The Wrong Question

Where I work, we are not in the business of stock picking. Nonetheless, clients sometimes ask us to weigh in on individual equities. Often these questions come in the form of “should I buy Oil Company A or Pharma Company B?”

This is the wrong question. Even if you are proceeding from the assumption that stock picking is a worthwhile endeavor, it is the wrong question.

For starters, underlying this question is the assumption that at least one of the two stocks is a good investment. However, it is quite possible (even likely) that neither is a good investment. The entire exercise proceeds from flawed premises.

Unsophisticated investors almost invariably generate investment ideas based on availability bias. They don’t actively seek out non-consensus opportunities. They gravitate toward what they already know, or information that is readily available in the media or in stock picking newsletters (shudder). This in turn leads to “research” driven by confirmation bias.

People who grew up with a grandparent buying blue chip stocks for them like blue chips. People who work in tech like tech. Ditto for aerospace. And so on. This is also why so many people own large amounts of employer stock in their 401(k)s despite the vast body of personal finance literature advising otherwise. No ex ante consideration is given to risk management or opportunity cost–what the investor could earn in a broadly diversified equity portfolio.

Another problem with the question “should I buy Oil Company A or Pharma Company B?” is it completely ignores the issue of time horizon. Do you plan on holding this stock forever? For three years? For one year? For a couple of quarters? A day? As Cliff Asness is fond of saying, you don’t want to be a momentum investor on a value investor’s time horizon.

I consider myself a value-oriented investor, but I would bet on momentum over value for short time periods. In response to a question about performance evaluation, I once told a colleague I would be comfortable owning a certain mutual fund for the next 20 years, but not for the next 5. My statement was met with uncomfortable silence. From his reaction you would think I had spoken a koan. (I guess maybe I did)

I could go on by delving into financial statement analysis, but that’s beside the point. By using “should I by Oil Company A or Pharma Company B?” as a jumping off point you are skipping steps. You are making a security selection decision divorced from any larger context or purpose.

In other words, you are gambling.

Now, the purpose of this blog is not to admonish people for gambling. I enjoy the occasional negative expectation game as much as the next person. However, it is hazardous to your wealth to conflate gambling and investing.

Is your favorite marijuana stock a fraud?

Woman_smoking_marijauanaThe cannabis industry is the bane of my existence as an investment analyst. It is a wretched hive of scum, villainy and penny stock fraud, and yet the “legalization story” is so compelling that retail investors are drawn to the space like moths to a flame. This is lottery ticket investing at its finest only many clients don’t seem to realize that the lottery is rigged.

Fortunately it is pretty straightforward to pick these things off from an analytical point of view. As a public service I will share some tips that may be helpful in avoiding obviously fraudulent stocks. Most of these can be generalized to other investment opportunities.

Stock Fraud Red Flags

#1: The stock is a microcap/penny stock (trades on the OTC bulletin board or pink sheets). Penny stocks are riddled with fraud. There are a lot of them and the SEC doesn’t have the time and resources to run around investigating every shady operator in existence. Trading volumes are usually thin which means the prices are easy for insiders and assorted other scumbags to manipulate through a variety of schemes (the most common being the pump and dump). Not all microcaps are frauds but they are much riskier fare for unsophisticated investors.

#2: Thin trading volumes and/or a price history showing huge spikes and crashes. This can be indicative of market manipulation.

