Alphabet is growing its moat every day – Common 2017 platitude
Advertising is cyclical
A key mistake when looking at current and future valuation multiples, is to assume exponential growth without cyclicality in cyclicals. Slide 27 shows future ratios based on extrapolation of growth. Earnings are not normalized. To be clear, cyclicality is absolutely fine for a high moat / return on capital, secular growing business, it should just be discounted in the valuation!
While the large tech stocks are praised to widen their moat every day, we have to remember that Alphabet is not growing in a vacuum, and indeed its clients (i.e. advertisers) are more cyclical.
Google has been able to grow revenues in the 2009 recession, as it had a long growth runway and superior value proposition
initially, the ROI on Google ads for clients was much higher than conventional ads because of drastically better targeting
superior relative ROI protects against a recession as clients will cut advertising with inferior ROI first
Today, Alphabet grew to a staggering 15% market share of global ad spending (Google and Facebook control a combined >20% today)
as Google becomes more dominant
it is only strategic for Google to steadily capture more of this extra value of online targeting for itself through higher (quasi-monopolist) pricing, and for the relative ROI gap versus offline to come down accordingly
Clients ad budgets are increasingly online, diminishing the ad spending slash cushion that existed in 2009
Google revenue’s sensitivity to a global recession should therefore grow over time as it “becomes” the ad spending market. In other words, in the next recession, Google’s revenue could even shrink.
Using a 10% discount rate, Google’s value decreases 20% if it has a one-off two year setback of flat revenues.
Alphabet is growing its cyclicality every day – TC
While many like to opine that Alphabet is growing its moat every day, I will add that Alphabet is also growing its cyclicality every day by growing their take of corporate costs in the old cyclical world. And that is probably absolutely fine for this business, and its (true) long-term investors. As for short-term oriented investors, hopefully they will exchange their shares at attractive price levels when the cyclicality of this great business rears its head.
For Google ad revenues, 2009 is an afterthought
For total US ad spending, it took five years (!) to recover from the 2007 peak.
Since we view ourselves primarily as practitioners, and our posts have been largely theoretical lately, I thought it would be instructive – and transparent – to disclose portfolio positions. You can expect more discussion on current and future positions soon.
The following are my portfolio positions (TC):
Dart Group plc (DTG)
Wilh. Wilhelmsen Holding (WWIB)
Baoye Group (2355)
Boustead Projects (AVM)
Interactive Brokers (IBKR)
Philly Shipyard (PHLY)
Treasure ASA (TRE)
EAM Solar (EAM)
New York REIT (NYRT)
Prospect Japan Fund (PJF)
And lastly, cash at around 20% of portfolio (up from 1% at the start of the Trump rally)
This paper“Who are the Value and Growth investors” is a great read because it investigates who are the suspects that create the value effect.
[..] we relate the value tilt to household characteristics. Value investors are substantially older, are more likely to be female, have higher financial and real estate wealth, and have lower leverage, income risk, and human capital than the average growth investor. By contrast, men, entrepreneurs, and educated investors are more likely to invest in growth stocks. These baseline patterns are evident in both stock and mutual fund holdings. The explanatory power of socioeconomic characteristics is highest for households that invest directly in at least five companies, a wealthy subgroup that owns the bulk of aggregate equity and may therefore have the greatest influence on prices.
Note from the statistics table that three large explanatory variables are sex, education level “human capital” and immigrant.
Women tend to be value investors, while men and immigrants buy glamour
The academic top growth or “glamour” stock decile historically shows a histogram of returns with more fat tails (see books by Haugen previously linked on this blog). Thus, glamour stocks look more like lottery tickets. I presume this contributes to why women tend to shun glamour, as they have been shown to be more risk averse. This speculation is corroborated by the immigration dummy: immigrants buy more glamour: are they more risk seeking than locals?
Higher education correlates with glamour investing
Maybe we are close to the peak usage of the word moat. The word sounds smart, but it means a million different things to different people. That makes it hard to scientifically test. I am of course not arguing that investing in companies with moats is stupid, as the grandfather of the word Buffett has done phenomenally well. Rather, I am questioning the ability of the majority of moat seekers to outperform, especially when everyone is looking for moats [Bloomberg: Moat is the latest Silicon Valley jargon].
The study shows that smarter people tend to buy high multiple stocks. High multiple stocks are related to companies with explosive/high growth rates and/or high (incremental) returns on capital. Although it was shown empirically that some “quality” factors (such as high and stable historical return on capital) outperform without the use of valuation metrics (Remark 1), the magnitude is marginal versus the value effect.
Hence, for the average smart person, seeking great companies with moats might be a dangerous game when the crowd is paying high multiples.
Indeed, what I think is going on here is an example of Kahneman’s insider view problem. The more people are experts in a field, e.g. investing or technology, the more they are drawn to go against the base rates in their field of expertise. It is ironic that knowledgeable investors might well be aware of the value effect but still find high moat investing to be more intellectually challenging.
A practical advice for investment professionals and myself that seek to do quality investing could be that one should very slowly, and with a lot of self-contempt, evolve toward high moat investing. This might well be what Warren Buffett did over his long life.
A primary focus on staying within one’s circle of competence, a secondary focus on expanding that circle slowly.
Value investing is for old folks
The not often discussed fact that value investing bears lower duration risk (to borrow a term from bond investors) than growth stocks is cited to explain why older people – who will soon draw money for their pension – own more value stocks.
I think this is a great explanation.
