Category Archives: Valuation Techniques

Sandstorm Gold Analysis; Other Readings

A reader (the ONLY one) presented his valuation below:

The analysis is in response to http://csinvesting.org/2017/10/27/sandstorm-gold-so-whats-it-worth/

Business Model

Sandstorm provides financing for other junior, mid-tiers and major gold producers. In exchange for a principal amount provided by Sandstorm, gold producers exchange a royalty stream on their gold production. This royalty stream can take different forms, the most common being a percentage of Net Smelter Revenue (“NSR”) or by offering an off-take agreement at discounted prices. In addition, Sandstorm may receive warrants or other traded securities.

Sandstorm offers their shareholders a diversified portfolio of royalty streams, which offers some benefits over investing in a gold exploration/producing company: 1) predictable cash flows, 2) very low cost structure, 3) replacement capex (investment in new projects) typically much lower than for a gold producer.

In my view, this kind of investment should benefit from a lower cost of capital because of its lower risk, so therefore investors would be willing to pay more for this cash flow stream vs. a cash flow stream coming out directly from a mine.

Valuation

I’m looking at 3 main buckets of value here.

1) Producing assets – currently generate ~US$50mm of cash flows each year. My rough assumption here is that these cash flows should be relatively stable over the next 10 years. I assume no terminal value as these mines are winded-down over time. Discounting this at a cost of equity of ~8% yields ~US$335mm

2) Advanced exploration / explo / investment portfolio – I’m assuming no value to the exploration projects and assuming a 10% discount to the FMV of the investment portfolio. This yields ~US$70mm.

3) Development projects – this one is certainly tougher to ascribe value. If we look at the SSL presentation, we see that the majority of future cash flows will come out from Hot Maden which was paid US$175mm. Assuming there was a market check done on the sale of the stream, we can assume this is market value. Other projects from the development portfolio will also yield future cash flows, but it can be assume to be somewhat captured in the above DCF.

So overall, I compute US$335mm + US$70mm + US$175mm = US$580 which is far from the current market cap of the company.

One needs to believe that the development portfolio will materialize into sustainable cash flows (which would essentially translate into the addition of a terminal value in my DCF) before investing in this business.

Let me know your thoughts and feel free to share my response on your website!

CSInvesting: I will be posting my thoughts soon.

Interesting readings

https://thefelderreport.com/2017/10/31/tobias-carlisle-on-beating-the-little-book-that-beats-the-market/     Worth a listen!

 

Sandstorm Gold–So What’s it Worth?

If am able to provide an investing course, then once the fundamentals are covered, we could study cases.  Let me know your thoughts.

The Life of an Analyst

Your boss slaps these documents on your desk.  “Let me know what you think. I want a back-of-the-envelope valuation and a sixty-second summary of this business by this afternoon.”

What’s the essence of this business? Hannibal Lecter will guide you:  https://youtu.be/UhDZPYu8piQ?t=58s

Your analysis should be clear and simple:

How can the portfolio manager expect YOU to answer quickly with this deluge of info? That’s what we will learn here today.

I will post my “answer” by Tuesday of next week.   Email me at aldridge56@aol.com if you wish to share your thoughts or do so at the deep-value group at Google Groups (sign up here: http://csinvesting.org/2015/01/14/deep-value-group-at-google/) rather than post in the comments section, because readers shouldn’t be influenced by others.  No help!   This case illustrates the reality at investment firms.   Your boss dumps a 500-page prospectus and says get back to me in two hours–“What’s it worth?”

Have fun!

Poking Holes in the Market Bubble Hypothesis

Nygren Commentary September 30, 2017

CSInvesting: We can’t increase our IQ but we can try to improve our critical thinking skills by seeking out opposing views to the now current din of pundits screaming that this “over-valued market is set to crash.”  1987 here we come.  What do you think of his arguments?  I certainly agree about how GAAP accounting punishes growth investments.  

At Oakmark, we are long-term investors. We attempt to identify growing businesses that are managed to benefit their shareholders. We will purchase stock in those businesses only when priced substantially below our estimate of intrinsic value. After purchase, we patiently wait for the gap between stock price and intrinsic value to close.

“All the company would have to do is raise prices 50% and the P/E ratio would fall to the low-teens.”   -Analyst recommending a new stock purchase

We are nine years into an economic and stock market recovery and P/E ratios are elevated somewhat beyond historic averages. So when an experienced portfolio manager hears a young analyst make the above comment, he hears alarm bells. But instead of seeing this as a sign that the market has peaked, we purchased the stock for the Oakmark Fund. But, more on that later.

For several years, the financial media has been dominated by pronouncements that the bull market is over. Throughout my career, I can’t remember a more hated bull market. Many state that a recession is “overdue” since past economic booms have almost never lasted as long as this one. But do nine years of sub-normal economic growth even constitute a recovery, much less a boom? If recessions occur to correct excesses in the economy, has this recovery even been strong enough to create any? Maybe recessions are less about duration of the recoveries they follow and more about the magnitude. If so, earnings might not even be above trend levels.