#3: Screwy financials. Shady penny stocks tend to share some common attributes in their financials. Here are some (in no particular order):

  • Seemingly large mismatches in revenues and expenses (e.g. revenues of a couple hundred thousand dollars and operating expenses of several million dollars) that don’t seem aligned with investment programs, R&D or product ramps.
  • Inadequate capitalization, such as a few hundred or a few thousands of dollars in cash in a bank account and no other assets, but grand ambitions of market penetration and dominance.
  • Large accumulated shareholder deficits in place of shareholder equity.
  • Substantial liabilities associated with conversion options on convertible debt. This type of financing is variously referred to as “toxic debt” and “death spiral financing.” It can also be used to perpetrate a pump and dump scheme.
  • Going concern flags from auditors.
  • Material weaknesses in internal controls flags from auditors.
  • Business summaries that discuss a past history of operating as different companies or in entirely different industries. One way these frauds perpetuate themselves is by repeatedly merging and reverse merging shell companies to operate in “hot” industries like cannabis.
  • Large volumes of related party transactions. It is particularly egregious if cash is flowing from a public entity to a private entity controlled by insiders.
  • Limited independent oversight of the executive. Some of these stocks will have a CEO who is also the CFO and the sole board member (or variations on that theme). This could be a sign of a lean startup operation but it is also indicative of limited risk control–especially with regard to cash controls.
  • Patterns of late SEC filings or requests for clarification on filing data from SEC staff.

#4: Executives have past association with crashed penny stocks. This is maybe the most significant red flag you can find. Most shady operators in this space are professional or semi-professional scumbags and so they move from scam to scam to scam. Google is your friend here (Googling NAME + SEC can be remarkably fruitful).

#5: Screwy online presence. Fraudulent companies will have potemkin websites designed to provide a veneer of legitimacy. But if you dig a little deeper there are weird inconsistencies such as half-built pages, non-functioning forms or sham social media accounts that are never updated.


#6: The stock is pitched almost entirely on total addressable market (TAM). Lots of people like to smoke weed. I get it. There are hundreds of billions of dollars up for grabs. But would legalized marijuana not be a commodity product? Beyond the initial gold rush fervor it’s not clear to me, why, in the long run, the weed business should be so great viewed through the lens of returns on capital. Competition will be fierce if and when cannabis is legalized. The market will be flooded with entrants who will compete away the margins. And what’s to stop some super well-capitalized adjacent player like Philip Morris from rapidly entering the weed space and ramping up capacity? I may be completely wrong about the details. My point is simply that the economics are more complex than “if you build it, they will come.”


I will conclude with a point I cannot emphasize enough. I will put it in big, bold, all-cap type for extra emphasis:


In a past life I did a little freelance journalism. I have friends with years of experience in journalism, including business journalism. Believe me when I tell you that journalists are not paid to do proper due diligence.

In most cases the journalist is assigned a story by an editor. The editor says “go talk to this weed guy, weed is hot right now.” The journalist dutifully goes and interviews the weed guy and reports on what the weed guy has to say. Most journalists are not looking into the weed guy’s background and they are certainly not digging into the weed guy’s company’s financials. They may not even be comfortable interpreting financial statement data. In fact, they may never even set foot on the business premises (budgets are tight in media these days). So the journalist will see what the weed guy wants him to see. This is all well and good if you are in the entertainment business but not so much if you are an investor.

John Hempton summarizes the issue neatly in an old post dissecting the Sino Forest fraud (Sino Forest was a Chinese timber company that fabricated acreage). He writes:

Where I am less sympathetic is to Paulson’s statements that staff went to see the operations (and hence they judged they were real) and also to the line that they did a thorough review of the financial statements.

If you go see Sino Forest’s operations you will see what Sino Forest wants to show you. They will show you trees. You can’t tell whether that is 5 thousand hectares or 500 thousand hectares. Seeing trees does not answer the question. There is no point looking at things that are not going to tell you anything anyway – and so Paulson’s staff member wasted his time looking. That is an amateur-hour mistake.

If you are going to look at the operations (and it is often worthwhile) then do the work properly and look through the eyes of a competitor or a customer or a supplier. And find them yourself rather than talk to sympathetic ones supplied by the management.

When management say good things about themselves that provides no actionable investment information. When management say good things about a competitor that is golden. When suppliers you have found yourself say good things about a company that is useful. When management say bad things about their business that is useful.

Speaking to management and hearing good things about them said by them does not help in investment and hence does not constitute actionable analysis.

