Building a Core China Portfolio – Part 1

22 Aug

I’ve wanted to build a China portfolio for a while.  But I’m not (yet!) an expert on Chinese stocks.  So in order to make an educated decision (and improve mine and your expertise along the way), I’ve decided to apply the same principles I’ve learned and used in other markets.  My goal is to discern which companies should form the core of my portfolio. I’m not sure where my investigation will take me, but I invite you along for the ride.

The ultimate goal is to look for quality investments (sustainable high returns) with no balance sheet or left-field risk, and a competitive edge.  This must all come at a reasonable price with a wide margin of safety.  These are the kind of names one holds for the long term i.e. investments, not speculation.  And yes… such companies do exist!

The first step is often the most difficult.  With 1000’s of mainland and Hong Kong listed companies, where does one start?  Well I decided to apply a basic return heuristic to the entirety of the Shanghai and HK stock exchanges, and rank for the best performers – i.e. create a shortlist for deeper analysis and investigation. I admit that it’s not a fool proof system, and I know quality companies will fall by the wayside at this early stage, and that low quality might slip through the safety net. But the logic is fairly sound, which should lead to a fairly sound short list of 10’s of stocks.

I’m going to take every listed company and compute a form of return over a series of different length periods. The reason to take different periods (5, 10 and 15 years in this study) is to test how robust the returns of the company are historically. Sustainable returns are important as returns are the most important indicator of quality to a stock analyst.  If we understand the environment (both internal and external) that contributed to said returns, and can qualitatively assess how likely those factors are to continue in the coming years, that combination of solid facts and common sense is as close to science as stock picking comes.

In addition, the primary job of management from our perspective is to allocate capital effectively within their company and create value for stockholders. In other words, their job is to consistently deliver returns greater than their cost of capital.  Many analysts will meet management and declare whether or not they are good or bad.  Now my first degree is in psychology, but it didn’t make me a mind reader. How can an analyst jump to such conclusions, often using them as a basis for his or her investment decision? I don’t know how. But the return data does not lie, and solid returns over an extended period (note – solid depends on many things, and will vary according to sector – though as a rule of thumb, the developed market is returning about 12% return on book equity) is a good indicator that management knows what its doing. This data point should not be used in isolation to judge management, but the importance of capital allocation and returns to the shareholder, along with the dependence of returns on management decisions, makes good management golden.

Anyway, back to creating our shortlist.  The return heuristic I am going to calculate is the following:

Increase in net income over the period / (Increase in common equity + increase in net debt)

In other words, this tells me over a certain period of time how much capital a company employed to create additional income over that same period. The higher the return sustainably, the better the company has been at using its additional capital to create value for its shareholders. Granted it uses P&L data which is itself riddled with caveats, (not to mention that the system does not account at this stage for capital structure) but ranking such returns over different periods and assessing sustainability is a good way to create a short list of 10’s of companies for close inspection from a universe of 1000’s.

I will use Bloomberg data, again not fool proof, and I caution against making a final decision to buy (or short) without reading or modeling from numbers taken from actual annual reports. But I’m happy using their data as part of an exclusion exercise, as it is mostly robust and extremely reliable

I will publish my findings in the coming days.  Maybe I will put some EU and US data so that readers familiar with those markets might feel more comfortable.

Zai Jian

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