Buying high & stable return on capital businesses solely by looking into the rear-view mirror outperforms the market. This means that part of high moat investing can be tested and works. I call this the quantitative moat strategy. There are three caveats though:
Quantitative moat strategy return component: the historical out-performance of this naive rear-view mirror strategy is marginal versus e.g. the value effect
Qualitative moat strategy return component: most returns from buying high moat companies come from outwitting the market in the pool of easily identifiable high ROIC companies. In other words, by having a variant view on magnitude and/or longevity of anomalous ROIC before it reverts back to the mean (see e.g. the book Accounting for Value). This is where moat investingbecomes more art than science.
return detractor: the out-performance of the moat strategy can be washed away, and then some, if one is buying high multiple stocks
I am not sure of the paper’s last conclusion. Based on the observation that employees in more cyclical industries buy more growth stocks, the authors assume owning value is more correlated to key macroeconomic risk factors (as the die-hard efficient market believers do).
The central tenet of the rational approach is that the value premium is compensation for forms of systematic risk.
To the best of our knowledge, our paper is the first to provide direct evidence of hedging demand of any kind in the risky portfolios of households. It also lends support to the link between the value premium and income risk, which has been the subject of a vast asset pricing literature.17 In his Presidential Address to the American Finance Association, Cochrane (2011) develops the following interpretation of the value factor: “If a mass of investors has jobs or businesses that will be hurt especially hard by a recession, they avoid stocks that fall more than average in a recession.” Our results confirm Cochrane’s prediction.
I think the paper by Lakonishok & Shleifer (1994) shows compelling contrary empirical evidence to this cited theory. Lakonishok looks at value versus growth relative performance in the worst economic times (i.e. recessions) to conclude that value is safer in an environment where safety is at a premium.
If we look into Cochrane’s (2011) paper that was cited, we find – beyond an amazing amount of theory – no empirical data for value versus growth in recessions. The only data that is cited in this paper is across asset classes, not within equities. Cochrane cites Fama, French (1996):
Consider Fama and French’s (1996) story for value. The average investor is worried that value stocks will fall at the same time his or her human capital falls. But then some investors (“steelworkers”) will be more worried than average, and should short value despite the premium; others (“tech nerds”) will have human capital correlated with growth stocks and buy lots of value, effectively selling insurance. A two-factor model implies a three-fund theorem, and a three-dimensional multifactor efficient frontier as shown in Figure 17. It is not easy for an investor to figure out how much of three funds to hold.
When we then look at the F&F (1996) paper, we find that it addresses the Lakonishok (1994) “LSV” paper without any data (see page 24 to 26 “The distress premium is irrational”).
Fama responds that the argument that value stocks outperform growth stocks in general recessions is not sufficient to disprove that value is riskier:
In my opinion the burden of hard evidence is on F&F as they contend that value is necessarily more risky than growth. Rather, their answer to LSV as to why value is riskier than growth rests on the non-quantified concept of “relative investor distress”. In other words, the theory that investors care more about downside risk at exactly the time when a diversified value basket under-performs.
Despite the finding that value outperforms in a recession when everyone most likely cares more about downside risk (except perhaps efficient market intelligentsia), F&F respond that this data is not sufficient, and that there might be a multitude of offsetting times that employees/investors in various industries care more about downside risk when value under-performs. It seems that the deus ex machina for F&F is that we just can’t measure subjective distress as this argument is not elaborated on. No empirical data was shown formulating their answer.
I find it quite ironic that the the individual industry distress argument is coming from proponents of CAPM, the theory that stipulates that diversifiable risk should earn no premium. If an investor that works in the cyclical steel industry shuns value stocks in steel companies, wouldn’t it suffice to own a diversified basket of value stocks of different industries to diversify employment risk?
I admire the intellectual honesty of Guy Spier, author of The Education of a value investor. He practices what I would describe as low stress, minimal decision making, low turnover value investing from the Alps.
In his talk at Google he mentions simple but powerful rules he adheres to.
This is the list with my favorites in bold and great open questions in italic.
Stop checking the stock price
If someone tries to sell you something – don’t buy it
Don’t talk to management
Gather Investment Research in the right order
Never buy or sell stocks when the market is open
If a stock tumbles after you buy it, don’t sell it for two years
Don’t talk about your current investments
Classic one: remove the noise, focus on signal
Simple but very powerful: I use it in investing, life. In investing, I think this can be extended to shunning stocks that appear very often in the media, battlefield stocks that lure you to take a stance etc. Attention breeds efficiency in general
Obviously controversial. In any case, while talking to management one should always be acutely aware that CEO’s self-select for salesmanship and a great salesman doesn’t appear to sell something. I think most takeaways from management are more meta-knowledge (traits such as candidness, rationalism)
I think this is a very powerful rule. I interpret this as “don’t change or make any decisions on trading while the market is open” because you’d most probably be using the availability bias against yourself looking at noise
Don’t know if this is a good heuristic
I guess that talking and summarizing your investment thesis makes you fall in love more with a stock. This rule is an interesting one. I interpret “talking” as broadcasting investment theses to random people/clients without a specific aim to get valuable thoughts or feedback. This would be a rule I consider using, given I update my investment thesis by writing it down.
I am really sorry, I will be unable to buy your product because you are selling it to me. – Guy Spier informing cold caller about rule number two
I will be upfront: I’ll provide the reader with one key thing to remember, but also one Q&A answer to forget.
The Best Idea Fund
Charlie Munger coined a problem at his dinner party: Capital Group (large LA based fund manager) created The Best Ideas fund. This fund would collect one favorite stock per analyst in the fund. Munger mentioned that this fund underperformed the market significantly and asked guests as to why this might be.
