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Seeking The Asian See’s Candies

Buffett’s Investment In See’s Candies See’s Candies, a manufacturer and distributor of candy, in particular, boxed chocolates, was cited by Warren Buffett in his response to the first question asked at this year’s Berkshire Hathaway (NYSE: BRK.A ) annual meeting. Buffett said that “The ideal business is one that takes no capital, and yet grows. And there are a few businesses like that, and we own some.” See’s is one of them.” Buffett purchased See’s Candies in January 1972 for $25 million, equivalent to 10 times and 6.2 times its after-tax earnings of $2.5 million and pre-tax earnings of $4.2 million respectively. See’s Candies’ Wide Moat At Berkshire Hathaway’s 1997 annual meeting, Charlie Munger made reference to the purchase of See’s Candies as “the first time we paid for quality,” according to Robert G. Hagstrom’s book “The Warren Buffett Way.” In Berkshire Hathaway’s 2007 letter, Buffett called See’s Candies the “prototype of a dream business.” See’s Candies’ wide moat is derived from several factors, including an enduring brand, strong pricing power, low capital intensity and local dominance. A 71-year old lady named Mary See started See’s Candies as a small candy shop in Los Angeles in 1921. In the domain of enduring consumer brands, where histories are measured in decades, instead of years, See’s Candies benefits from significant customer loyalty driven by habitual purchases and affiliation with the brand. The best illustration of See’s Candies’ brand power comes from none other than Buffett himself: When you were a 16-year-old, you took a box of candy on your first date with a girl and gave it either to her parents or to her. In California the girls slap you when you bring Russell Stover, and kiss you when you bring See’s. See’s Candies’ pricing power is validated by the fact that its pre-tax earnings per pound of chocolate sold grew by a 8.3% CAGR from 25 cents in 1972 to $2 in 1998, which were largely attributed to annual price increases which can be as much as 5% . It had the power to raise prices due to its brand equity and customer price sensitivity. While See’s Candies derived tremendous profit from the sale of boxed chocolate, the money spent on a small-ticket item like chocolates was only a small proportion of household expenditure (and were occasion-driven purchases), and buying more modestly-priced chocolate generated limited cost savings. According to Berkshire Hathaway’s 2007 shareholder letter, See’s Candies was a capital-efficient business which generated a 60% pre-tax return on invested capital at the time of Buffett’s purchase, helped by the fact that sales were transacted in cash (receivable days close to zero) and the production and distribution cycle was short (low inventory days). Regarding local dominance, it was noted in Buffett’s letters that See’s “obtains the bulk of its revenues from only a few states,” “our candy is preferred by an enormous margin to that of any competitor, and “most lovers of chocolate prefer it to candy costing two or three times as much” in the company’s primary marketing area on the West Coast. On the demand side, it is impossible to be everything to everyone given local tastes and heritage; See’s Candies clearly cemented its reputation in California and on the West Coast. See’s also benefited from local economies of scale by dominating the few states and benefiting from fixed cost leverage for logistics and advertising. In a nutshell, See’s Candies enjoyed the widest moat possibly by combining high customer captivity with scale economies. Asia’s See’s Candies Thailand-listed Taokaenoi Food & Marketing, a manufacturer of seaweed snacks, is potentially Asia’s See’s Candies and a wide moat investment candidate at the right price. Taokaenoi was founded by Mr. Itthipat “Tob” Peeradechapan in 2004 (he was 23 years old then), who is currently in his