Bears will also point to the very high CAPE ratio—or the cyclically adjusted P/E. That metric averages corporate earnings over the past decade in an attempt to smooth out peaks and valleys. But remember that the past decade includes 2008 and 2009, frequently referred to as the “Great Recession” because of how unusually bad corporate earnings were. I’ll be the first to say that if you think an economic decline of that magnitude is a once-in-a-decade event, you should not own stocks today. But if it is more like a once-in-a-generation event, then that event is weighted much too heavily in the CAPE ratio. If the stock market and corporate profits maintained their current levels for the next two years—an outcome we would find disappointing—simply rolling off the Great Recession would result in a large decline in the CAPE ratio.

Higher P/E ratios are also caused by near-zero short-term interest rates because corporate cash now barely adds to the “E” in the P/E ratio. When I started in this business in the early 1980s, cash earned 8-9% after tax. Consider a simple example of a company whose only asset is $100 of cash and the market price is also $100. In the early 1980s, the $8 or $9 of interest income would generate a P/E ratio of about 12 times. Today, $100 would produce less than $1 of after-tax income, driving the P/E ratio north of 100 times. There is, of course, uncertainty as to whether that cash will eventually be returned to shareholders or invested in plants or acquisitions, but it seems that making a reasoned guess about the value of cash is more appropriate than valuing it at almost nothing.

A less obvious factor that is producing higher P/E ratios today is how accounting practices penalize certain growth investments. When a company builds a new plant, GAAP accounting spreads that cost over its useful life—often 40 years—so the cost gets expensed through 40 years of depreciation as opposed to just flowing through the current income statement.

But when Amazon hires engineers and programmers to help it prepare for sales that could double over the next four years, those costs get immediately charged to the income statement. When Facebook decides to limit the ad load on WhatsApp to allow it to quickly gain market share, the forgone revenue immediately penalizes the income statement. And when Alphabet invests venture capital in autonomous vehicles for rewards that are years and years away, the costs are expensed now and current earnings are reduced.

The media is obsessed with supposedly bubble-like valuations of the FANG stocks—Facebook, Amazon, Netflix and Google (Alphabet). The FANG companies account for over 7% of the S&P 500 and sell at a weighted average P/E of 39 times consensus 2017 earnings. In our opinion, the P/E ratio is a very poor indicator of the value of these companies. Alphabet is one of our largest holdings, and our valuation estimate is certainly not based on its search division being worth 40 times earnings. If one removed the FANG stocks from the S&P multiple calculation—not because their multiples are high, but because they misrepresent value—the market P/E would fall by nearly a full point. And, clearly, more companies than these four are affected by income statement growth spending.

In addition, no discussion of stock valuations would be complete without some consideration of opportunities available in fixed income. Many experts argue that investors should sell their stocks because the current S&P 500 P/E of 19 times is higher than the 17 times average of the past 30 years. By comparison, if we think of a long U.S. Treasury bond—say, 30 years—in P/E terms, the current yield of 2.9% results in a P/E of 34 times. The average yield on long Treasuries over the past 30 years has been 5.5%, which translates to a P/E of 18 times. Relative to the past 30 years, the long bond P/E is now 90% higher than average. We don’t think the bond market at current yields is any less risky than equities.

The point of this is not to advance a bullish case for stocks, but rather to poke holes in the argument that stocks are clearly overvalued.

We think our investors would also fare best by limiting their in-and-out trading. We suggest establishing a personal asset allocation target based on your financial position and risk tolerance. Then limit your trading to occasionally rebalancing your portfolio to your target. If the strong market has pushed your current equity weighting above your target, by all means take advantage of this strength to reduce your exposure to stocks.

Now, back to the P/E ratio distortions caused by investing for growth. This highlights a costly decision we made six years ago. In 2011, when Netflix traded at less than $10 per share, one of our analysts recommended purchase because the price-per-subscriber for Netflix was a fraction of the price-per-subscriber for HBO. Given the similarity of the product offerings and Netflix’s rapid growth, it seemed wrong to value the company’s subscribers at less than HBO’s. But, at the time, streaming was a relatively new technology, HBO subscribers had access to a much higher programming spend than Netflix subscribers and Netflix was primarily an online Blockbuster store, providing access to a library of very old movies. Netflix had only one original show that subscribers cared about, House of Cards, and churn was huge as they would cancel the service after a month of binging on the show. Despite the attractive price-per-sub, we concluded that the future of Netflix was too uncertain to make an investment.