The answer was
consistency bias: the idea that managers had spent the most research time on was typically their “best idea”. Human beings tend to selectively filter new information that confirms the first thing they belief to be true based on something they read or listened to.
the specialist problem: specialist analysts get biased toward their sector benchmarks. They do not select the best stock, but the best stock in the sector.
TC Comment: this compounds the insider view bias that Kahneman discovered. Man is naturally taking too much of an insider view already in general, and not enough benchmarking ideas to the outsider view, or base rates.
Why I think Pabrai’s thought on the general market valuation was confusing at best
In the Q&A Pabrai says (minute 57):
If interest rates stay low, for an extended period of time, then present valuations may be a bargain. [..] And of course we won’t know that, til we get to ’20 – ’24. And so, markets are discounting mechanisms, if markets had a crystal ball to tell us where interest rates were at 2020 or beyond, you could get to [the valuation] accordingly.
Of course we won’t know how interest rates for maturity X will change in the future, this is self-evident. What Pabrai seems to forget is that we do know what the expected future interest rate is for maturity X at future time T by backing out forward rates from the current term curve.
In short, I found it a bit stunning that Pabrai forgets to mention that we can use the market discounting mechanism today to find the market’s implied future interest rate for maturity X at future time T. The market does provide us with a crystal ball that gives us the expected interest rates in the future, or the ‘central scenario’, if you will.
In my post on Horizon Kinetics’ Bregman, I described several factors that deter index funds. Today I look at a factor that deters not only index funds, but many institutional and speculators in general.
Liquidity is a stronger factor for returns than size (see below by comparing columns).
Another interesting finding is that the value effect is stronger in illiquid companies, so strong that it overshadows the size effect (see below and compare most liquid smallest companies with most liquid largest companies). In other words, the market is less efficient in the universe of illiquid stocks
Fama would not agree with me as he argues value stocks are more risky in general and that risk is more nuanced than simple volatility (for example, we do not know when the prospensity for risk is lowest), but this is debunked in the famous Lakonishok et al. ’94 paper Contrarian investment, Extrapolation and Risk and an updated 2009 study. The papers show that not only is value less volatile than glamour in general, it outperforms glamour in the bad states of the world, i.e. recessions, where risk aversion is most probably higher due to job losses and the powerful animal spirit fear.
Many institutions are simply too large and cannot support research into these illiquid companies from a cost-benefit perspective, as they would drive up the price to build an economically meaningful position
Total friction costs for an investor are driven by liquidity and total trading volume. Ideally, an investor in illiquid stocks should
have a low turnover portfolio
The other benefits of low turnover tie into one of my favorite passages in the book I am currently reading, Capital Returns by Edward Chancellor. Low turnover* allows the investor to minimize the amount of decision making. Having to make a lot of decisions disproportionately increases errors by increasing time pressure. The pressure of highly frequent decision making is a disincentive to long term thinking about hard questions that matter. Guy Spier talks about the benefits of reducing the amount of decision making too in his book The education of a value investor.
As such, illiquidity forces the investor to think twice before investing in a stock.
The right spirit to invest in illiquid stocks is to worry about being right.
In my investing experience, illiquidity has been on balance a positive for me. Having said that, most of my investing has been in the time frame of the 2009 – 2017 bull market.
*Note that I use low turnover and not long term investing. In my view, low turnover is not a necessary condition to get the label “long term investor” and is not to be worshipped as an end in itself. Long term is merely a succession of short terms, and rational fundamental-driven investors should be willing to act short term if Mr. Market provides the opportunity to close a position at a margin of safety that is inadequate.
I’ve been reading investors letters by Steven Bregman for years (Horizon Kinetics). I admire his original thinking as he highlights many effects of the long bull market in passive funds or indexation.
Although it is hard to think about where we are in the indexation bull market, I think it is very interesting to hear Mr. Bregman talk about what indexation means, and will mean, for stock-picking.
For those that are not familiar to the Horizon Kinetics letters, I encourage you to take a look at some of the idiosyncratic stock picks they did.
For that we need to get into the plumbing of indexation.
float adjusted market cap: index ETF’s changed the simple market cap weighted rule when they became more popular. For example, benchmark owner S&P changed to float-adjusted weighting, which weighs every stock by the valuation of the float, or the piece of the business that is held by the general public
Insiders selling create mechanical (=motivated) buying pressure from index funds, while studies have shown that companies with less insider ownership underperform (I believe the sweet spot to be around 50% insider ownership in a regression)
Insiders buying or companies buying their own shares fast such as the uber cannibals make index funds motivated sellers, while these companies outperform
TC Comment: if we assume that the fundamental performance of these outperformance stays identical vis-à-vis a scenario where no index funds exist to create selling pressure, this means that, ceteris paribus, the insider and uber cannibals anomaly is expected to become bigger over time by virtue of a lower entry price
Automatic bid: as long as capital flows into index funds, stocks that are currently most weighted in passive funds (such as mega caps and FANGs) are expected to get more automatic bids. If not, they are subject to the marginal buyers (e.g. active investors supposedly not interested in FANG stocks at the current price levels)
Bregman compares anticipating the tech bubble versus the index bubble with a fishing boat observing incoming waves from a storm versus a fishing boat getting lifted by the overall higher water level from a tsunami, not very visible until it hits the shores
Passive indexation is not equally pervasive in the stock market. This imbalance exists not only because of the market-cap rule, but also because there are sector and country specific ETFs offerings that create differences depending on ETF buyers’ current preferences with respect to total stock supply
Bregman cites Norway as an under-indexed country.