early-thirties. Mr. Itthipat had an entrepreneurial bent since his high school days, when he made money selling virtual weapons for cash on the online role-playing game EverQuest, according to a December 2015 Wall Street Journal article titled “Thai Fried Seaweed King Is on a Roll.” Tao Kae Noi was started as a roasted-chestnut stall business, before he discovered the huge demand and potential for seaweed snacks. Seaweed snacks can be perceived as the Asian equivalent of potato chip and snacks in the West. The brand Tao Kae Noi is synonymous with seaweed snacks in Thailand and many parts of Asia. Taokaenoi passes the local dominance test, boasting a 61.5% market share of Thailand’s 2.5 billion baht packaged seaweed snack market in 2015, according to AC Nielsen research. In other words, Taokaenoi has more than three times the market share of its closest competing brand Masita (17.5% market share) owned by Singha Corporation. The Company’s gross margin, a proxy for pricing power, increased by 610 basis points from 29.3% in 2011 to 35.4% in 2015. I estimate Taokaenoi’s 2015 return on invested capital to be approximately 80% in 2015, comparable with See’s Candies’ 60% pre-tax return on invested capital at the time of Buffett’s investment. Taokaenoi’s inventory days are decent at slightly over a month. Taokaenoi has set an ambitious target of becoming the top Asian seaweed snack brand with annual revenues of 5 billion baht by 2018 and transforming into a global (Taokaenoi derived 52% of its 2015 sales outside of its home market Thailand via export to 34 countries) seaweed snack powerhouse with yearly sales of 10 billion baht by 2024. This implies three-year and nine-year revenue CAGRs of 12.6% and 12.4% respectively compared with Taokaenoi’s 2015 sales of 3.5 billion baht. Taokaenoi was first highlighted to my premium research service subscribers on December 5, 2015 in a subscribers-only article listing five Asian hidden champions. Since Taokaenoi’s listing and trading debut in December 2015, its share price has surged by over 70%. Please refer to my article “Hidden Champions As A Source Of Wide Moat Investment Opportunities” for more information on hidden champions. As a bonus for my subscribers of my premium research service , they will get access to a profile of another Asia-listed hidden champion/See’s Candies in the food business and a list of five “new” Asian hidden champions. Asia/U.S. Deep-Value Wide-Moat Stocks Premium Research Subscribers to my Asia/U.S. Deep-Value Wide-Moat Stocks exclusive research service get full access to the list of deep-value & wide moat investment candidates and value traps, including “Magic Formula” stocks, wide moat compounders, hidden champions, high quality businesses, net-nets, net cash stocks, low P/B stocks and sum-of-the-parts discounts. The potential investment candidates I profiled for my subscribers in May 2015 include: (1) a U.S.-listed market leader in a niche consumer lifestyle space which is trading at 0.80 times P/NCAV and 0.70 times P/B, but remains debt-free and profitable; (2) a U.S.-listed Net Operating Losses-rich deep value play valued by the market at 2.6 times EV/EBITDA net of the present value of its NOLs; (3) an Asian-listed manufacturer of wireless communication products which is the market leader in its home market and the first to export such products to the U.S.; it is a net-net trading at 0.75 times P/NCAV with net cash equivalent to its market capitalization; (4) a U.S.-listed Magic Formula stock trading at 3 times trailing EV/EBIT and Acquirer’s Multiple, sporting a 10% dividend yield net of withholding tax; (5) a U.S.-listed Munger Cannibal trading at 7 times trailing EV/EBIT and Acquirer’s Multiple; (6) an Asian-listed company which is a global leader in a certain medical device niche trading at 3.5 times trailing EV/EBIT and 3.5 times Acquirer’s Multiple, versus a trailing ROIC of 27%. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