Not all penny stocks are frauds. Nor are all marijuana businesses. However, in my experience retail investors often struggle to distinguish between compelling narratives and attractive investment opportunities.

The reason for this boils down to availability bias. Retail investors assume that the information that is readily available to them is also the most useful. In reality it is just the opposite. The rosier outlook, and the easier it is to find information confirming that view, the more skeptical you need to be with your due diligence. It is in the fraudster’s interest to make sure positive information is widely and readily available.

Big Piles of Money

Source: Morningstar/Pitchbook
Private equity is sitting on a huge pile of money. The line of investors runs out the door. In fact, private equity today reminds me a lot of hedge funds circa 2002-2005. It is the hot space. Flows have gone bananas. But will all of this money find a good home?

I am skeptical.

It is now a commonly held view that strong asset flows led to diminishing returns for hedge fund strategies over time. In general, too much money chasing limited opportunities is never a recipe for exceptional investment performance. What would otherwise be good investments become bad investments and what would otherwise be marginal investments become terrible investments.

As Seth Klarman puts it, everything is a buy at one price, a hold another and a sell at another. When a lot of money chases a limited opportunity set, prices rise. This may be good for today’s sellers but not so much for today’s investors.

I empathize with private equity investors because while there is a whole lot of money out there looking for a good home there are simply not many good homes available.

It is also now a commonly held belief that all the good companies stay private. I am not sure that is true when you have an enormous private company like Uber engaging in bizarre valuation gymnastics to manipulate its paper value. I am more inclined to argue that across the board, high valuations and easy money have turned what might otherwise be good companies into bad investments. Meanwhile, certain businesses go public that probably should not even exist if investors hope to see their capital again.

The title of this post is a play on the very well done NPR special, “A Giant Pool of Money.” (You should listen to it) The thrust of that piece was that artificially low interest rates drove investors to inflate a speculative bubble in US housing and related financial instruments, which in turn led to the global financial crisis of 2008.

Think how attractive a mortgage loan is to that $70 trillion pool of money.

Remember, they’re desperate to get any kind of interest return. They want to beat that miserable 1% interest Greenspan is offering them. And here are these homeowners paying 5%, 9% to borrow money from some bank. So what if the global pool could get in on that action?

There are problems. Individual mortgages are too big a hassle for the global pool of money. They don’t want to get mixed up with actual people, and their catastrophic health problems, and their divorces, and all the reasons that might stop them from paying their mortgages. So what Mike and his peers on Wall Street did, was to figure out a way to give the global pool of money all the benefits of a mortgage– basically higher yield– without all the hassle and risk.

So picture the whole chain. You have Clarence. He gets a mortgage from a broker. The broker sells the mortgage to a small bank. The small bank sells the mortgage to a guy like Mike at a big investment firm on Wall Street. Then Mike takes a few thousand mortgages he’s bought this way, he puts them in one big pile.

Now he’s got thousands of mortgage checks coming to him every month. It’s a huge monthly stream of money, which is expected to come in for the next 30 years, the life of a mortgage. And he then sells shares of that monthly income to investors. Those shares are called mortgage-backed securities. And the $70 trillion global pool of money loved them.

The above was reported in May 2008.

To date the underlying issue has not been resolved. As I write this the yield on the 10-year Treasury sits at 2.32%. Today the giant pool of money is not flowing into US housing and mortgage bonds. Instead it flows into private equity, high yield debt, leveraged loans and technology stocks. Plenty of it is eyeing cryptocurrencies.

But fundamentally it is doing the same thing it did in the mid-2000s. It is chasing returns.


On Spurious Precision (With Special Guest Seth Klarman)

One thing I love about investing is that, barring insider information and market manipulation, you must make decisions based on imperfect information. As a high school student I did not care for mathematics (an attitude I profoundly regret as an adult). Reflecting on this, much of what I disliked about mathematics had to do with the fact that it was taught as an exercise in memorization and regurgitation. Many kids I knew who excelled at high school mathematics were simply prolific memorizers of formulas. The whole exercise seemed rather silly to someone who was more creatively inclined. Of course, I have since learned that “real” mathematics couldn’t be any further from rote memorization.