Bregman cites the shipping sector as an example of an under-indexed sector
TC Comment: while the reasons are not discussed (for shipping this could be because it is in a down cycle and unpopular right at the time that most money is flowing into indexation), I took this phenomenon as an input in my checklist item of “Why the mispricing might exist” before buying Wilh. Wilhelmsen Holding (WWIB) in Aug. ‘16 a Norwegian shipping company
Bregman mentions Siem Industries in Norway. Having looked into the company a bit we found there are only a couple of hundred shareholders for this >1B USD market cap company. Illiquidity is clearly a barrier for indexing.
Illiquidity in stocks, acting as a deterrent of index funds, and institutions in general, is known as a big driver of returns. I will do a separate post on the topic.
Note: I highly recommend Patrick’s reading list, and of course his podcast on which he manages to pull off great interviews by asking great questions to many quality guests.
Mr. Thorndike owns a private equity company and wrote this book after finishing a project at Harvard Business School to characterize CEOs with tenures that enriched all shareholders.
The book starts by quoting Sir John Templeton:
It is impossible to produce superior results unless you do something different. – John Templeton
The aim of the book is to paint a picture of the iconoclastic CEO’s behind some of the companies with the highest returns in the last decades. Each chapter is a case study of one such individual’s history and lessons.
In this summary, I will not go into each case, but the CEO’s discussed were: Tom Murphy (Capital Cities), Henry Singleton (Teledyne), Bill Anders (General Dynamics), John Malone (TCI), Kathy Graham (Washington Post Co.), Bill Stiritz (Ralston Purina), Dick Smith (General Cinema) and Warren Buffett (Berkshire Hathaway).
One remarkable point as I was reading the book was how similar the philosophies of the CEO’s were to that of Warren Buffett. This point was emphasized by the author ending his book with a case study on Warren.
The common treats among all these remarkable Outsiders were:
Single-minded, not obedient to “the institutional imperative”
willing to invent new metrics which are now famous (e.g. John Malone’s EBITDA, Smith’s self-defined cash earnings, Buffett’s pioneering focus on the advantages of float)
first-time CEO’s (Kathy Graham was 70 years old when she was ‘forced’ in the position of CEO of the Washington Post and had no prior corporate experience!)
using almost no outside advisers such as bankers and consultants (e.g. when John Malone’s did his multibillion sale of TCI to AT&T in 1999 he showed up alone with a notepad while the buyer side sat down with an army of advisers)
Numerate, cool and rational thinkers
doing the math on M&A, buybacks, organic growth, sales themselves with back-of-the-envelope simple models to take ultimate decision
looking at all options available
Preference for decentralized operations
leading conglomerates of 10 000’s of people with only <10 people in headquarters
“hire well, manage little”: willingness to give CEO subsidiaries management freedom (e.g. Warren Buffett and Singleton are the extreme specimen here)
Preference for centralized capital allocation
cash from subsidiaries had to pass via HQ’s before being reallocated after having made significant capital allocation decisions. Cash flow budgets were strictly enforced
Heavy focus on minimizing – that which is a guaranteed loss if not optimized – taxes
no / very little dividends, as share buybacks are more tax efficient
willingness to be patient when peers are impatient
willing to invest bigopportunistically
invest in … [pick anything from M&A, share buybacks, organic investments] when that market is depressed
not willing to pay for paint on the HQ’s side which does not face the public
unwillingness to issue new shares: the EPS denominator matters
When John Malone handled the negotiations for the 50B$ sale of TCI to AT&T, he showed up alone with a notepad. He was often facing a sizable crowd of AT&T lawyers, bankers, and accountants across the table.
I compiled this overview from two tables in the last chapter.
>30% float buybacks
Acquisitions >25% of mkt cap
Wall Street Guidance
Cash flow margins
Cash ROI ‘CFROIC’
Malcolm Gladwell’s ’10 000 hours’
Primary activity: operations management and communication
“Growth”, “Revenues”, “Net income”
If you appreciate the learning potential from case studies (like me), you’ll love this book.
What is still a bit unclear to me is to what extent these characters were chosen to be features in the book because they shared these special character treats, and to what extent they belong together because they belong to the general group of CEO’s with tenures of great total shareholder returns. The rationale for picking these personalities is not discussed and this is my main criticism.
On the upside, as an investor I ideally want to know about CEO’s with characters and behaviors that go against the ‘Institutional imperative’, as the probability that their companies are misunderstood and hence undervalued is much larger. The book satisfied that demand very well.
This book is a deep dive in the science/art of forecasting. The author published academic research on forecasting (e.g. results of his Good Judgment Project) and felt it was often misunderstood, especially journalists.
The Good Judgment project fed many forecasting questions over a multiyear time-frame to a large group of volunteers, often amateur forecasters. The book draws insights from the best ones in the group.
As usual, I will summarize the book and comment on how it relates to value investing.
Chapter 1 – Optimistic Skeptic
The mainstream media caters to pundits that make vague statements about the future. Often, there’s a symbiotic relationship between attention-grabbing vague forecasts and the media.
while bold statements create more buzz hence revenues for mainstream media channels, adding vagueness guarantees that this revenue can be repeated indefinitely
sometimes the publisher encourages these type of forecasts: sell-side analysts might participate in a run-up for the most attention grabbing predictions like “Dow 10 000”, “Oil 30$” which are intended to impress a large number of clients
Even serious public personalities sometimes make non-falsifiable statements, hedging themselves for the outcome:
vague statements “may happen”, “is a serious possibility”, “significant/serious probability” can be interpreted ex-post as any probability between 0% to 100%
a deadline is often not specified, one example was the 2010 open letter to Bernanke about the risks of “future inflation” (the signees were Seth Klarman, Jim Chanos, James Grant, Niall Ferguson, AQR Cap, Elliott’s Paul Singer). Are the forecasters wrong because inflation has not materialized yet? They don’t talk about which type of inflation (price inflation, asset inflation, monetary inflation which is, well, true by definition), the deadline, or odds. Instead, the below statement from the letter cannot be falsified:
The planned asset purchases risk currency debasement and inflation, and we do not think they will achieve the Fed’s objective of promoting employment.
