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How To Identify A Stock With A Competitive Advantage: Cross Your Fingers

When selecting a long-term stock investment, most investors take a page out of Warren Buffett’s playbook and look for companies with strong competitive advantages. History has shown that the odds of identifying such advantages are no better than the flip of a coin. Protecting the Castle A competitive advantage is one of the most sought after characteristics of any long-term investment. Wall Street analysts grade stocks almost exclusively on the strength and sustainability of a company’s competitive advantage. Value investors demand stocks that have track records of fending off competition and sustaining high profit margins. Warren Buffett describes a business with a competitive advantage as a castle with a moat around it. The wider, deeper, and more treacherous the moat, the better the investment. In a 1999 article in Fortune Magazine, Buffett said: “The key to investing is not assessing how much an industry is going to affect society, or how much it will grow, but rather determining the competitive advantage of any given company and, above all, the durability of that advantage. The products or services that have wide, sustainable moats around them are the ones that deliver rewards to investors.” This makes sense. Without some sort of advantage, a profitable business will not be profitable for long. Deterioration Not only are economic moats hard to find, they are hard to maintain. Furthermore, predicting which companies will have strong competitive advantages in the future can be downright impossible. Economic theory shows that once a business obtains an advantage in the marketplace, competitors will attempt to copy and improve upon the successful business model. This will ultimately eat away at the company’s profits and deteriorate its competitive advantage. Here, we’ll look at two case studies which have played out over the last 20 years. In one case, the company possessed all the advantages of an economic moat. In the other case, the company was rapidly losing market share and had a miserably bleak future. Today, one company is out of the business and the other has multiplied hundreds of times over. Blockbuster Video A perfect example of a deteriorating competitive advantage can be found in the case of Blockbuster Video. After opening its first location in 1985, the company became synonymous with movie rentals in the 1990s. By the late 90s Blockbuster had been able to maintain its competitive advantage and fend off its toughest competitor – Hollywood Video. Once the new millennium began, Blockbuster had a whole new set of competitors – Netflix (NASDAQ: NFLX ) and Redbox. Where Blockbuster charged over $5.00 per rental and required customers to pick up movies in person, Redbox charged only $1.00 and Netflix offered unlimited DVD rentals by mail for one flat monthly price. When both companies were in their infancies, Blockbuster could have easily copied their business models or bought them out. In 2000, Netflix offered to sell its operations to Blockbuster for only $50 million. Blockbuster passed on the opportunity to own the company that would eventually be the cause of its demise. Eight years later, as Blockbuster’s business model was teetering on the brink of collapse, management still had its head in the sand. Here’s what Blockbuster CEO said in 2008: “Netflix doesn’t really have or do anything that we can’t or don’t already do ourselves.” It can be argued that Redbox had just as big – if not a bigger – impact on Blockbuster than Netflix. After launching its first video rental kiosks in 2004, Redbox quickly became a cheap and convenient way to rent new release movies on a nightly basis. It wasn’t until 2010 that Blockbuster decided to aggressively pursue this business model with its Blockbuster Express kiosks. By then, it was too late. Apple If Blockbuster is the perfect example of a profitable business losing its competitive advantage, Apple (NASDAQ: AAPL ) is the perfect example of a company regaining its competitive advantage. Less than 15 years after selling its first computer, the company posted its most profitable year in 1990. However, things started to turn south quickly for Apple. While both founders – Steve Jobs and Steve Wozniak – moved on to other ventures, the company started losing market share. By 1996, industry experts