As the book does not have a big narrative, I’ll share some interesting concepts and quotes I picked up. The book is also full of “concept boxes” that explain certain touched-upon concepts. Even for seasoned investors, you will learn a few things. I will not share these.
Interesting thoughts, resource and quotes. Unsurprisingly, as an electrical engineer myself, the “hard science” investors’ thoughts resonated most:
most investors used the bulletin boards to share info with others ADVFN (which I find useful as well for our Dart Group plc position), Fool, iii.co.uk, stockopedia.co.uk
Investing is not like Olympic diving: there are no marks for degree of difficulty
optimal betting size (i.e. Kelly Betting) is more cautious to downside risks than simply going by “expected returns” (i.e. probability-weighted return). Optimal betting uses logarithmic returns: while an investment with 50% chance of +25% return and 50% change of -20% has a 5% “expected return”, it has a 0% expected logarithmic return. Another way to see how an investor “gets” 0% and not the expected return is by continuously investing in the above 50/50 +25%/-20%-type of investments: +25%’s that are equally followed by -20% return 0% over time
Path-indendepent thinking: occupational identity can be a mental constraint. Don’t let your thinking be constrained by your identity.
I don’t seem to have very much influence on Walter. That’s one of his strengths: nobody seems to have much influence on him.
Warren Buffett on walter schloss
look for motivated sellers
better be right than consistent
The best decisions in the stock market attract no applause
structuring your investments by writing down a brief 1) thesis 2) secondary factors 3) “hygiene factors” (absence of red flags)
investing is a game with negative scoring: avoid mistakes, learn from other people’s mistakes
optimal rate of error: it is not worth knowing everything about a company, because every point investigated has a time-opportunity cost. Your aim in checking “hygiene factors” is not to find out everything, but to reduce your error rate to an acceptable level
On talking to insiders and activism:
strategic naïvety: it can help to appear less sophisicated than you are. It helps persuade insiders to open up.
manage company meetings: at AGM’s, set expectations at the start of the meeting by informing insiders you have several questions to ask. Take note of who answers which questions and how they interact.
create a paper trail: putting your communication on paper makes it harder for directors to evade their fiduciary duties and ignore you
Another interesting – and complimentary – review can be found here.
I read this book because its author proved to be correct on oil. This is a non-exhaustive book summary I made last year. In the meantime, other events prove another call in the book: the book predicts convergence of global energy prices: oil has come down and the cheapest natural gas in the world (American) is rising.
The Energy World is Flat offers a refreshing view on the oil market. I found it through one of the better Real Vision interviews with Diego Parilla two years ago. The title is a variation to Tom Friedman’s best-selling book on globalization The World is Flat. Lastly, Diego Parilla and I are alumni from the same oil & gas business school.
I only read the book now as I realized that the author’s first call on the flattening of oil call has already proven profitable. These are the main calls the book makes:
the term curve of oil will flatten
geographic spreads will flatten
spreads between energy equivalent prices of fossil fuels will flatten
oil price volatility will lessen
If we compare the oil term curve between the publishing date (1/1/15) and now, we find that it has flattened considerably.
Chapter 1: the Flattening and Globalization of the Energy World
In the oil shock of the ’70s, oil was displaced for power generation and industrial uses in favour of coal, natural gas, nuclear and others because the primary consideration is price in these industries.
Today, oil still reigns over other fossil fuels for transport purposes despite its higher price (e.g. oil was 10X more expensive per energy equivalent than natural gas in the US in 2012). The main is reason is that oil is exceptionally compact both in terms of volume and weight per energy equivalent. Over the short-term, transport is very price inelastic.
Geopolitical events that created volatility sowed the seeds for more buffers ‘flatteners’: storage, demand destruction, new technologies and discoveries. A result can be found in 2014 when the exceptional combination of the below supply disruptions failed to make the oil price spike (the move was limited to 10$/barrel from bottom to peak).
the arab spring (e.g. disruptions in Libya)
oil sanctions in Iran,
conflicts and disruptions in Sudan, Syria and Iraq
Chapter 2: Lessons from the Dotcom bubble
The tech revolution (and bust) created huge capital inflows that led to miserable investor returns over the cycle. The big winners were consumers that benefited from stranded assets such as fiber-optic broadband.