The chapter also discusses how forecastable the future is, or could become with advances. The butterfly effect in complex non-linear systems guarantees that foresight of even Superforecasters is very limited when more than five years out. Even “big data” cannot push the temporal boundaries of forecasting.
the last bit about the time boundary to forecasting complex systems reminded me about the empirical finance books by Robert Haugen on quantitative value investing
Haugen found that market multiples correlate well with five year subsequent profit growth. In other words, the market predicts the fundamental performance of companies in the near future well. However, Haugen also correlated market multiples with profit growth between the fifth and tenth subsequent years. The correlation was almost zero (for Haugen’s empirical results see The New Finance . Haugen’s other book which treats the low volatility anomaly is The Inefficient Stock Market . Although ironically the low volatility anomaly has worked well ever since CAPM came into existence, it is currently in vogue because of the smart beta ETF hype. The Warren Buffett quote What the wise do in the beginning, the fools do in the end might well apply here. Another author I can recommend on the low volatility anomaly is Eric Falkenstein, through his book and blog).
I agree on the big data limitations for forecasting. For other big data skepticism, I can recommend this Wired article by Nassim Taleb
As the number of variables grow, the amount of spurious correlations explode. – N. Taleb
Chapter 2 – Illusions of Knowledge
There’s many drivers why humans are bound to kid themselves.
Being scientific means practicing experiment and self-doubt, but typically this doesn’t reflect well on the expert if he is catering to a broad public. Examples:
the way medicine was practiced for centuries was very unscientific, physicians killing many patients in the process because of arrogance of (illusory) knowledge. See also Antifragile’s chapter on medicine by Nassim Taleb
Adopting a scientific way of thinking takes a lot of mental energy, as prioritizing intuitive snap judgment or fast thinking is in our genes to protect us from caveman dangers (see Thinking fast, Thinking Slow by Kahneman)
Advice to force scientific thinking is asking questions like “What could convince me I’m wrong?”, which is the equivalent of science setting up experiments to prove hypotheses wrong.
Also, closely monitor your mind to not mutilate hard questions into simple ones, e.g. is some animal lurking for me in the grass simplifies to “has an animal ambushed me in the past”, or likewise the question “do I have cancer” simplifies into “does the expert say I have cancer?”. This form of system 1 thinking is called bait-and-switch, subconsciously converting hard questions in easy ones.
“What could convince me I’m wrong?”
Other bait-and-switch examples in investing
“is this a stockwith solid future returns?” simplifying into “is this a company with great fundamentals?” (irrespective of valuation). See the nifty-fifty bubble and Ben Graham’s discussion on this bait-and-switch behavior of some retail investors that equate good companies with good stocks
“will electric cars / the 3D printing technology break through?” simplifying into “will this growing industry offer attractive returns to me as a shareholder” (“Don’t equate potential for social progress with shareholder returns, just look at airlines” – Warren Buffett)
Note that this is even more dangerous in assets or collectibles without real anchor, e.g. bitcoin, alt-coins. The question “will bitcoin rise?” simplifying into “will the blockchain have a bright future”. Likewise, for gold the question “will gold rise?” simplifies to “will there be war?”, “will the monetary system crumble?” with no regard as to how much of this is already priced in. Indeed, the question how much is already priced in is so hard to answer for these no-anchor assets that bait-and-switch has an exceptional appeal
Chapter 3 – Keeping Score
Forecasts with vague probability statements and no time-frames are driven by tip-of-the-nose intuition. A big drawback is that feedback is impossible as hindsight bias will almost ensure forecasters interpret their vague statements favorably after the fact. This is what Mr. Tetlock calls the wrong-side-of-maybefallacy, stretching “maybe” to the ex-post correct outcome in the forecaster’s mind.
The conclusion is that we cannot get around the necessity to use rigorous definitions of what we are forecasting, and number estimates of probabilities to be scientific and get real feedback after the facts. The other advantage is that specific statements force the mind to think more, as vague statements lull the mind in warm fuzzy thoughts. Indeed, Tetlock proved in a randomized trial that even in the domain of using number estimates, forcing subjects to use more decimals in probability made their forecast more accurate!
Diving deeper in the hard science of forecasting we have several key concepts that can be quantified:
Calibration – percentage correct versus percentage forecasted, if we would draw a graph of buckets of the % of positive event outcomes versus the forecaster’s estimated % of positive outcome and the graph is a straight 45° line, the forecaster would have perfect forecasting calibration to outcomes
Resolution – resolution is a measure that captures whether a forecaster dares to make forecasts outside the “maybe” domain of 60%-40% to 40%-60% and venture into the extremer and more difficult domain of predicting 90%-10% if warranted “well calibrated but cowardly” = poor resolution, “well calibrated and brave” = good resolution
The Brier score (Wikipedia) aggregates these forecasting qualities into one number for a set of forecasts. The Brier score captures the mean squared difference of actual outcomes versus the forecaster’s estimated probability of those outcomes occurring. Hence it is a cost function that ideally is as close to zero as possible. In short, it captures forecasting ability well.
Chapter 4 – Superforecasters
This is where the book starts drawing lessons from common characteristics of best performing forecasters in Tetlock’s Good Judgment Project.