Today, Netflix trades at $180 per share and has more global subscribers than the entire U.S. pay-TV industry. Netflix provides its subscribers access to more than two times the content spending that HBO offers, making it very hard for HBO to ever match the Netflix value proposition. Finally, Netflix is no longer just a reseller of old movies. The company has doubled its Emmy awards for original programming in each of the past two years and now ranks as the second most awarded “network.” On valuation, Netflix is still priced similarly to the price-per-subscriber implied by AT&T’s acquisition of HBO’s parent company Time Warner, despite Netflix subscribers more than quadrupling over the past four years while HBO subscribers have grown by less than one third.

Last quarter, when our analyst began his presentation recommending Netflix, selling at more than 100 times estimated 2017 earnings, I was more skeptical than usual. His opening comment was that Netflix charges about $10 per month while HBO Now, Spotify and Sirius XM each charge about $15. “All the company would have to do is raise prices 50% and the P/E ratio would fall to the low teens,” he argued. Anecdotally, those who subscribe to several of these services tend to value their Netflix subscription much higher despite its lower cost. Quantitatively, revenue-per-hour-watched suggests Netflix is about half the cost (subscription fees plus ad revenue) of other forms of video. Netflix probably could raise its price to at least $15 without losing many of its subscribers. For those reasons, Netflix is now in the Oakmark portfolio.

So, is Netflix hurting its shareholders by underpricing its product? We don’t think so. Like many network-effect businesses, scale is a large competitive advantage for content providers. Scale creates a nearly impenetrable moat for new entrants to cross. With more subscribers than any other video service, Netflix can pay more for programming and still achieve the lowest cost-per-subscriber. As shareholders of the company, we are perfectly amenable to Netflix’s decision to forfeit current income to rapidly increase scale.

Because we are value investors, when companies like Alphabet or Netflix show up in our portfolio, it raises eyebrows. Investors and advisors alike are full of questions when investors like us buy rapidly growing companies, or when growth investors buy companies with low P/Es. Portfolio managers generally don’t like to be questioned about their investment style purity, so they often avoid owning those stocks. We believe our portfolios benefit from owning stocks in the overlapping area between growth and value. Therefore, we welcome your questions about our purchases and are happy to discuss the shortcomings of using P/E ratio alone to define value.

Book Review: Pitch the Perfect Investment

Pitch the Perfect Investment, by two money managers who have also taught for many years at Columbia University’s Graduate Business School, can stop small caliber bullets or deflect a vicious sword blow with its heavy-gloss 496 color pages.  Bad jokes aside, is the book worth the $30+ for its intended audience, young professionals seeking an investment career or can other readers gain investing insights?

FYI: I previously mentioned here Sept. 6th 2016, Pitch the Perfect Investment and Sept. 21, 2017 Pitch the Perfect Investment

Slide presentation:170926_Fordham_LC_final

The authors synthesized many academic publications for the reader to understand the subtleties behind concepts like the Wisdom of Crowds, market efficiency, behavioral finance, and risk into clearer language.  This book with its colorful diagrams can help you grasp the theory of a discounted cash flow model or “DCF”; DCFs are used throughout the book because as the authors say, “all valuation is at the core a DCF, either explicitly or implicitly, whether they (analysts and portfolio managers) admit it or not.”   Of course, it is a given that the young analyst can gain his or her own company and industry expertise so as to insert reasonable assumptions into the DCF model.

Investing is simple but not easy some say. This book provides the simple concepts in a colorful, insightful way, but you have to do the hard part—scratch out a variant perception while competing with many other professionals. Sobering.

The reader is taken through the basics of valuing an asset, a business, how to evaluate competitive advantage and value growth with simple examples (The Lemonade Stand).   The authors drive home the importance of differentiating between nominal growth and profitable growth.  Growth without competitive advantage earning a return above its cost of capital is useless or worse. Certainly, all investors must grasp those concepts.  Every page is festooned with color cartoons, diagrams, tables and graphs.  This is a visual text.

The most interesting part of the book for me was the Chapter 6, The Wisdom of Crowds.  As Buffett says, “You must know two things as an investor: how to value a business, and how to think about prices.”  If I can paraphrase correctly, the Wisdom of Crowds with an adequate amount of domain-specific knowledge and diverse views acting independently from each other on disseminated information will be a force to push price towards efficiency or intrinsic value.  My respect for market efficiency and the person on the other side of the trade from me was reinforced.  If you gain anything from this book, understand that earning an investment edge or variant perception is EXTREMELY difficult and rare.   The authors may have intentionally driven home their point with their example of Cloverland Timber Company.

In their example, the analyst had the domain expertise to notice a line in the financial statement that the Cloverland was undercutting its forests, then satellite imagery was used to assess the quality of the asset and arrive at a more accurate valuation than the market’s current estimate.  The information is available but not publicly disseminated.   I wonder how many analysts/portfolio managers have the time, energy, money, or inclination to go this extra mile?  If you are this able, then you deserve alpha.   What are the implications?