The revolution of fracking and horizontal drilling is similar. Although there is still a lot of skepticism towards shale for environmental reasons, Parilla draws a parallel with ultra-deep-water drilling that faced critics in the early ’90s but developed into a very safe technology. Peak oil sentiment similarities to the tech revolution includes huge capital investment into:
LNG terminals (requires huge upfront capex)
pipelines (see European and Asian projects)
One trap for energy investors is to follow consensus according to Parilla. The sector is driven by extremely optimistic assumptions of demand growth. Every year, demand growth estimates are revised down an average of 15-20% from the January estimates (IEA, OPEC). Since 1998, only one year, 2012, has seen meaningful upward revisions. Main reasons are
optimistic GDP growth estimates
using the rear-view mirror correlation between GDP and energy demand that has been breaking down since 1998
Another parallel with the dotcom boom is the diversified ‘venture capital’ approach. In the energy world a lot of capex is being made in new technologies, with a lot of losers. The mentality for
big integrated O&G company boards is to ‘be’ invested in new areas as it looks better on paper
investors to be invested in all new areas as “you only need one winner”
Examples in the transportation world are:
compressed natural gas (CNG)
LNG for trucks, trains and ships
electric and hybrid vehicles (EV’s and HV’s)
Note: according to Parilla, governments have delayed EV’s by subsidizing combustion-engine car sales (and bailing out the companies) post-recession by a 6-to-1 investment factor to EV subsidies.
Last parallel: the bubble accelerates the impact of the revolution. The runaway oil price in 2007 set in motion a huge supply response by oil producing and oil consuming countries alike.
Diego warns that a sum-of-the-parts valuation for companies that invest in many fashionable new technologies can be very dangerous with bad capital allocators, as the good parts might subsidize loss-making ones, and that focused companies should be welcomed.
Chapter 3: The 10 Flatteners of the Energy World
During the super-cyclical run up in corn prices in the 2000s, most commodities were making historical highs, from crude oil, to coal and natural gas, to copper and corn. Correlations had notably increased, which was often used as an argument to justify that speculators were driving prices. And of course, high fuel and food prices were generating inflation and increasing the risk of financial stability. One again, politicians and regulators were quick to blame the speculators. “Food inflation, how dare they?” Corn was considered too expensive and would impact the poor the most and increase inequality. How cynical.
The main reason why corn prices were going up was the surge in demand for corn-based ethanol in response to both high energy prices and the regulated mandates. Corn, which had traditionally been “food and feed”, had become “food,feed, and fuel”. [..] In 2012, following an acute drought in North America, the prices of corn reached historical highs, 400% of 2005 prices. “The speculators are taking advantage of the situation.” Yet, that year over 40% of the physical harvest went to ethanol to “feed” the car. The quantities were mandated by the government as “fuel” forced the demand destruction of “food and feed” via high prices. It was the cattle and hogs who had to change their diet, not the car. By mandate.
Do spot prices converge to futures prices, or is it the other way around? A causality study by Merill Lynch, and Parilla, say futures converge toward the physical fundamentals of the spot market. Speculators will discount future fundamentals in the price. If they improperly discount future risk factors into prices, they will lose money as the future prices converge toward the in-the-future-prevailing spot fundamentals.
Before we move on to new books I am reading, I wanted to come back to my favorite read in 2016: The Everything Store. It is the story about Jeff Bezos and the creation of the giant that Amazon is today.
I hope my fallible memory is a great filter to sum up only great and memorable points.
Big things I learnt
I will largely frame my writing on Amazon’s values:
Customer obsession, frugality, bias for action, ownership, and high bar for talent, innovation.
On avoiding competition
Gaining edge initially: the decision to sell books
The thing that impressed me most was Bezos’ superior analytical thinking skills. As a new online store, Amazon needed an edge versus traditional brick-and-mortar stores. One of the ways he could achieve that is by having more customer choice (as brick-and-mortar have limited physical space to show and stock the product). He figured a product that has an immense variety in product offerings would maximize his edge. He went for books, hence the name Amazon, which aims to symbolize the variety of the rainforest. After Amazon became popular for books, he compiled a list of product categories with similar huge variety of choice and went for those as a second step.