The main feature of Superforecasters is being open minded to different ideas, acknowledging that useful info is widely dispersed
ideological forecasters use colored lenses to look at the world and because of confirmation bias see everything as confirmation to their “big idea”
“To the man with the hammer, every problem looks like a nail. – Charlie Munger (or the dangers of ideology in this context)
As for the lucky coin tossers argument that would invalidate Tetlock’s Superforecasters, Tetlock cites Michael Mauboussin that says:
To see how much luck plays a role in a skill/luck game, see how much mean reversion occurs in players with good results.
The author tested whether his forecaster group mean reverted fast over multiple years. He concludes that the best forecasters from the group, Superforecasters, could not be reproduced by coin tossers.
a very big lesson here is that ideology and investing do not mix well at all
one should be suspect of investors making ideological arguments for certain macro views / stocks.
in this we should be resolute and distrust superinvestors
Seth Klarman: this reminds me of Bronte Capital’s John Hempton’s post about how Klarman went long a gold miner that Hempton soon found out was operated by tricksters and went soon to zero (Hempton went through Soviet documents about the deposits and knew it was zero approx. zero). Mr. Hempton made a very convincing case on how Klarman got blindsided: the ideology of being an Austerian (Klarman was also a signee of the Bernanke letter) and wanting to believe the Keynesian policy would lead to disaster).
David Einhorn’s 2011 investment in miners and gold was arguably outside his circle of competence as a stock picker and did not do well. He documented his buys with a lot of ideology on some conferences (see Buttonwood)
if I invert this, I’d say that investors investing “against” their own ideology deserve some attention (e.g. Hugh Hendry)
Klarman became a victim of not only ideology (Chapter 4) as precious metals didn’t do well, but also bait-and-switch “precious metals will do well, hence miners will do well”
Synthesis of Hempton’s post how Klarman got blindsided with Superforecasting
Chapter 5 – Supersmart
Outside of the main feat of open-mindedness, Tetlock found that Superforecasters were above average intelligent (but not necessarily >130 IQs), had a growth mindset (people of the viewpoint that intelligence is something you can attain, not genetically fixed), and decomposed problems.
1. The Fermi Technique
To answer a difficult question like “how many piano tuners are active in Chicago”, the physicist Fermi decomposed this into sub-problems to get to a surprisingly accurate answer. Central questions are:
What would have to be true for this to happen?
What information would allow me to answer this question?
It is very important to focus on these questions first to prevent system 1 thinking of taking over.
guess the number of pianos in Chicago
how many people live in Chicago
what percentage own a piano
how many pianos are at institutions
how many pianos need tuning each year
how long does tuning take per piano
how many hours a week does a tuner work
Another example of why it is so important to approach the problem rationally immediately is the Arafat question. Following Arafat’s death an investigation to polonium poisoning was launched. The forecasting question was: will the investigator find polonium in Arafat’s body.
A bait-and-switch pitfall would be to conflate the question with “did Israel poison Arafat”, because many more possibilities exist (palestine rival, CIA, ex-post fake poisoning, and even natural amounts of polonium in the body). Therefore always ask “what would it take for this to happen?”. Also note that the question is not “was Arafat murdered with polonium” but “will polonium be found“. What would it take for this not to happen? Significant probability: investigator messing up investigation.
What would have to be true for this to happen? What would it take for this not to happen?
Summarizing, we always have to ask ourselves these serious questions before our system 1 thinking takes over.
2. The outside view
What we have to explore first is what Kahneman calls The Outside view or the base rate in statistics.Ask what the probabilities are for certain things to happen as viewed from an outsider perspective.
For example, “Tom likes racing on circuits and often speeds in traffic. He commutes to work every day, what is the probability he has a car crash in the next five years?”, a gut feeling answer would be swayed by the story elements like “racer” and guess a probability from this emotional element. The base rate would be to find the rate of deaths per km driven for the sub-group that Tom belongs to (e.g. male, 20-30 year old, American), using all quantifiable objective elements that are available.
The reason why the base rate is important is that people are suckers for stories and have been shown to overestimate particulars of a story and underestimate the weight of the benchmark.
The reason why starting with the base rate is paramount is that anchoring bias will work in our advantage (instead of our disadvantage if we start with our gut feeling probability) by having our forecasting process in this order, knowing the previous point that we underestimate the weight of the base rate. Only after knowing the base rate, we would craftily have to tilt it by taking into account qualitative elements of the story.
The reason why starting with base rates is so important is that we make anchoring bias work in our advantage.
3. Variant views, what are experts saying, pre-mortems, forget estimation and re-estimate
After having incorporated a tilt to the base rate, next we want to assume we are wrong. When researchers told forecasters they were wrong and had to find a better estimate, their subsequent new estimate was more accurate.
Assuming one is wrong in advance and making an analysis of what went wrong is called a pre-mortem and can provide good insights.
Finding variant opinions might also improve one’s grasp of the situation.
Another technique is to first forget one’s estimation process after letting several weeks pass and do another estimate. It turns out that these second estimates are on average more accurate.
Lastly, turning a question on it’s head, for example “will South Africa allow the Dalai Lama” can reveal new questions “will South Africa deny the Dalai Lama”, why would South Africa deny the Dalai Lama?