If diverse individuals with independent thoughts are required to have the “Wisdom of Crowds” operate effectively, how will investment firms with their hordes of MBAs and CFAs all taught the same concepts, reading the same newspapers, magazine, research reports, and attending the same investment conferences arrive at non-consensus conclusions often–or ever?

The Wisdom of Crowds gives you an understanding of how prices are set under normal conditions when the forces of darkness and “Mr. Mayhem” (cartoon figure in the book using a magnet to pull prices away from market efficiency; he is the guy you need to spot quickly) are not strong enough to pull prices sufficiently away from intrinsic values.  In other words, behavioral finance is complementary to efficient markets.  One can then recognize when the Wisdom of Crowds becomes the Madness of Crowds.  For an understanding of how prices are set by individuals in a free market, go to pages 79-185 in Man, Economy, and State by Murray Rothbard (Google: Man, Economy, and State.pdf) which has an analysis of how individuals set prices through direct exchange.

Another valuable chapter in the book is Chapter 9, How to Assess Risk.  When investors confuse uncertainty (unknowns) with risk (losing money), then opportunity may appear.

Paul Sonkin, one of the authors, gives sobering advice to students who dream of becoming money managers.  Page 151: “I’m not trying to discourage you from pursuing your dreams, but you should do it with your eyes open.  Do it because you love analyzing companies, not to make a quick buck. And, if your goal is to outperform the market, keep in mind how difficult it has been in the past and the fact that it will only be more challenging in the future.” Those are true words.  The investing profession may end up like acting.  Only the crazy brave will pursue.

Once you have finished Section 1, The Perfect Investment, you then learn how to “Pitch” the Perfect Investment.   Assuming you are diligent enough to acquire the information, assess risk, identify an actual mispricing, and know the catalyst, then convincing another of the merits of your investment should be the easy part.  Unfortunately, too many do not provide a convincing case for the merits of their investment.   An example, of a devastatingly compelling case: The truth shall set you free (liar, liar)

The authors lay out a framework below in this example:

Value or What Can I Make:  Market price is $90 but the stock is worth $140—time horizon is less than 18 months.

Catalyst: Or Who else will figure this out:  Activist with a good track record is pushing for a sale.

Mispricing: The activist did an independent appraisal which the market is unaware of showing a substantially higher value than the company appraisal.  Also, the presence of the activist does not appear to be priced into the stock.  The market is unaware of the activist or does not think he will be successful.

Downside:  Limited. Timberland is a hard asset.

For another example of a forceful investment case with an implied catalyst: Other People’s Money Does Danny Devito provide a strong case? Does he show how much one can make, lose, what is the market missing, and the catalyst?

If you truly have a variant perception, then this is usually your reception: Michael Burry’s Variant Perception

And, only if you are right, and you make the decisions can you present this way: Michael Burry’s Investors  If you read the book, The Big Short, ironically you know that Michael Burry was not making a macro bet, but on the impossibility of individual mortgage holders to make their mortgage payment when asset prices decline and/or interest rates reset higher.

An investment edgeThere are only three ways to gain an edge

In summary, while I do not agree with the book-jacket blurb:

Mr. Nicholas Gallucio, CEO of Teton Advisors, who said, “In this era of hyper-competition on Wall Street ……even the smallest edge can make the difference between success and failure. Pitch the Perfect Investment will give the professional investor that edge.”  I do believe the book is worth $30 for a beginning and intermediate investor who wants to refine their understanding of key investment concepts and to review how to make clear and convincing investment pitches.   Even if an investor does not have a boss to pitch to, the investor should always write down a succinct investment case for each investment.

Remember, I’m biased. I’m a cheapie who went Dutch on his honeymoon, charged an entrance fee, and had a cash bar.  Sure, I made a profit, but the divorce cost a fortune.  Perhaps, I confused price with value.

PS: Graham and Dodd Oct 2017

No Price Discovery Then No Markets; A Reader’s Question

Has the meteoric rise of passive investing generated the “greatest bubble ever”?
The better we understand the baked-in biases of algorithmic investing, the closer we can come to answers.

 

The following article was originally published in “What I Learned This Week” on June 15, 2017. To learn more about 13D’s investment research, visit website.     https://latest.13d.com/tagged/wiltw

In an article for Bloomberg View last week titled “Why It’s Smart to Worry About ETFs”, Noah Smith wrote the following prescient truth: “No one knows the basic laws that govern asset markets, so there’s a tendency to use new technologies until they fail, then start over.” As we explored in WILTW June 1, 2017, algorithmic accountability has become a rising concern among technologists as we stand at the precipice of the machine-learning age. For more than a decade, blind faith in the impartiality of math has suppressed proper accounting for the inevitable biases and vulnerabilities baked into the algorithms that dominate the Digital Age. In no sector could this faith prove more costly than finance.

