Hayden Capital letter for the second quarter ended June 30, 2017.
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- Mott Capital Management 2Q17 Commentary
Dear Partners and Friends,
In the second quarter of 2017, Hayden Capital gained +12.6% (net of fees). This brings our performance to +13.7% year-to-date. Meanwhile, the S&P 500 and MSCI World were up +3.1% and +4.7% over the same period, respectively. The largest contributor to this outperformance was our recent investment in Zooplus, which I describe in more detail below.
Since inception, we have returned +11.0% annually, versus +9.1% for the S&P 500 and +6.3% for the MSCI World, while keeping 25% of the portfolio in cash.
Earnings Growth = ROIC x Reinvestment Rate
It’s a pretty simple formula, although one that’s largely ignored by most investors. I’ve alluded to this before in prior letters, but most investors tend to focus on the right-hand side, “multiple expansion” portion of the equation as a source of returns. This is essentially betting that other investors will pay more for the same asset in the future.
(I believe this is an unsustainable method of investing and a more detailed discussion of its fallibilities can be found in our Q1 2017 Letter, LINK).
This method of “investing” has become ever more prevalent in recent years, as the time horizons of fundamental investors shorten (studies indicate the average is 18 months or less). 18 months is far too short of a time frame for positive developments at companies to show up in the form of earnings growth. Long-term value creating projects are not created overnight. There’s often years of R&D, investment, implementation, and customer adoption, before investors will see the tangible results of these efforts.
Sadly, most investors are like over-active children – easily bored, and without the patience to stick around before moving on to the next hot stock.
Due to this dynamic, most market participants primarily rely upon multiple expansion for their stock returns. To spot this in stock pitches, look for the keywords “multiple re-rating”, “sentiment shifts”, “change in perception”, etc. Even the broader need for a “catalyst” can be grouped in this category.
While our portfolio certainly benefits from this at times, I view multiple expansion as a “bonus” and is never the crux of our thesis. I would be perfectly happy if our companies were never bid up (i.e. the multiple remained the same), and instead returns simply came from the growth in earnings power.
To evaluate the potential for earnings growth, I focus on ROIC x Reinvestment Rate. ROIC, or “Return on Invested Capital”, is the return a company can earn on each dollar it reinvests back into the business. If the return is sufficiently higher than what investors can achieve elsewhere, the money should be used for new projects that expand the company’s operations4. Thus the higher the incremental ROIC, the higher I look for the Reinvestment Rate to be.
Note though, that what we should care is about is the returns on future projects, not those that took place years ago. Simply looking at the average historical ROIC over the last 10 years from Morningstar isn’t going to cut it. Industry dynamics change, and so does the opportunity set for companies.
So the natural question is, how can we be forward-looking instead of backward looking? Can we collect the data necessary to estimate what the future return on projects will be?
Gaining An Informational & Analytical Edge
The answer is what I call “data-point analysis”. The idea is if we can have a framework which we believe a company’s opportunities will fall under (i.e. the thesis), we can test the validity of this framework by looking at specific projects which we believe are representative of its economics.
For example, let’s assume an e-commerce company has recently announced it plans to build 100 new warehouses across the country. If we owned 100% of the business, we would naturally have the option of either saving the cash and putting it into our pockets, or building out these warehouses. We would want to know which is the better option. It is no different as a public markets investor (albeit only owning a piece, rather than the whole company).
Thus as investors, the most important thing we want to know is what type of return can we expect, from investing money into this project. If the return’s lower than I could get in other similar risk investments (or worse, negative returns), I would rather have the cash in my pocket to invest elsewhere.
It’s likely we wouldn’t have the time or resources to evaluate all 100 warehouses individually. However, we could single out 5 – 10 of these warehouses, ones which we believe give a representative sample, and run the analysis on those. Although we won’t get to 100% confidence, we’ll at least be able to get to 80% confidence on whether this is a good or bad project. Crucially, this exercise it will give us insight into the broader capital allocation skills of management. If our analysis starts indicating paltry returns of 2% project ROIC’s for example, we should be much more hesitant to hand over our hard-earned capital to the company and / or this management team.
So how do you do this in practice? Let’s assume that one of the warehouses is being built in Rochester, NY. This warehouse is meant to replace another one 300 miles further, in order to cut the distance to the customer in half, and deliver goods quicker to households in the region.
The first thing we should do is call the Rochester Chamber of Commerce to get the public filings for this project. Whenever a company plans to build a new warehouse, it needs to get approval by the city. The firm will need to report how many jobs will be created, the average salary per employee (which we can use to estimate labor costs), how much tax revenue it will generate (used to estimate revenues), how large the property will be (used to estimate how many packages it can handle), etc. In public markets investing, where management teams are often reluctant to provide granular details, this type of data is invaluable.
By talking to industry experts, we can also determine how large of a region this size of warehouse can cover (let’s assume 100 square miles in this example). With this knowledge in hand, we can then look at US census data to see how many households live within that parameter (let’s say 1 million). If on average 5% of households use this company’s services, this would imply a coverage of 50,000 households5.
In analyzing the prior financials, we were able to back out that on average it previously costed $7 to ship a package to a customer. Using a rule of thumb of last mile shipping making up 50% of total logistics costs (which we determined by evaluating multiple