All these techniques together provide the Superforecaster with a dragonfly-eye that has approached the problem from many perspectives to form an accurate view.
finding variant perceptions is a classic in investing. I believe Michael Steinhardt was first to coin it in the ’80s (I got this from his book I don’t really recommend). Personally I think this is extra important in investing as you want to understand why the opportunity exists (what is the consensus view, or who is selling/buying) . David Einhorn is also known for saying he typically looks at situations that on which he already knows reasons why it could be mispriced. I try to do the same because
not doing so will lead one to research many more mediocre opportunities. Because investing is a decision making game that is ultimately won with limited resources (i.e. research time and information), great filters are needed. See the concept of Bounded Rationality by Herbert Simon.
if I do find an opportunity that looks great, but I can’t formulate an answer to why this “great” opportunity exists, I am more likely to be missing something myself! This ties to Warren Buffet who says
If you do not know who the patsy is at the table, you’re the patsy.
Chapter 6 – Superquants
Numeracy is often misunderstood as an advantage for forecasters. Superforecasters were found to be highly numerate people, but weren’t explicitly using much numbers. Rather they were “thinking in” numbers instead of words. Tetlock:
Superior numeracy does help superforecasters but not because it lets them tap into arcane math that divine the future. The truth is simpler, subtler, and much more interesting.
Many non-numerate people use three-setting mental dials and think in low resolutions: impossible, sure or maybe “50-50”. It was shown (this is somewhat trivial) that people who gave 50%-50% often as an answer were much less accurate in their forecasts.
Non-numerate people also tend to believe in fate, which is the opposite of probabilistic thinking. Finding meaning in events is positively correlated with well-being but negatively with forecasting ability.
I relate a lot to this argument that numeracy is misunderstood as an advantage in forecasting/investing. I consider myself numerate but don’t use any arcane math in investing. I also belief that Buffett speaks the truth when he says he doesn’t use any higher order math, although I’m sure he knows higher order math.
Chapter 7 – Supernewsjunkies
The best forecasters continually updated their probability forecasts to get a good Brier score by keeping current to the news. They craftily balanced underreaction with overreaction to news.
When the facts change, I change my mind. What do you do? – J.M. Keynes
in investing the danger of news is primarily to overreact because of recency bias (facts that are easiest to recall get more weight in judgments)
by using this rule, recency bias is attenuated because even the most recent news becomes older. It also forces an investor to rethink, similar to the findings from Chapter 5 to forget ones first analysis and redo it is beneficial to the accuracy of the analysis
when news is unfavorable to an investor’s position, the danger shifts to underreaction because of confirmation bias
Chapter 8 – Perpetual beta
Superforecasting requires a lot of mental energy to keep system 2 thinking running. A great forecaster knows that he should always be on guard to mental biases. Overconfidence might lure him back to becoming mediocre by doing snap judgments or system 1 thinking, because he built a great track record and hence “his judgment must be correct”. For a superforecaster to stay on track, he needs to keep improving his abilities and question himself. In computer programmer terms he is in perpetual beta.
An important factor to remain grounded to reality is to have good feedback. Research has shown that police officers are overconfident about their lie-detection abilities, but become even more detached to reality the more older and “experienced” they become. This is because their feedback is very limited.
Superforecasters make post-mortems of wrong estimates and put a lot of effort in these analyses.
It speaks for itself that this is true for investors, especially when they are in the spotlight for good performance. For CEO’s there’s a similar effect: “managers of the year” company’s stocks perform pretty bad in the subsequent year. This is perhaps because of a myriad of other causes, but one of them is almost certainly the overconfidence one gets from the public spotlight.
On feedback: luckily, markets provide feedback, which in the short term is very noisy. Although the market is noisy, it is important to remain fact-based and not mentally retain big winners while forgetting ones’ losing positions. Easy shortcut is to keep grounded to reality by looking at aggregate portfolio performance, which is already less noisy than individual positions.
Chapter 9 – Superteams
Teams are difficult to analyse and to control for.
General findings were that teams that
got instructions to work together constructively but avoid groupthink “tell me what you think is wrong about my reasoning” (i.e. pre-mortem encouragement), performed better than the average of the individuals
had members with diverse skills added to the team performance as the wisdom-of-crowds effect kicks in when diverse information is put together
Chapter 10 – The Leader’s Dilemma
To be a great forecaster takes self-doubt and experimentation. How do you reconcile this with the qualities that are expected from a leader (i.e. a prominent decision-maker), like confidence and decisiveness?
An answer was found in the Prussian army and the Wehrmacht. These armies had a very decentralized structure where superior’s ask their subordinates to achieve something but not how to achieve it. Note: Hitler violated these rules he inherited from the Wehrmacht, and some key Wehrmacht mistakes in the Normandy landing were arguably because of Hitler’s insistence on top-down commands.
A culture of second guessing superiors about battlefield tactics was nurtured in junior officers. Although everyone was encouraged to second-guess oneself and everyone else, the leaders’ mindset exhibited calmness and decisiveness once that decision-making process was completed.
the decentralized way the Prussian army functioned is similar to Berkshire Hathaway. Buffett clearly communicates to his managers he prioritizes high ROIC over growth for its own sake, but he does not tell his managers how to achieve that
Staying a superforecaster is inherently difficult because of the overconfidence pulling us back toward system one thinking.
We can be cautiously optimistic about the future of scientific forecasting. On the one hand, the rise of prediction markets and the trend at the intelligence agency to use probability numbers instead of vague language seems to be set.
On the other hand, forecasting will always remain a sensitive topic: forecasting that Trump might win the Presidential Elections creates a backlash from the democratic party that might conflate the forecast with political views. Many professional career pundits will keep on self-selecting for sensational content for mainstream media channels that need revenues. They will keep on using vague language if they want to be assured of their long term career. Famous talking heads will keep on using the wrong-side-of-maybe fallacy after the fact to defend their reputation.