Anyway, financial markets do not reward memorizers of formulas. There is too much uncertainty. Too much change. Too much randomness. Below is an excerpt from Seth Klarman’s incomparable Margin of Safety to elaborate:

How Much Research and Analysis Are Sufficient?

Some investors insist on trying to obtain perfect knowledge about their impending investments, researching companies until they think they know everything there is to know about them. They study the industry and the competition, contact former employees, industry consultants, and analysts, and become personally acquainted with top management. They analyze financial statements for the past decade and stock price trends for even longer. This diligence is admirable, but it has two shortcomings. First, no matter how much research is performed, some information always remains elusive; investors have to learn to live with less than complete information. Second, even if an investor could know all the facts about an investment, he or she would not necessarily profit.

This is not to say that fundamental analysis is not useful. It certainly is. But information generally follows the well-known 80/20 rule: the first 80 percent of the available information is gathered in the first 20 percent of the time spent. The value of in-depth fundamental analysis is subject to diminishing marginal returns.

Information is not always easy to obtain. Some companies actually impede its flow. Understandably, proprietary information must be kept confidential. The requirement that all investors be kept on an equal footing is another reason for the limited dissemination of information; information limited to a privileged few might be construed as inside information. Restrictions on the dissemination of information can complicate investors’ quest for knowledge nevertheless.

Moreover, business information is highly perishable. Economic conditions change, industries are transformed, and business results are volatile. The effort to acquire current, let alone complete information is never-ending. Meanwhile, other market participants are also gathering and updating information, thereby diminishing any investor’s informational advantage.

David Dreman recounts “the story of an analyst so knowledgeable about Clorox that ‘he could recite bleach shares by brand in every small town in the Southwest and tell you the production levels of Clorox’s line number 2, plant number 3. But somehow, when the company began to develop massive problems, he missed the signs… .’ The stock fell from a high of 53 to 11.'”

Although many Wall Street analysts have excellent insight into industries and individual companies, the results of investors who follow their recommendations may be less than stellar. In part this is due to the pressure placed on these analysts to recommend frequently rather than wisely, but it also exemplifies the difficulty of translating information into profits. Industry analysts are not well positioned to evaluate the stocks they follow in the context of competing investment alternatives. Merrill Lynch’s pharmaceutical analyst may know everything there is to know about Merck and Pfizer, but he or she knows virtually nothing about General Motors, Treasury bond yields, and Jones & Laughlin Steel first-mortgage bonds.

Most investors strive fruitlessly for certainty and precision, avoiding situations in which information is difficult to obtain. Yet high uncertainty is frequently accompanied by low prices. By the time the uncertainty is resolved, prices are likely to have risen. Investors frequently benefit from making investment decisions with less than perfect knowledge and are well rewarded for bearing the risk of uncertainty. The time other investors spend delving into the last unanswered detail may cost them the chance to buy in at prices so low that they offer a margin of safety despite the incomplete information.

Valuing A Bitcoin – Part III

Building off yesterday’s post today I will unveil a Bitcoin valuation.

Before we go any further I must emphasize that I am sharing this information as an intellectual exercise and for entertainment purposes only. This is not an investment recommendation and the output of this model should not be used to make investment decisions. You should consult with a financial advisor before making any investment decision. In the interest of full disclosure you should also know that I currently own neither cryptoassets nor exchange traded cryptoasset products (ETPs and ETNs).

The theoretical underpinning from this model is taken from Burniske and Tatar’s book, Cryptoassets. The authors propose adapting the Equation of Exchange (MV = PY) for valuing cryptocurrencies.