and Wall Street analysts had left the company for dead. At the time, no one could foresee the return of Steve Jobs and the innovative products he would bring to the company. Rather than competing head-on with Microsoft (NASDAQ: MSFT ) in the PC market, Jobs decided Apple would gain a competitive advantage by focusing on music-related products. First came the iPod, then iTunes and the rest is history. Today, iPhones and iPads have transformed Apple from a $3 billion company in 1996 to a market cap of $600+ billion in 2016. Identifying a Moat Looking back, it’s easy to say that Apple was the better investment in 1996. A share of Apple has multiplied more than 200 times over in the last 20 years. By contrast, an investment in Blockbuster Video would have gone to zero. But was it possible to have seen this at the time? In investing, the past is history. The future is where profits are made. Let’s put ourselves in the shoes of a stock market investor in 1996 and try to identify which company had the better competitive advantage. According to Pat Dorsey, Equity Research Analyst at Morningstar, there are four types of economic moats: Intangible Assets Customer Switching Costs The Network Effect Cost Advantages Dorsey says, “At Morningstar, thinking about economic moats, or structural competitive advantages, is central to how we do equity research.” He claims that having any or all of the above characteristics, “give superior companies the power to stay on top.” Breaking It Down By breaking down each advantage we should be able to compare Blockbuster with Apple and see what an investor would have thought about the future prospects of each. Intangible Assets: Brand recognition, customer loyalty, patents or trademarks, etc. In 1996, Blockbuster video had strong brand recognition and customer loyalty. The company’s slogan, Make it a Blockbuster night was on everyone’s mind when they wanted a night in. The name familiarity gave customers confidence that the local Blockbuster would have a broad selection of the latest new releases and all the old classics. In order to rent from Blockbuster, customers had to have a membership card. Keeping this card in their wallets reminded customers on a daily basis that Blockbuster was where they went to rent movies. Customer Switching Costs: Time, money, or inconvenience to switch to a competitor. Opening an account with a movie rental company in the mid-1990s was like setting up an IRA. It required multiple forms of ID, proof of residence, a complete family-tree and the family dog for collateral. The Network Effect: Everyone uses it because everyone uses it. Everyone had a Blockbuster card in their wallets, so there was a Blockbuster in every town. The more customers Blockbuster obtained, the more locations they would open. The more locations they opened, the more customers they obtained. It became very convenient to be a Blockbuster customer because the stores were everywhere. Cost Advantages: Scale-based cost advantages allow for huge profit margins on additional sales. Blockbuster received payments dozens, hundreds, or even thousands of times on one VHS or DVD. If one more customer decided to rent a movie on a Friday night, it would not cost the company anything. The additional rental would be pure profit to Blockbuster. The VHS or DVD had already been paid for by Blockbuster. The customer was required to give the movie back to Blockbuster for them to rent again. On The Other Hand By contrast, Apple had none of these competitive advantages in 1996. The OS business was controlled by Microsoft, and Dell controlled the PC market. Apple was a tiny player which the market valued for less than its liquidation value. At the end of 1996, Apple qualified as a Benjamin Graham NCAV stock . It was literally valued more dead than alive. Putting It into Practice Today, there are all kinds of companies which appear to have wide moats protecting their profits. Even more so are the companies which appear to be past their prime and rapidly losing market share. The problem with insisting on investing only where sustainable moats exist, is that strong competitive advantages are impossible to foresee. It’s one thing to identify companies where moats currently exist, it’s another thing to know whether those moats will exist in 5, 10 or 25 years from now. Can you confidently predict which stocks will be the next Blockbuster and which will be the next Apple?