There’s less competition for very long-term business plans
Most corporate agents are focused on the next quarter, year or years. A founder can afford to think longer term. There’s much less competition in plans that require patience.
Gaining and keeping edge longer term: market leadership
Gaining market leadership requires foregoing traditional financial metrics like GAAP earnings margins. Keeping market leadership requires relentless client/product focus and continuous cost consciousness. But that’s OK, Jeff knew very early on that market leadership gives you economies of scale in this new online business with huge fixed costs.
On customer obsession
Creating alignment through pricing
Amazon does not earn money on selling Kindles. Amazon earns when customers are satisfied with the Kindle ecosystem and buy Kindle books. This creates alignment.
We make money when we help customers make purchase decisions […] Merchants have never had the opportunity to understand their customers in a truly individualized way, E-commerce is going to make that possible. – Bezos
Creating customer oriented culture through rotation
Amazon has a mandatory rotation system to make every employee talk to customers through the Amazon.com call service. Engineers passing through customer service has also brought to light IT problems that were subsequently solved.
Obsess over customers, not competitors
On innovation and frugality
AWS and getting out of the way of the customer
Jeff is not a fan of wasting time, so he especially hates the idea that many of his employees would waste time together. Amazon’s culture was shaped early on to avoid meetings (e.g. standing up during meetings, no TV screens). Another slogan was “communication is a sign of failure”. No wonder that Amazon preferred IT systems talking to each other without human friction through good API’s. However, the IT needs for Amazon.com became eventually so big that considerable time of API designers went to asking and interfering with how the internal API clients were going to use the service.
This growing internal demand led Amazon to define basic computational building blocks, or “primitives” and make them really scaleable in order to sell these services to external clients as well. This way Amazon.com would recoup these large fixed costs to improve internal operations. The initial primitives were storage, computing, payments and messaging. This became what is known as Amazon AWS and the rest is history.
This innovation was based on the idea to get out of the way of developers and provide them all required building blocks with no questions asked. It is reminiscent of electricity generation becoming centralized in the 19th century, removing capex requirements on the consumer side.
When a platform is self service, even the improbable ideas can get tried because there’s no expert gatekeeper ready to say ‘that will never work!’” – Jeff Bezos
Frugality to drive innovation
Although Jeff always loved optionality and funded many long-shot projects, it has to be stressed that he likes cheap optionality by being frugal. Another advantage of frugality on top of lower costs is reflected in the following quote.
We try not to spend money on things that don’t matter to customers. Frugality breeds resourcefulness, self-sufficiency and invention. There are no extra points for headcount, budget size or fixed expense.
Leaders are intellectually curious, but commit to execute their decisions
The following is a quote by Bezos that was also covered in Superforecasting that we summarized. It marks the decision-making process of a great leader: be always questioning, but once you decide, show commitment to the execution of your decision.
Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly. – Jeff Bezos
Always keeping the bar high
“If that’s our plan, I don’t like our plan.”
“I’m sorry, did I take my stupid pills today?”
“Do I need to go down and get the certificate that says I’m CEO of the company to get you to stop challenging me on this?”
“Are you trying to take credit for something you had nothing to do with?”
“Are you lazy or just incompetent?”
“I trust you to run world-class operations and this is another example of how you are letting me down.”
“If I hear that idea again, I’m gonna have to kill myself.”
“Does it surprise you that you don’t know the answer to that question?”
“Why are you ruining my life?”
[After someone presented a proposal.] “We need to apply some human intelligence to this problem.”
[After reviewing the annual plan from the supply-chain team.] “I guess supply chain isn’t doing anything interesting next year.”
[After reading a narrative.] “This document was clearly written by the B team. Can someone get me the A team document? I don’t want to waste my time with the B team document.”
Defining non-GAAP KPI’s and what it means for investors
The following point relates to my previous book summary “The Outsiders”. I realize that the ability to creatively define the right key performance indicators is shared among great CEO’s and founders. Last post, we covered EBITDA defined by John Malone. Jeff Bezos is definitely another example of a founder who defines success by many unconventional metrics (e.g. long term absolute free cash flow, not GAAP profit margins).