What the equation of exchange tells us is that the money supply times the velocity with which money circulates (left side) must equal the price level times real output (right side, a.k.a nominal output). So:

M = Money Supply

V = Velocity of Money

P = Price Level

Y = Real Output

I will apply the model to Bitcoin using data from Many of my inputs will be rounded but I have always believed that perfect is the enemy of good when it comes to investing and valuation in particular. I am not sweating the small stuff. You are welcome to redo the work to two decimal points if spurious precision is your thing.

Anyway, we start with the supply of Bitcoin. This is easy. There will only ever be 21 million Bitcoins (unless of course the code is changed and that is a governance issue for the time being not a valuation issue). To be conservative I will assume all 21 million Bitcoin are in circulation for the valuation calculation.

The velocity of Bitcoin is a bit fuzzier but I can try to approximate the number using Bitcoin transaction data. According to the data Bitcoin transaction volumes are fairly stable oscillating around 200,000. We can annualize this by multiplying by 365 which equals about 73,000,000. We divide 73,000,000,000 by the current Bitcoin supply of about 16 million to get a velocity of about 4.56.

Price in USD is the variable we solve for. So we will pass over it for now.

With output we make a small adjustment and use output in USD terms as it will be easier to place our assumptions in context that way. This is about $1bn per day currently which we can annualize to about $365bn. That is estimated output today. What we need for our model is to also estimate the output at some point in the future. For the sake of this exercise let’s say in five years we think the USD equivalent transaction output for the Bitcoin network will be $1tn. This is a critical variable and some readers may think I am being overly conservative. Maybe so but do consider that this represents a compound annual growth rate of 112% a year.

We set up the model as follows:

21,000,000 x 4.56 = P ($1,000,000,000,000)

Solve for P using basic algebra and you get about .000096 BTC/USD. To make this number intelligible we take the reciprocal 1/.000096 to get USD/BTC which (using a spreadsheet for spurious precision) is about $10,443. That is a the estimated value of one Bitcoin five years from now.

For the final step we simply discount this price 5 years at our required rate of return. Since discount rate estimation is a pain and something of a guessing game in the best of times I like to simply choose a desired hurdle rate. For an asset like BTC I think 30% is reasonable given the risks and the immaturity of the asset class.

So discounting $10,443 for 5 years at 30% I estimate the value of one BTC today at $2,813. A summary of these calculations is included below.

Sources: Burniske & Tatar (model); Myself (calculations & tweaks)

Contrary to what some may think modeling is not about predicting the future. Rather it is about being explicit with your assumptions. This helps you test your assumptions for reasonableness. It also helps you identify the key variables you need to get right. Finally, it helps you build and maintain conviction in the face of market price volatility.

With Bitcoin here are the key variables:

  • How big can it get? -> How much “share” of global transaction volume will it take?
  • How long will it take to get there?
  • To what extent will it be used to transact versus as a store of value? The lower the velocity the more it is being used as a store of value and vice versa.
  • How much reward do you require given the risks?

You might disagree with my results and that is fine. However, I would ask you to consider where our views differ in the context of this model. Is it because you believe Bitcoin will get “bigger” and/or that it will get there “faster”? Is it because you think Bitcoin is less risky than I do?

I hope to update this valuation from time to time as Bitcoin evolves as an asset.

In closing, I would like to once again emphasize:

I am sharing this information as an intellectual exercise and for entertainment purposes only. This is not an investment recommendation and the output of this model should not be used to make investment decisions. You should consult with a financial advisor before making any investment decision. In the interest of full disclosure you should also know that I currently own neither cryptoassets nor exchange traded cryptoasset products (ETPs and ETNs).

Book Review: Cryptoassets: The Innovative Investor’s Guide to Bitcoin and Beyond

Cryptoassets_CoverI pre-ordered this book on Amazon after seeing it mentioned on Josh Brown’s blog, The Reformed Broker. I was intrigued because it purported to be a rigorous treatment of cryptocurrency and cryptoassets written from the perspective of a relatively sophisticated investor.