Clips From Abdulaziz Alnaim’s Interview With The Manual Of Ideas (Video)

Originally Published on March 21, 2016 I was recently interviewed by the wonderful publication, The Manual of Ideas , where we discussed various issues related to our strategy and to investing in general. I would like to share the following three clips from that interview with you. I hope you enjoy them. Abdulaziz Alnaim on Market Efficiency and Why Value Investing Works Abdulaziz Alnaim: We Begin by Looking for a Reason to Say ‘No’ Abdulaziz Alnaim on the Importance of Robustness

Estimating Future Stock Returns, Follow-Up

Click to enlarge Idea Credit: Philosophical Economics Blog My most recent post, Estimating Future Stock Returns was well-received. I expected as much. I presented it as part of a larger presentation to a session at the Society of Actuaries 2015 Investment Symposium, and a recent meeting of the Baltimore Chapter of the AAII. Both groups found it to be one of the interesting aspects of my presentation. This post is meant to answer three reasonable questions that got posed: How do you estimate the model? How do we understand what it is forecasting given multiple forecast horizons seemingly implied by the model? Why didn’t the model forecast how badly the market would do in 2001 and 2008? And I will add 1973-4 for good measure. Ready? Let’s go! How to Estimate In his original piece , @Jesse_Livermore freely gave the data and equation out that he used. I will do that as well. About a year before I wrote this, I corresponded with him by email, asking if he had noticed that the Fed changed some of the data in the series that his variable used retroactively. That was interesting, and a harbinger for what would follow. (Strange things happen when you rely on government data. They don’t care what others use it for.) In 2015, the Fed discontinued one of the series that was used in the original calculation. I noticed that when the latest Z.1 report came out, and I tried to estimate it the old way. That threw me for a loop, and so I tried to re-estimate the relationship using what data was there. That led me to do the following: I tried to get all of them from one source, and could not figure out how to do it. The Z.1 report has all four variables in it, but somehow, the Fed’s Data Download Program, which one of my friends at a small hedge fund charitably referred to as “finicky”, did not have that series, and somehow FRED did. (I don’t get that, but then there are a lot of things that I don’t get. This is not one of those times when I say, “Actually, I do get it; I just don’t like it.” That said, like that great moral philosopher Lucy van Pelt, I haven’t ruled out stupidity yet. To which I add, including my stupidity.) The variable is calculated like this: (A + D)/(A + B + C + D) Not too hard, huh? The R-squared is just a touch lower from estimating it the old way… but the difference is not statistically significant. The estimation is just a simple ordinary least squares regression using that single variable as the independent variable, and the dependent variable being the total return on the S&P 500. As an aside, I tested the variable over other forecast horizons, and it worked best over 10-11 years. On individual years, the model is most powerful at predicting the next year (surprise!), and gets progressively weaker with each successive individual year. To make it concrete: you can use this model to forecast the expected returns for 2016, 2017, 2018, etc. It won’t be very accurate, but you can do it. The model gets more accurate forecasting over a longer period of time, because the vagaries of individual years average out. After 10-11 years, the variable is useless, so if I were put in charge of setting stock market earnings assumptions for a pension plan, I would do it as a step function, 6% for the next 10 years, and 9.5% per year thereafter… or in place of 9.5% whatever your estimate is for what the market should return normally. On Multiple Forecast Horizons One reader commented: I would like to make a small observation if I may. If the 16% per annum from Mar 2009 is correct we still have a 40%+ move to make over the next three years. 670 (SPX March 09) growing at 16% per year yields 2900 +/- in 2019. With the SPX at 2050 we have a way to go. If the 2019 prediction is correct, then the returns after 2019 are going to be abysmal. The first answer would be that you have to net dividends out. In March of 2009, the S&P 500 had a dividend yield of around 4%, which quickly fell as the market rose and dividends fell for about one year. Taking the dividends into account, we only need to get to 2,270 or so by the March of 2019, works out to 3.1% per year. Then add back a dividend yield of about 2.2%, and you are at a more reasonable 5.3%/year. That said, I would encourage you to keep your eye on the bouncing ball ( and sing along with Mitch … does that date me…?). Always look at the new forecast. Old forecasts aren’t magic – they’re just the best estimate of a single point in time. That estimate becomes obsolete as conditions change, and people adjust their portfolio holdings to hold proportionately more or less stocks. The seven-year-old forecast may get to its spot in three years, or it may not – no model is perfect, but this one does pretty well. What of 2001 and 2008? (And 1973-4?) Another reader wrote: Interesting post and impressive fit for the 10-year expected returns. What I noticed in the last graph (total return) is, that the drawdowns from 2001 and 2008 were not forecasted at all. They look quite small on the log-scale and in the long run but cause lot of pain in the short run. Markets have noise, particularly during bear markets. The market goes up like an escalator, and goes down like an elevator. What happens in the last year of a ten-year forecast is a more severe version of what the prior questioner asked about the 2009 forecast of 2019. As such, you can’t expect miracles. The thing that is notable is how well this model did versus alternatives, and you need to look at the graph in this article to see it (which was at the top of the last piece). (The logarithmic graph is meant for a different purpose.) Looking at 1973-4, 2001-2 and 2008-9, the model missed by 3-5%/year each time at the lows for the bear market. That is a big miss, but it’s a lot smaller than other models missed by, if starting 10 years earlier. That said, this model would have told you prior to each bear market that future rewards seemed low – at 5%, -2%, and 5%, respectively, for the next ten years. Conclusion No model is perfect. All models have limitations. That said, this one is pretty useful if you know what it is good for, and its limitations. Disclosure: None