Lastly, some questions that really do matter cannot be framed in forecasting questions. The skill of asking the right question (a question that is worth investigating) is often independent of the skill of forecasting. An example is the Korean conflict: “will North Korea launch a rocket to country X by time T?” is a question that is perhaps not that worthwhile to answer. Meanwhile, “how does this all unfold?” is more worthwhile but more difficult to frame in binary/multiple choice forecast problems. A solution can be to dissect the big question that matters into sub-problems, or a “cluster of forecasts” belonging to the big question (e.g. nuclear tests, rocket launch to country X , Y, cyber attacks, artillery shelling, nuclear war). A column writer like Thomas Friedman doesn’t have a great track record for well-defined clear forecasts, but people like him that think more qualitatively raise many interesting questions (e.g. certain aspects of the outcome of the Iraq war) that forecasters can then try to solve.
there exist several hidden agendas for biased or vague forecasts: political, brokers wanting to generate fuzz and commission, mainstream media pundits aiming for a career in eye-grabbing vague statements
to improve forecasting ability, one needs feedback. The only way to get clear feedback is to lay out clear forecasts in numbers (that is, probability estimates)
use base rates first, adapt to qualitative specifics after
ask yourself scientific questions:
What would have to occur for this (not) to happen?
What information would allow me to answer this question?
decompose the problem into subproblems “the Fermi technique”
assess several points of view and thinkers, stear clear of ideology
forecasting well is inherently fragile as overconfidence directs us to use our “great” snap judgment again
asking the right questions is a whole different problem
The main lesson is obvious, but very important. This is why repetition it is not harmful (actually a lot like of going to church every week).
There are three fundamental concepts that you can take away from the exhibit. First, a company earning its cost of capital will trade at the commodity price-earnings multiple, 12.5 times in this case, irrespective of growth. You can imagine these companies as being on an economic treadmill: You can speed up or slow down the treadmill of growth and it makes no difference, the companies are not going anywhere. Value neutral companies must first figure out how to increase ROIIC before they worry about growth.
As one can see in the below figure, there are two valuation drivers: ROIC and earnings growth. The most important point: The key valuation driver is the variable at which the company is worst.
P/Es given ROIC and earnings growth (assuming an 8% WACC).
Value neutral companies must first figure out how to increase ROIIC before they worry about growth.
Second, if a company is generating returns in excess of the cost of capital, growth is good. Indeed, all things being equal, faster growth translates directly into a higher price-earnings multiple. For instance, the warranted price-earnings multiple for a company with a 24 percent ROIIC and 4 percent growth is 16.1 times, whereas a company with the same ROIIC but a more rapid growth rate of 10 percent is worth 25.7 times. The value of high ROIIC companies is extremely sensitive to changes in perceived rates of growth.
A high ROIC company with sluggish growth should focus on growth. A low ROIC growth company should focus on capital efficiency.
Important simplification: we keep ROIC constant for different company sizes (e.g. Amazon improves ROIC by growing).
We (Gotham Partners) know a little bit more than what I wrote in the book (The Magic Formula). But I figured if you could double people’s returns in stocks or close to triple the return in small stocks that was worthwile. We do look for these two things (high ROIC and high earnings yield), but instead of looking at last year’s earnings we use normalized earnings. Most people can’t figure that out. We can’t figure it out for most stocks, but for those stocks where we can figure it out, we are looking for companies with high returns on tangible capital on a normalized basis and high earnings yield based on normalized earnings. That is just very logical.
Problems with valuation shorting
My quibble with long/shorts – the guys who do special situations in shorts where it is a scam or the company will run out of money. I like those type of shorts though I am not particularly good at them. If you are doing valuation shorts then I don’t like that. That strategy blows up every seven or eight years – the shorts go up and the longs go down and that happens to every quant guy. I am not saying a long/short hedge fund doesn’t make sense. But I don’t value short term volatility because I take a three or four year view. Then why give up 2.5% a year in returns by shorting. I am not adding value. It doesn’t add value because I am losing 2.5% a year and I don’t care about volatility.
Break out no-growth value & value from growth
Simplify everything – what is it worth now if they just stopped growing? Then if they take some of their incremental dollars capital and buy stuff what kinds of incremental returns do I think they are going to get on that? So I break things into two pieces generally. [..] I have to make an assumption of what will they do with that cash (on no-growth value).
How should one go about investing and life in general?
Please enjoy this great poem by Kipling.
If you can keep your head when all about you
Are losing theirs and blaming it on you,
If you can trust yourself when all men doubt you,
But make allowance for their doubting too;
If you can wait and not be tired by waiting,
Or being lied about, don’t deal in lies,
Or being hated, don’t give way to hating,
And yet don’t look too good, nor talk too wise:
If you can dream—and not make dreams your master;
If you can think—and not make thoughts your aim;
If you can meet with Triumph and Disaster
And treat those two impostors just the same;
If you can bear to hear the truth you’ve spoken
Twisted by knaves to make a trap for fools,
Or watch the things you gave your life to, broken,
And stoop and build ’em up with worn-out tools:
If you can make one heap of all your winnings
And risk it on one turn of pitch-and-toss,
And lose, and start again at your beginnings
And never breathe a word about your loss;
If you can force your heart and nerve and sinew
To serve your turn long after they are gone,
And so hold on when there is nothing in you
Except the Will which says to them: “Hold on!”
If you can talk with crowds and keep your virtue,
Or walk with Kings—nor lose the common touch,
If neither foes nor loving friends can hurt you,
If all men count with you, but none too much;
If you can fill the unforgiving minute
With sixty seconds’ worth of distance run,
Yours is the Earth and everything that’s in it,
And—which is more—you’ll be a Man, my son.