Burniske and Tatar state their goal was to produce a book that is the equivalent of Benjamin Graham’s Intelligent Investor for cryptoassets. That is kind of like Dennis Rodman saying he wanted to do for rebounds what Michael Jordan did for dunks. To the authors’ credit I think they have done an admirable job of approaching a fast-evolving space in a balanced and rigorous way.


The book is well-organized. It is segmented into three parts: What, Why and How.

What: Discusses the theoretical underpinnings of cryptoassets and provides background information on the history and evolution of several major cryptocurrencies: Bitcoin, Ethereum, Ripple, Monero, Zcash and Dash (I may have omitted a couple). Burniske and Tatar take pains to distinguish between cryptocurrencies, cryptocommodities and cryptotokens.

Why: This section provides an overview of Modern Portfolio Theory (MPT) and the use of mean-variance optimization in constructing an investment portfolio. The authors argue for the inclusion of cryptoassets in an investor portfolio based on their potential to improve overall portfolio efficiency, similar to more “traditional” alternative investments such as hedge funds, private real estate and commodities. I skipped most of this section as I am very familiar with MPT.

How: This section was really what attracted me to the book as it lays out a framework for performing due diligence on a prospective cryptocurrency investment. The authors address issues of custody, valuation and trading, as well as some of the nuances of trading in fragmented markets with the potential for wide fluctuations in trading volumes. The valuation model they float for cryptoassets is more or less the Equation of Exchange (MV = PY or in this case P = MV/Y). One issue I don’t think they adequately address is the issue of reflexivity in the “velocity” of crypto transactions (speculative trading activity drives up network activity which in my view creates a kind of feedback loop).

Who Should Read This Book

Anyone looking for a comprehensive introduction to cryptoassets would benefit from reading this book. It would be particularly useful financial advisors looking to educate themselves in order to address client questions or advisors considering cryptoassets for inclusion in client portfolios. The book is very much written in the language of the financial professional.

Who Should Not Read This Book

This book does not contain any secret sauce for getting rich quick. People who are looking for “hot tips” or “hacks” will be disappointed. While the authors are clearly bullish on the long-terms prospects for cryptoassets, they emphasize the need for investors to educate themselves, conduct thorough due diligence and develop an investment discipline. The due diligence concepts outlined in the book are applicable to any asset class or investment opportunity.

My comments on this book should in no way be taken as a recommendation to buy or sell any cryptoasset. If you are wondering whether you should own cryptoassets as part of your investment portfolio you should consult with a financial advisor who can advise you based on your unique financial circumstances.

Valuing Bitcoin – Part II

In my previous post on valuing Bitcoin I settled on supply/demand balance as the “least-bad” valuation model. I have been thinking more on how one might actual implement this in practice. The supply side is fairly straightforward. There are lots of free calculators that allow you to play with cost assumptions for Bitcoin miners. Now, there are probably going to be places in the world where an astute Bitcoin miner can arbitrage differences in electricity costs. But for now that’s splitting hairs.

The far trickier part is the demand side.

The reason is that while there are lots of use cases for Bitcoin, far and away the most prevalent is speculative trading. Therefore, if you take network activity at face value you are probably missing the fact that there is some reflexivity in those statistics. It’s basically a circular error problem. Speculative trading activity drives up network activity which drives up miner’s costs which causes the equilibrium price to rise. BUT, if speculative trading activity slackens (e.g. Bitcoin is in an asset bubble that deflates in the future) then the reverse will occur on the way down.

So in my view what you need to do is account for potential increases or decreases in speculative trading activity (and other kinds of activity) in your model. To do this you would need data that segments different transaction types.

The trick is finding that data.

As always this is not an investment recommendation. It is written for entertainment purposes only. As my thorough disclosure states very clearly, you should never make any investment decision based on something some random dude writes on the internet. Everything I am saying here could be wrong. In fact it is likely wrong. If you are looking for a recommendation on whether to own Bitcoin or any other cryptocurrency you should consult with a trusted financial advisor.