In my opinion, investors trying to identify great CEO’s by checking if they use unconventional metrics is misguided however. Fraudsters are known to be very creative in defining new metrics. Many short-seller reports are full of criticism on creative metrics. Ultimately, the hard task for the investor is to think independently and check if these metrics make sense from a business owner perspective.
The value of a business is ultimately determined by a multiple to its absolute cash flow generating ability, not a percentage of GAAP earnings.
We believe that a fundamental measure of our success will be the shareholder value we create over the long term. This value will be a direct result of our ability to extend and solidify our current market leadership position. The stronger our market leadership, the more powerful our economic model. Market leadership can translate directly to higher revenue, higher profitability, greater capital velocity, and correspondingly stronger returns on invested capital.
Our decisions have consistently reflected this focus. We first measure ourselves in terms of the metrics most indicative of our market leadership: customer and revenue growth, the degree to which our customers continue to purchase from us on a repeat basis, and the strength of our brand. We have invested and will continue to invest aggressively to expand and leverage our customer base, brand, and infrastructure as we move to establish an enduring franchise.
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
This book was written in 2005 (!) and predicts/explains the rise of the platform company, why the US trade deficit is sustainable, why prime real estate in Western nations is poised for long term growth, and . This book is freely available online on the website of the author.
Generally I try to shun macro investing (although I do enjoy reading about it). Nevertheless, I have followed Louis-Vincent Gave for the last years as I think he and Charles Gave are very original thinkers. The Gave’s are of the Austrian school of economics.
I previously read Too Different for Comfort, which I rate even higher than this book. A follow-up with a summary of that book will perhaps follow.
See below for some very interesting snippets and my commentary.
The rise of the platform company & its implications
Vertical design/produce/sell company has been the usual model for the last 50 years. The future’s business model is to produce nowhere but to sell everywhere, Gave calls this “the platform company” (in 2005!). Platform companies simply organize the ordering by the clients and keep the high added-value parts of R&D, marketing in-house. They outsource production to competing producers. Examples are Dell, IKEA, Walmart, H&M.
Production is more cyclical and ties up large amounts of capital. Companies in the West are trying to shed capital-heavy parts, even in the hotel business, e.g. Mariott real-estate spinoff.
“Being capital light is like travelling with a light backpack instead of a suite of trunks: one is able to change course rapidly and avoid losses. When executed properly, the platform company makes for very high and stable ROI’s.”
If after an asset bubble, productive assets are not allowed (because the following conditions are not (all) met: governments resisting fears of unemployment, bankruptcy law that permits creditors to take over assets, efficient markets that permit) to flow from weak hands into strong hands, deflationary forces take over: zombie companies waste capital (human and/or financial), drag down returns for competitors, maintain excess capacity and keep prices low for everyone.
Commentary: this is exactly what happened in 2009, a bail-out boom created a long deflationary wave of overcapacity.
Fortunately for platform companies, it seems that most countries continue to be happy financing low return capital spending. Like parasites, platform companies thrive on other people’s excess capacity.
The Western economy is becoming less industrialized. This is great news as the economy becomes less cyclical (production is by far the most cyclical element). Overcapacity is now the emerging market countries’ problem. Positive feedback from industrial worker layoffs is primarily a problem in those countries. Less economic cyclicality is good news of course. But this has a second order effect of Western society leveraging up.
Commentary: the book does not highlight bad effects of over-outsourcing (true for e.g. Dell). Christensen discusses this in his books: companies will sometimes lose critical product know-how when they over-outsource, an example is the launch of Asus laptops as a competitor to Dell after the latter over-outsourced to them (How will you measure your life?). The Innovator’s Dillemma discusses the upward pressure for companies in the value chain on the other hand. This is all to say that emerging markets might not stay mere workshops of the world.
The world leverages up
It is perfectly reasonable then, for the consumer to leverage up if the economic cycle is less volatile (in countries like the US, UK, Sweden). For example, one generation ago 25-year-olds did not buy apartments. Today they can as they have an increased visibility over their future earnings power, and there are increasingly many parents-per-kid with seed capital to help them buy a house.
Commentary: Gave justifies the new normal of consumer debt booming in the US back in 2005.
