticipated to start shrinking again or narrowing back to it's long-term mean. Most pair strategies are highly correlated and show mean reversion.

This phenomenon is seen in the China A-Share (domestic market) vs Hong Kong H-Share (foreign market). China A-Shares are listed on the Shanghai and Shenzhen stock market. Hong Kong H-Share are PRC companies listed on the Hong Kong stock exchange, most of which are China State Owned Enterprises (SOEs). The intention of the SOE's listing in HK was to raise additional capital, upgrade the corporate governance board and establish international standards. Companies listed in both the China and Hong Kong markets are considered dual-listed companies (DLC).

In this analysis, we study the premium/discount of these dual-listed companies. Currently there are 102 DLCs. We want to investigate whether there is a trading strategy for them by looking at the time series data of the premium/discount and whether or not specific companies trade in bands and show mean reversion. The price discrepancy is caused by regulatory factors (China's policies in specific industries), FX, intraday stock movements, company specific news, float and SOE (controlling stakeholders), market liquidity, volatility, fundamentals, asymmetric information and investor base (China has a huge retail investor market).

We calculate the A/H pricing differential by taking the ratio of the A-Share stock price divided by the H-Share stock price in USD minus one. If the ratio is greater than one, we say that the pair is trading at a premium and if it's less than one, we say that the pair is trading at a discount. We can see that there is often a convergence/divergence pattern and when the gap is wide enough, the pattern will narrow again.

We first take a random data set of 23 names and take data from Jan 2015 until July 2018.

Below is an example of Agricultural Bank of China. We chart the Premium/Discount for the chart from Jan 2015 to July 2018. We also plot a smooth trend line to see the direction of the price of the Premium.

Trading Ideas

  • We see there were 4 opportunities between 2015 and 2016 to go long the A-Shares and Short H-Shares at around the 5% premium range.
  • There are a couple of opportunities between middle of 2015 and middle of 2016 to short the A-Shares and Long H-Shares at the 26% to 30%  Premium range.
  • Early 2017, we see twice at parity to long the A-Shares and short the H-Shares
  • Middle of 2017 to Early 2018 at the 20% to 25%, there are 2 occasions to short the A-Shares and Long H-Shares.
  • So far this year, there has been one opportunity in mid-2018 at the 5% premium to Long A-Shares and short H-Shares.

We see that the long-term mean reversion is at a 15% premium and can trade at the wider ends of the band.


The boxplot below shows the statistical bands of ABC and the other 22 names in the sample. The bands for ABC are

Max: 30.4%, 3rd quartile: 20.25%, Median: 15%, 1st quartile: 9.1% and Min: 0%

Below is a scatterplot of the Premium/Discount vs the MarketCap of the company. We can see that companies under $50billion have a higher premium than those companies greater than $50billion.



The pie graph below shows the following out of the 23 names

11 names trade at a premium > 20%

9 names trade at a premium between 0% and 20%

2 names trade at a discount between 0% and -10%

1 name trades at a discount less than -10%




The A-H pair names have a long-term mean reversion premium/discount. You can trade at levels when the bands are wide enough, as they eventually tend to go back to their mean.

About Author


Howard Chang

Howard is currently a NYC Data Science Academy Fellow with a MS in Applied Math and Statistics from Stony Brook University. He has work experience in Portfolio Finance and Margin at a billion dollar multi-strategy hedge fund and...
View all posts by Howard Chang >

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