The rise of passive investing has been well-reported, yet the statistics remain staggering. According to Bloomberg, Vanguard saw net inflows of $2 billion per day during the first quarter of this year. According to The Wall Street Journal, quantitative hedge funds are now responsible for 27% of all U.S. stock trades by investors, up from 14% in 2013. Based on a recent Bernstein Research prediction, 50% of all assets under management in the U.S. will be passively managed by early 2018.

In these pages, we have time and again expressed concern about the potential distortions passive investing is creating. Today, evidence is everywhere in the U.S. economy — record low volatility despite a news cycle defined by turbulence; a stock market controlled by extreme top-heaviness; and many no-growth companies seeing ever-increasing valuation divergences. As always, the key questions are when will passive strategies backfire, what will prove the trigger, and how can we mitigate the damage to our portfolios? The better we understand the baked-in biases of algorithmic investing, the closer we can come to answers.

Over the last year, few have sounded the passive alarm as loudly as Steven Bregman, co-founder of investment advisor Horizon Kinetics. He believes record ETF inflows have generated “the greatest bubble ever” — “a massive systemic risk to which everyone who believes they are well-diversified in the conventional sense are now exposed.”

Bregman explained his rationale in a speech at a Grant’s conference in October:
“In the past two years, the most outstanding mutual fund and holding- company managers of the past couple of decades, each with different styles, with limited overlap in their portfolios, collectively and simultaneously underperformed the S&P 500…There is no precedent for this. It’s never happened before. It is important to understand why. Is it really because they invested poorly? In other words, were they the anomaly for underperforming — and is it reasonable to believe that they all lost their touch at the same time, they all got stupid together? Or was it the S&P 500 that was the anomaly for outperforming? One part of the answer we know… If active managers behave in a dysfunctional manner, it will eventually be reflected in underperformance relative to their benchmark, and they can be dismissed. If the passive investors behave dysfunctionally, by definition this cannot be reflected in underperformance, since the indices are the benchmark.”

At the heart of passive “dysfunction” are two key algorithmic biases: the marginalization of price discovery and the herd effect. Because shares are not bought individually, ETFs neglect company-by-company due diligence. This is not a problem when active managers can serve as a counterbalance. However, the more capital that floods into ETFs, the less power active managers possess to force algorithmic realignments. In fact, active managers are incentivized to join the herd—they underperform if they challenge ETF movements based on price discovery. This allows the herd to crowd assets and escalate their power without accountability to fundamentals.

With Exxon as his example, Bregman puts the crisis of price discovery in a real- world context:

“Aside from being 25% of the iShares U.S. Energy ETF, 22% of the Vanguard Energy ETF, and so forth, Exxon is simultaneously a Dividend Growth stock and a Deep Value stock. It is in the USA Quality Factor ETF and in the Weak Dollar U.S. Equity ETF. Get this: It’s both a Momentum Tilt stock and a Low Volatility stock. It sounds like a vaudeville act…Say in 2013, on a bench in a train station, you came upon a page torn from an ExxonMobil financial statement that a time traveler from 2016 had inadvertently left behind. There it is before you: detailed, factual knowledge of Exxon’s results three years into the future. You’d know everything except, like a morality fable, the stock price: oil prices down 50%, revenue down 46%, earnings down 75%, the dividend-payout ratio almost 3x earnings. If you shorted, you would have lost money…There is no factor in the algorithm for valuation. No analyst at the ETF organizer—or at the Pension Fund that might be investing—is concerned about it; it’s not in the job description. There is, really, no price discovery. And if there’s no price discovery, is there really a market?”

 

We see a similar dynamic at play with quants. Competitive advantage comes from finding data points and correlations that give an edge. However, incomplete or esoteric data can mislead algorithms. So the pool of valuable insights is self-limiting. Meaning, the more money quants manage, the more the same inputs and formulas are utilized, crowding certain assets. This dynamic is what caused the “quant meltdown” of 2007. Since, quants have become more sophisticated as they integrate machine learning, yet the risk of overusing algorithmic strategies remains.

Writing about the bubble-threat quants pose, Wolf Street’s Wolf Richter pinpoints the herd problem:

“It seems algos are programmed with a bias to buy. Individual stocks have risen to ludicrous levels that leave rational humans scratching their heads. But since everything always goes up, and even small dips are big buying opportunities for these algos, machine learning teaches algos precisely that, and it becomes a self-propagating machine, until something trips a limit somewhere.”

As Richter suggests, there’s a flip side to the self-propagating coin. If algorithms have a bias to buy, they can also have a bias to sell. As we explored in WILTW February 11, 2016, we are concerned about how passive strategies will react to a severe market shock. If a key sector failure, a geopolitical crisis, or even an unknown, “black box” bias pulls an algorithmic risk trigger, will the herd run all at once? With such a concentrated market, an increasing amount of assets in weak hands have the power to create a devastating “sell” cascade—a risk tech giant stocks demonstrated over the past week.