The irresistable rise of real estate
This is where reasoning went wrong. Gave argues that real estate prices in 2005 are sustainable because
of mass produced goods’ price deflation (discussed in previous chapters): purchasing power for other things rises: local services and scarce goods like real estate can become much more expensive
furthermore, less economic cyclicality, man and woman working instead of just one person makes debt coverage visibility much better. This should support permanently higher real estate prices in the US
the structural collapse in inflation (thanks to better use of resources because of globalization/platform companies, and demography on the other hand) leads to sustained lower interest rates
houses being a very long lived asset like thirty year government bonds. As bond alternatives, houses moved up remarkably in lockstep with 30 year govt. bonds.
Commentary: the mentioned factors seem to have played out. Gave argues against the real estate bubble talk in 2005. To be fair, Gave wrote that for a true real estate collapse a lot of foreclosures are needed (with the banks being motivated sellers of houses). Indeed, the subprime borrowers turned out to be the catalyst for the price collapse (subprime are not discussed in the book!).
Consumption & US deficits – Accounting versus economics
Gave has a great example to illustrate that just looking at trade flows is insufficient to see which continent is doing best.
The flat screen, built in Taiwan, costs US$300. The margin of the Taiwanese manufacturer is US$30. The mechanical part and the box, built in China, cost US$100, with a margin of US$5. The Intel chip (designed in the US but made by TSMC in Taiwan) cost US$70 with a margin of US$35 going back to Intel and US$5 going to TSMC. The Microsoft software cost US$200, with a margin of 90%, or US$180. Dell tacks on a US$30 profit for selling the PC.
Profits for the US economy: US$35 (Intel) + US$180 (MSFT) + US$30 (Dell) = US$245
Conclusion: this looks like a good deal all around for the US: the US consumer gets a cheap PC and US companies capture most of the profits in the process. On an accounting basis, everything looks rosy… Now let’s see how an economist views the above transactions.
Imports: US$470 (price of the PC minus the Dell mark-up and Microsoft
Exports: US$0 Trade Defi cit= US$470
Increase in GDP, due to Microsoft, Dell and Intel profits = US$245
Net loss for the US economy, US$ 470-US$245 = -US$225
Conclusion drawn by the economists: this is a really unsustainable situation. The US economy is moving more and more in debt to foreigners who one day could decide not to sell in the US anymore, leading to a collapse in the US$, a rise in US interest rates, etc. But in the real world, is this situation really unsustainable? Absolutely not! What is unsustainable is measuring global trade flows in terms of sales, without looking at profits – which is what trade numbers do – and deriving investment implications from these measures. If the fellows exporting to the US make on average a margin of 1%, while US exporters churn out margins of 20%, then which economy would you rather own?
In the past five years, US profits (cash-flows) have increased by US$500 billion and the US trade deficit has increased by US$250 billion. Assuming that the assets generating the profits are selling at 20x earnings, this leads to an increase in US assets of US$ 10,000 billion, to be compared with a deterioration in the external debt situation of less than US$1,200 billion. Where is the lack of sustainability?
The conclusion is clear: if America’s wealth keeps growing about as fast as it has in the past decade (and we have no reason to believe that it will not), the current account will remain sustainable, whether it stays at $700 billion, falls to $500 billion or soars to $1 trillion a year.
Commentary: In a nutshell, because ROICs in the US are increasingly better than those in China, trade deficits (measured on net sales, not net profits) can easily be financed by selling small pieces of the valuable assets that produce high ROICs (e.g. equity in Dell). And this does not necessarily mean Americans become poorer over time, as the assets they own become more valuable over time by virtue of their rising ROIC.
Takeaways from the book
I loved the Economics vs Accounting example on the US trade deficit, and the case for higher profit margins
Gave was spot on that higher US profit margins were here to stay in 2005. This brought enormous incremental US wealth. Only selling a fraction of that incremental wealth balances out the trade deficit. 12 years later many investors (e.g. Jeremy Grantham, Hussman) are still trying to time the mean reversion of “inflated” US corporate profit margins
meta-takeaway for a value investor: some predictions did not come true: US housing did crash. If even the smartest macro managers are often wrong, is macro solely meant as a leisure activity for me?