A Better Way To Run Bootstrap Return Tests: Block Resampling

Developing confidence about a portfolio strategy’s track record (or throwing it onto the garbage heap), whether it’s your own design or a third party’s model, is a tricky but essential chore. There’s no single solution, but a critical piece of the analysis for estimating return and risk, including the potential for drawdowns and fat tails , is generating synthetic performance histories with a process called bootstrapping. The idea is based on simulating returns by drawing on actual results to see thousands of alternative histories to consider how the future may unfold. The dirty little secret in this corner of Monte Carlo analysis is that there’s more than one way to execute bootstrapping tests. To cut to the chase, block bootstrapping is a superior methodology for asset pricing because it factors in the reality that market returns exhibit autocorrelation. The bias for momentum – positive and negative – in the short run, in other words, can’t be ignored, as it is in standard bootstrapping. There’s a tendency for gains and losses to persist – bear and bull markets are the obvious examples, although shorter, less extreme runs of persistence also mark the historical record as well. Conventional bootstrapping ignores this fact by effectively assuming that returns are independently distributed. They’re not, which is old news. The empirical literature demonstrates rather convincingly a strong bias for autocorrelation in asset returns. Designing a robust bootstrapping test on historical performance demands that we integrate autocorrelation into the number crunching to minimize the potential for generating misleading results. The key point is recognizing that sampling historical returns for analysis should focus on multiple periods. Let’s assume that we’re looking at monthly performance data. A standard bootstrap would reshuffle the sequence of actual results and generate alternative return histories – randomly, based on monthly returns in isolation from one another. That would be fine if asset returns weren’t highly correlated in the short run. But as we know, positive and negative returns tend to persist for a stretch, sometimes in the extreme. The solution is sampling actual histories in blocks of time (in this case several months) to preserve the autocorrelation bias. The question is how to choose the length for the blocks, along with some other parameters. Much depends on the historical record, the frequency of the data, and the mandate for the analysis. There’s a fair amount of nuance here. Fortunately, R offers several practical solutions, including the meboot package (“Maximum Entropy Bootstrap for Time Series”). As an illustration, let’s use a couple of graphics to compare a standard bootstrap to a block bootstrap, based on monthly returns for the US stock market (S&P 500). To make this illustration clear in the charts, we’ll ignore the basic rules of bootstrapping and focus on a ridiculously short period: the 12 months through March 2016. If this was an actual test, I’d crunch the numbers as far back as history allows, which runs across decades. I’m also generating only ten synthetic return histories; in practice, it’s prudent to create thousands of data sets. But let’s dispense with common sense in exchange for an illustrative example. The first graph below reflects a standard bootstrap – resampling the historical record with replacement. The actual monthly returns for the S&P (red line) are shown in context with the resampled returns (light blue lines). As you can see, the resampled performances represent a random mix of results via reshuffling the sequence of actual monthly returns. The problem is that the tendency for autocorrelation is severed in this methodology. In other words, the bootstrap sample is too random – the returns are independent from one another. In reality, that’s not an accurate description of market behavior. The bottom line: modeling history through this lens could, and probably will, lead us astray as to what could happen in the future. Let’s now turn to block bootstrapping for a more realistic profile of market history. Note that the meboot package does most of the hard work here in choosing the length of the blocks. The details on the algorithm are outlined in the vignette. For now, let’s just look at the results. As you can see in the second chart below, the resampled returns resemble the actual performance history. It’s obvious that the synthetic performances aren’t perfectly random. Depending on the market under scrutiny and the goal of the analytics, we can adjust the degree of randomness. The key point is that we have a set of synthetic returns that are similar to, but don’t quite match, the actual data set. Note that no amount of financial engineering can completely wipe away uncertainty. The future can and probably will deliver surprises, for good and ill, no matter how clever our analytics. Nonetheless, bootstrapping historical data (or in-sample returns via backtests) can help separate the wheat from the chaff when looking into the rearview mirror as a preview of what lies ahead. But the details on how you run a bootstrap test are critical for developing comparatively high-confidence test results. In short, we can’t ignore a simple fact: market returns have an annoying habit of exhibiting non-random behavior.

Moby-Markets

“Thar she blows!” “Where away?” “Three points off the lee bow, sir.” “Raise up your wheel. Steady!” Illustration: I.W. Taber. Source: Wikipedia It’s easy to become obsessed. Melville’s famous novel Moby-Dick describes Captain Ahab’s obsession with a giant albino sperm whale. On a previous voyage, the white whale had bitten off Ahab’s leg, leaving him with a prosthesis. Ahab goes on a mission of revenge, casting his spell over the rest of the crew. His fanaticism robs him of all caution. In the end, Moby-Dick destroys the ship and drags Ahab to the bottom. When you’ve suffered a loss in the market, the best thing to do is to put it behind you. Sometimes it’s because the nature of the economy has changed. Sometimes there was an unexpected development – new management, or some external factor. Sometimes you simply miscalculated. Whatever the reason, it’s important to understand that markets are forward-looking. They take current circumstances and future expectations and try to discount all the expected cash-flows to a present value. That’s what market prices represent. Click to enlarge S&P 500 for the last 2 years. Source: Bloomberg So when they move significantly, it’s because the outlook is different. A stock doesn’t know that you own it, and it certainly doesn’t care what the price was when you bought it. Investors can get obsessed with “getting out even.” But that’s a mistake. The only reason to worry about where you bought a stock is to manage your tax-liability. In the midst of the conflict, Ahab was given a final chance to give up his fanatical quest, but he rejects this – to his doom. Investors need to be sure they’re thinking and planning rationally – and not obsessively.