With leverage on the rise, the potential for a “sell” cascade appears particularly threatening. Quant algorithms are designed to read market tranquility as a buy-sign for risky assets—another bias of concern. Currently, this is pushing leverage higher. As reported by The Financial Times, Morgan Stanley calculates that equity exposure of risk parity funds is now at its highest level since its records began in 1999.

This risk is compounded by the ETF transparency-problem. Because assets are bundled, it may take dangerously long to identify a toxic asset. And once toxicity is identified, the average investor may not be able to differentiate between healthy and infected ETFs. (A similar problem exacerbated market volatility during the subprime mortgage crisis a decade ago.) As Noah Smith writes, this could create a liquidity crisis: “Liquidity in the ETF market might suddenly dry up, as everyone tries to figure out which ETFs have lots of junk and which ones don’t.”

J.P. Morgan estimated this week that passive and quantitative investors now account for 60% of equity assets, which compares to less than 30% a decade ago. Moreover, they estimate that only 10% of trading volumes now originate from fundamental discretionary traders. This unprecedented rate of change no doubt opens the door to unaccountability, miscalculation and in turn, unforeseen consequence. We will continue to track developments closely as we try and pinpoint tipping points and safe havens. As we’ve discussed time and again with algorithms, advancement and transparency are most-often opposing forces. If we don’t pry open the passive black box, we will miss the biases hidden within. And given the power passive strategies have rapidly accrued, perpetuating blind faith could prove devastating.

The Greatest Bubble Ever 13D Research   (Sign-up for their updates!)

A Reader’s question that I post below so the many intelligent folks that read this can chip in their thoughts….

The part that confuses me the most is this:

From what I gather, Greenblatt typically calculates his measurement of normal EBITDA – MCX. He then puts a conservative multiple on this, typically 8 or 10 times EBITDA-MCX. He says higher quality companies may deserve 12x or more. He often says something like “this is a 10% cash return that is growing at 6% a year. A growing income is worth much more than a flat income”. He seems to do this on page 309-310 of the notes you sent me  complete-notes-on-special-sit-class-joel-greenblatt_2.

My question is: Greenblatt’s calculation of earnings (EBITDA – MCX) only includes the maintenance portion of capital expenditure. The actual cash flow may be lower because of growth capex. Yet he is assuming a 6% growing income. It seems strange to me that he calculates the steady-state income (no growth capex. Only Maintenance capex), but he assumes that the income will grow. It seems like he is assuming the income will grow 6% but doesn’t incude the growth capex in his earnings calculation. Is it logical to assume that the steady-state earnings will grow, but not deducting the cost of the growth capex from the earnings? 

Answer/reply?………….

 

 

 

Finding Good Capital Allocators; The Problems with Using Sentiment

Finding good capital allocators

Strategic Presentation May 2017b    What would show you that this management team allocates capital well in their resource sector?   Are their actions EXTREMELY rare in the Junior Resource Mining industry?

The Perils of Using Sentiment As a Timing Tool

The limitations of sentiment, revisited

June 12, 2017

In a blog post in March of this year I discussed the limitations of sentiment as a market timing tool. I wrote that while it can be helpful to track the public’s sentiment and use it as a contrary indicator, there are three potential pitfalls associated with using sentiment to guide buying/selling decisions. Here are the pitfalls again:

The first is linked to the reality that sentiment generally follows price, which makes it a near certainty that the overall mood will be at an optimistic extreme near an important price top and a pessimistic extreme near an important price bottom. The problem is that while an important price extreme will always be associated with a sentiment extreme, a sentiment extreme doesn’t necessarily imply an important price extreme.

The second potential pitfall is that what constitutes a sentiment extreme will vary over time, meaning that there are no absolute benchmarks. Of particular relevance, what constitutes dangerous optimism in a bear market will often not be a problem in a bull market and what constitutes extreme fear/pessimism in a bull market will often not signal a good buying opportunity in a bear market.

The third relates to the fact that regardless of what sentiment surveys say, there will always be a lot of bears and a lot of bulls in any financial market. It must be this way otherwise there would be no trading and the market would cease to function. As a consequence, if a survey shows that almost all traders are bullish or that almost all traders are bearish then the survey must be dealing with only a small — and possibly not representative — segment of the overall market.

I went on to write that there was no better example of sentiment’s limitations as a market timing indicator than the US stock market’s performance over the past few years. To illustrate I included a chart from Yardeni.com showing the performance of the S&P500 Index (SPX) over the past 30 years with vertical red lines to indicate the weeks when the Investors Intelligence (II) Bull/Bear ratio was at least 3.0 (a bull/bear ratio of 3 or more suggests extreme optimism within the surveyed group). An updated version of the same chart is displayed below.

The chart shows that while vertical red lines (indicating extreme optimism) coincided with most of the important price tops (the 2000 top being a big exception), there were plenty of times when a vertical red line did not coincide with an important price top. It also shows that optimism was extreme almost continuously from Q4-2013 to mid-2015 and that following a correction the optimistic extreme had returned by late-2016.

Sentiment was at an optimistic extreme late last year, at an optimistic extreme when I presented the earlier version of the following chart in March and is still at an optimistic extreme. In effect, sentiment has been consistent with a bull market top for the bulk of the past four years, but there is still no evidence in the price action that the bull market has ended.

Regardless of what happens from here, four years is a long time for a contrarian to be wrong.   See more at http://www.tsi-blog.com

Lesson? Always place data into context and do not rely on any one piece of information.   Sentiment can be useful as part of an over-all picture of a market or company.

Here is an example of an investor who applies that principle in his OWN method of investing. https://www.thefelderreport.com/2017/05/31/how-a-funny-mentalist-learned-to-avoid-annihilation/

He gained INSPIRATION from his investing heroes but did not try to mimic them.

This analyst of gold doesn’t just use news and sentiment but also fundamentals: https://monetary-metals.com/the-anatomy-of-browns-gold-bottom-report-4-june-2017/

And finally, consider the slow crash: https://mishtalk.com/2017/06/12/buy-the-faangs-baby-slow-torture/#more-46281

Stories and Numbers

I don’t always agree with Dr. Damodaran, but the video can help any analyst writing a research report. You have to link your story to numbers. He mentions that he prefers to teach history majors how to value than teach engineers how to be creative/tell a story with their numbers.

The mistake Prof. Damodaran made on Vale was not normalizing the biggest cyclical boom in iron ore prices in the past five hundred years.

Follow the discussion on valuing UBER.    This is a good exercise for valuing platform/network companies: Damordaran on UBER FiveThirtyEight

See more:

  1. http://pages.stern.nyu.edu/~adamodar/
  2. http://aswathdamodaran.blogspot.co.uk/

Global Warming? https://youtu.be/SXxHfb66ZgM

Core Labs (CLB) So What is It Worth? Is Einhorn Right?

One trick that Warren Buffett uses when he first looks at a company is NOT to look at the price OR another person’s opinion.  He doesn’t want price to influence his valuation of the company, and he seeks his own counsel.

Why don’t we test our valuation skills and do a valuation of Core Labs (CLB) with the data below.

  1. CLB VL 2015
  2. CLB VL 2017
  3. Core Labs CLB 2016_annual_report
  4. CLB 2017_1q_10q

Do your own work BEFORE looking at the post below.  See how you compare or differ Einhorn’s short thesis on CLB.    Do you differ?   Why and how.

 —
Whitney Tilson: CLB and HHC

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CSInvesting: I suggest subscribing.

——————————-

1) I (Whitney Tilson) attended the always-excellent Ira Sohn conference on Monday and, as often happens, Bill Ackman and David Einhorn stole the show with two outstanding, incredibly-well-researched ideas, long Howard Hughes (which has been one of my largest positions since it was spun out of GGP when it emerged from bankruptcy after the credit crisis) and short Core Laboratories (CLB), respectively.

They have posted their presentations here:

  • HHC
  • CLB   Here is Einhorn’s short thesis on CLB (Core Labs)

If you’re interested in learning more about HHC (and seeing the incredible development underway at South Street Seaport), the company is hosting its first investor day there a week from today, Wed. 5/17 from 8:00am to 1:30pm. Email Tracey.Wynn@howardhughes.com to register. I hope to see you there.

As for CLB, it doesn’t happen very often – maybe once every two years – but sometimes I (Whitney Tilson) see (at a conference) or read (on something like ValueInvestorsClub) an investment thesis that is so compelling and blindingly obvious that I immediately put the position on – which is what I did on Monday just after Einhorn’s presentation. Check it out for yourself – it’s that good. A highly cyclical company masquerading as a secular growth story – trading at nearly 9x REVENUES!


Must see video on indexeshttps://vimeo.com/216016883/10b948b174

I highly suggest you view the video—Yeah, there is hope for value investors.

Hedge Fund Quiz: Economies of Scale

Economies of scale.

If costs per unit decline as volume increases, because fixed costs make up a large share of total costs, then even with the same basic technology, an incumbent firm operating at large scale will enjoy lower costs than its competitors.

Edison Schools IPO’d at $18 per share and now it trades near $14.   Your boss runs in and throws the Edison School’s 2001 annual report on your desk and a top-rated analyst’s report on Edison.   “Get back to me in thirty minutes on what should we do: Buy a boat-load of stock, buy some, buy a little, short or stand aside?”  Your Boss says that management owns a lot of stock along with “smart” money.

You glance at the analyst report:

Greg Capelli, MBA, at Credit Suisse First Boston issued a $30 dollar price target in his fifty-page report.   Capelli says that Edison Schools is extremely undervalued because of “first-mover advantage” and “INCREASING OPERATING LEVERAGE THROUGH ECONOMIES OF SCALE.”

As a refresher you whip out Greenwald’s Competition Demystified:

LOCAL CHAMPIONS

In an increasingly global environment, with lower trade barriers, cheaper transportation, faster flow of information, and relentless competition from both established rivals and newly liberalized economies, it might appear that competitive advantages and barriers to entry will diminish. The fate of once powerful American firms in industries like machine tools (Cincinnati), textiles (Burlington Industries, J. P. Stevens), and even automobiles (Chrysler, GM, and Ford) seems to support this position. Either profits have shrunk or companies have disappeared entirely under the onslaught of imports. But this macro view misses  one essential feature of competitive advantages—that competitive advantages are almost always grounded in what are essentially “local” circumstances.

Consider the history of Wal-Mart, one of the greatest economic success stories of the late twentieth century. The retail business, especially discount retailing, is not an industry with many trade secrets or rare skills. The practices for which Wal-Mart is known, like “everyday low prices” and efficient distribution, are hardly proprietary technologies, impossible for other firms to duplicate. Yet Wal-Mart has successfully dominated many, although not all, of the markets in which it competes. The way in which it achieved this position is instructive.

Wal-Mart began as a small and regionally focused discounter in a part of the country where it had little competition. It expanded incrementally outward from this geographic base, adding new stores and distribution centers at the periphery of its existing territory. The market that it dominated and in which it first enjoyed competitive advantages was not discount retailing in the United States, but discount retailing within a clearly circumscribed region. As it pushed the boundaries of this region outward, it consolidated its position in the newly entered territory before continuing its expansion. As we shall see, when it moved too far beyond its base, its results deteriorated.

An Analyst ALWAYS ASKS:

OK, now you dig in quickly to the Edison Schools AR_2001

What do you say to your boss?  Your comments should be no more than a sentence or two of explanation backed up by a few simple calculations.   Besides the financials, what do you point out in the annual report?   Take no more than twenty minutes.   You go immediately to the important data and disregard the rest.

Address Capelli’s “First Mover Advantage” comment.

Next week, I will post analysis.

AFTER you have answered, you can see the future for investors in Edison:  https://youtu.be/QUYKSWQmkrg

UPDATE 4/17/2017

ETF Insanity is destroying price discovery–opportunity will return.

https://vimeo.com/209940152/f2154e4d3d

Part 1 of this post: http://csinvesting.org/2017/04/20/edison-schools-cs-on-econ-of-scale-part-ii/

An Example of the Analyst Course

It is 2006, and you want to know the true sustainable free cash flow of Mohawk because you love sleepy, mundane industries that are slowly growing. Why? Because people love to buy lottery tickets and glamour so you “go where they ain’t.”

So what is the difference between Mohawk’s GAAP earnings per share and your estimate of FREE CASH FLOW per share?    Use cash flow from operations and maintenance capex (which you need to roughly estimate). Be roughly right, not precisely wrong.

Can you explain the difference?   What is going on with Mohawk and the industry?   Note the ten-bagger or 25% CAGR for almost ten years (move over Buffett) from the 2009 lows.  Surprising or not?    MHK-2006 AR FCF Analysis.   Can you use this as a way to find other ten-baggers?

Of course, the above question is not hard for you since you have already read this 412 page book several times: Wiley Creative Cash Flow Reporting.  You do not have needed to read that book to answer the question. Use common sense and a small bit of accounting knowledge like (amortization of goodwill-hint!)

This an example of how the analyst course would teach. You have to do many case studies to practice learning concepts like free cash flow.  Like flying and sex, you have to practice.

Update: April 11, 2017: The price you pay

If you pay too much even for a great business, then you will have poor returns.

Take the three Value-Lines of Coke (KO).   VL KO and note the $80 + plus price, then track its free cash flow (as calculated by Value-Line which is After-tax earnings plus depreciation + amortization then minus capital expenditures), then earnings.   Track the price and those metrics: KO_VL_Jan 2013 and Ko_VL 2017.   You will see that on average cash flows and earnings and dividends rise from 1998 until 2017, yet returns have been just OK.   Paying too high a price hurts your total returns even with a strong, stable business like Coke. Investors were extremely optimistic over Coke’s growth prospects.  If you held Coke for a long time, your return would equal the company’s long-term return on equity.

Also, Buffett’s non-controlled public investments have generally lagged the S&P 500 Berkshire Hathaway AR 2016.

Good luck.    Those who do not provide the correct answer will have to spend time with my ex-wife: