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Larry Walters always wanted to fly. When he was old enough, he joined the Air Force, but he could not see well enough to become a pilot. After he was discharged from the military, he would often sit in his backyard watching jets fly overhead, dreaming about flying and scheming about how to get into the sky. On July 2, 1982, the San Pedro, California trucker finally set out to accomplish his dream. Because the story has been told in a variety of ways over a variety of media outlets, it is impossible to know precisely what happened but, as a police officer commented later, “It wasn’t a highly scientific expedition.”

Larry conceived his “act of American ingenuity” while sitting outside in his “extremely comfortable” Sears lawn chair. He purchased weather balloons from an Army-Navy surplus store, tied them to his tethered Sears chair and filled the four-foot diameter balloons with helium. Then, after packing sandwiches, Miller Lite, a CB radio, a camera, a pellet gun, and 30 one-pound jugs of water for ballast – but without a seatbelt – he climbed into his makeshift craft, dubbed “Inspiration I.” His plan, such as it was, called for him to float lazily above the rooftops at about 30 feet for a while, pounding beers, and then to use the pellet gun to explode the balloons one-by-one so he could float to the ground.

But when the last cord that tethered the craft to his Jeep snapped, Walters and his lawn chair did not rise lazily into the sky. Larry shot up to an altitude of about three miles (higher than a Cessna can go), yanked by the lift of 45 helium balloons holding 33 cubic feet of helium each. He did not dare shoot any of the balloons because he feared that he might unbalance the load and fall. So he slowly drifted along, cold and frightened, in his lawn chair, with his beer and sandwiches, for more than 14 hours. He eventually crossed the primary approach corridor of LAX. A flustered TWA pilot spotted Larry and radioed the tower that he was passing a guy in a lawn chair with a gun at 16,000 feet.

Eventually Larry conjured up the nerve to shoot several balloons before accidentally dropping his pellet gun overboard. The shooting did the trick and Larry descended toward Long Beach, until the dangling tethers got caught in a power line, causing an electrical blackout in the neighborhood below. Fortunately, Walters was able to climb to the ground safely from there.

The Long Beach Police Department and federal authorities were waiting. Regional safety inspector Neal Savoy said, “We know he broke some part of the Federal Aviation Act, and as soon as we decide which part it is, some type of charge will be filed. If he had a pilot’s license, we’d suspend that. But he doesn’t.” As he was led away in handcuffs, a reporter asked Larry why he had undertaken his mission. The answer was simple and poignant. “A man can’t just sit around,” he said.

The Inversion Principle

In one of the more glaringly obvious observations of all-time, it is safe to say that Larry’s decision-making process was more than a bit flawed. The Bonehead Club of Dallas awarded him its top prize – Bonehead of the Year – but he only earned an honorable mention from the Darwin Awards people, presumably because, even though things did not turn out exactly as he planned (another glaringly obvious observation), he was incredibly lucky and his flight did not end in disaster. Among his many errors, Larry did not follow the inversion principle popularized in the investment world by Charlie Munger. Charlie borrowed this highly useful idea from the great 19th Century German mathematician Carl Jacobi, who created this helpful approach for improving your decision-making process.

Invert, always invert (“man muss immer umkehren”).

Jacobi believed that the solution for many difficult problems could be found if the problems were expressed in the inverse – by working or thinking backwards. As Munger has explained, “Invert. Always invert. Turn a situation or problem upside down. Look at it backward. What happens if all our plans go wrong? Where don’t we want to go, and how do you get there? Instead of looking for success, make a list of how to fail instead – through sloth, envy, resentment, self-pity, entitlement, all the mental habits of self-defeat. Avoid these qualities and you will succeed. Tell me where I’m going to die, that is, so I don’t go there.” Charlie’s partner, Warren Buffett, makes a similar point: “Charlie and I have not learned how to solve difficult business problems. What we have learned is to avoid them.”

As in most matters, we would do well to emulate Charlie. But what does that mean?

It begins with working backwards, to the extent you can, quite literally. If you have done algebra, you know that reversing an equation is the best way to check your work. Similarly, the best way to proofread is back-to-front, one painstaking sentence at a time. But it also means much more than that.

Thinking in Reverse

Charlie’s inversion principle also means thinking in reverse. As Munger explains it: “In other words, if you want to help India, the question you should ask is not, ‘How can I help India?’ It’s, ‘What is doing the worst damage in India?’”

During World War II, the Allied forces sent regular bombing missions into Germany. The lumbering aircraft sent on these raids – most often B-17s – were strategically crucial to the war effort and were often lost to enemy anti-aircraft fire. That was a huge problem, obviously.

Boeing XB-17

One possible solution was to provide more reinforcement for the Flying Fortresses, but armor is heavy and restricts aircraft performance even more. So extra plating could only go where the planes were most vulnerable. The problem of where to add armor was a difficult one because the data set was so limited. There was no access to the planes that had been shot down. In 1943, the English Air Ministry examined the locations of the bullet holes on the returned aircraft and proposed adding armor to those areas that showed the most damage, all at the planes’ extremities.

The great mathematician Abraham Wald, who had fled Austria for the United States in 1938 to escape the Nazis, was put to work on the problem of estimating the survival probabilities of planes sustaining hits in various locations so that the added armor would be located most expeditiously. Wald came to a surprising and very different conclusion from that proposed by the Air Ministry. Since much of Wald’s analysis at the time was new – he did not have sufficient computing power to model results and did not have access to more recent statistical approaches – his work was ad hoc and his success was due to “the sheer power of his intuition” alone.

Wald began by drawing an outline of a plane and marking it where returning planes had been hit. There were lots of shots everywhere except in a few particular (and crucial) areas, with more shots to the planes’ extremities than anywhere else. By inverting the problem – considering where the planes that didn’t return had been hit and what it would take to disable an aircraft rather than examining the data he had from the returning bombers – Wald came to his unique insight, later confirmed by remarkable (for the time, and long classified) mathematical analysis (more here). Much like Sherlock Holmes and the dog that didn’t bark, Wald’s remarkable intuitive leap came about due to what he didn’t see (that Wald’s insight seems obvious now is a wonderful illustration of hindsight bias).

Wald realized that the holes from flak and bullets most often seen on the bombers that returned represented the areas where planes were best able to absorb damage and survive. Since the data showed that there were similar areas on each returning B-17 showing little or no damage from enemy fire, Wald concluded that those areas (around the main cockpit and the fuel tanks) were the truly vulnerable spots and that these were the areas that should be reinforced.

From a mathematical perspective, Wald considered what might have happened to account for the data he possessed. Therefore, what he did was to set the probability that a plane that took a hit to the engine managed to stay in the air to zero and thought about what that would mean. In other words, conceptually, he assumed that any hit to the engine would bring the plane down. Because planes returned from their missions with bullet holes everywhere but the engine, the other alternative was that planes were never hit in the engine. Thus, either the German gunfire hit every part of the plane but one, or the engine was a point of extreme vulnerability. Wald considered both possibilities, but the latter made much more sense.

The more useful data was in the planes that were shot down and unavailable, not the ones that survived, and had to be “gathered” by Wald via induction. This insight lies behind the related concepts we now call survivorship bias – our tendency to include only successes in statistical analysis, skewing or even invalidating the results – and selection bias – the distortions we see when the sample selection does not accurately reflect the target population. Thus, the fish you observe in a pond will almost certainly correspond to the size of the holes in your net. Inverting the problem allowed Wald to come to the correct conclusion, saving many planes (and lives).

This idea applies to baseball too. As I have argued before, the crucial insight of Moneyball was a “Mungeresque” inversion. In baseball, a team wins by scoring more runs than its opponent. The epiphany was to invert the idea that runs and wins were achieved by hits to the radical notion that the key to winning is avoiding outs. That led the story’s protagonist, general manager of the Oakland A’s Billy Beane, to “buy” on-base percentage cheaply because the “traditional baseball men” overvalued hits but undervalued on-base percentage even though it does not matter how a batter avoids making an out and reaches base.

Therefore, the key application of the Moneyball insight was for Beane to find value via underappreciated player assets (some assets are cheap for good reason) by way of an objective, disciplined, data-driven process that values OBP more than conventional baseball wisdom. In other words, as Michael Lewis explained, “it is about using statistical analysis to shift the odds [of winning] a bit in one’s favor” via market inefficiencies. As A’s Assistant GM Paul DePodesta said, “You have to understand that for someone to become an Oakland A, he has to have something wrong with him. Because if he doesn’t have something wrong with him, he gets valued properly by the marketplace, and we can’t afford him anymore.” Accordingly, Beane sought out players that he could obtain cheaply because their actual (statistically verifiable) value was greater than their generally perceived value.

The great Howard Marks has also applied this idea to the investing world:

“If what’s obvious and what everyone knows is usually wrong, then what’s right? The answer comes from inverting the concept of obvious appeal. The truth is, the best buys are usually found in the things most people don’t understand or believe in. These might be securities, investment approaches or investing concepts, but the fact that something isn’t widely accepted usually serves as a green light to those who’re perceptive (and contrary) enough to see it.”

The key investment application of the inversion principle, therefore, is that in most cases we would be better served by looking closely at the examples of people and portfolios that failed and why they failed instead of the success stories, even though such examples are unlikely to give rise to book contracts with six-figure advances. Similarly, we would be better served by examining our personal investment failures than our successes. Instead of focusing on “why we made it,” we would be better served by careful failure analysis and fault diagnosis. That is where the best data is and where the best insight may be inferred.

The smartest people may always question their assumptions to make sure that they are justified. The data set that was available to Wald was not a good sample. By inverting his thinking, Wald could more readily hypothesize and conclude that the sample was lacking.

Don’t Be Stupid

The inversion principle also means taking a step back (so to speak) to consider your goals in reverse. Our first goal, therefore, should not be to achieve success, even though that is highly intuitive. Note, for example, this recent list of 2017’s smartest companies, which focuses on “breakthrough technologies” and “successful” innovations. Instead, our first goal should be to avoid failure – to limit mistakes. Instead of trying so hard to be smart, we should invert that and spend more energy on not being stupid, in large measure because not being stupid is far more achievable and manageable than being brilliant. In general, we would be better off pulling the bad stuff out of our ideas and processes than trying to put more good stuff in.

As Munger has stated, “I think part of the popularity of Berkshire Hathaway is that we look like people who have found a trick. It’s not brilliance. It’s just avoiding stupidity.” Here is a variation: “we know the edge of our competency better than most. That’s a very worthwhile thing.” Buffett has a variation on this theme too: “Rule No. 1: Never lose money. Rule No. 2: Never forget rule No. 1.” Another is to be fearful when others are greedy and greedy when others are fearful. George Costanza has his own unique iteration (“If every instinct you have is wrong, then the opposite would have to be right”).

If we avoid mistakes we will generally win. By examining failure more closely, we will have a better chance of doing precisely that. Basically, negative logic works better than positive logic. What we know not to be true is much more robust that what we know to be true. Note how Michelangelo thought about his master creation, the David. He always believed that David was within the marble he started with. He merely (which is not to say that it was anything like easy) had to chip away that which was not David. “In every block of marble I see a statue as plain as though it stood before me, shaped and perfect in attitude and action. I have only to hew away the rough walls that imprison the lovely apparition to reveal it to the other eyes as mine see it.” By chipping away at what “did not work,” Michelangelo uncovered a masterpiece. There are not a lot of masterpieces in life, but by avoiding failure, we give ourselves the best chance of overall success.

As Charley Ellis famously established, investing is a loser’s game much of the time (as I have also noted before) – with outcomes dominated by luck rather than skill and high transaction costs. Charley employed the work of Simon Ramo, a scientist and statistician, from Extraordinary Tennis for the Ordinary Player, who showed that professional tennis players and weekend tennis players play a fundamentally different game. The expert player, playing another expert player, needs to win points affirmatively through good shot-making to succeed. The weekend player wins by not losing – keeping the ball in play until his or her opponent makes an error, because weaker players make many more errors.

“In expert tennis, about 80 per cent of the points are won; in amateur tennis, about 80 per cent of the points are lost. In other words, professional tennis is a Winner’s Game – the final outcome is determined by the activities of the winner – and amateur tennis is a Loser’s Game – the final outcome is determined by the activities of the loser. The two games are, in their fundamental characteristic, not at all the same. They are opposites.”

As Charlie wrote in a letter to Wesco Shareholders while he was chair of the company: “Wesco continues to try more to profit from always remembering the obvious than from grasping the esoteric. … It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent. There must be some wisdom in the folk saying, `It’s the strong swimmers who drown.’”

Moreover, it turns out that we can quantify this idea more precisely.

As Phil Birnbaum brilliantly suggested in Slate, not being stupid matters demonstrably more than being smart when a combination of luck and skill determines success. Suppose you are the GM of a baseball team and you are preparing for the annual draft. Avoiding a mistake helps more than being smart.

Suppose you have the 15th pick in the draft. You look at a player the Major League consensus says is the 20th best player and think he is better than that – perhaps the 10th best player. By contrast, the MLB consensus on another player is that he is the 15th best player but you think he is only the 30th best. What are the rewards and consequences if you are right about each player when the draft comes?

If the underrated player is available when your pick comes, you can snap him up for an effective gain of five spots. You get the 10th best player with the 15th pick. That is great. Of course, since everybody else is scouting too, you may not be the only one who recognizes the underrated player’s true value. Anybody with a pick ahead of you can steal your thunder. If that happens, your being smart did not help a bit.

If the overrated player is available when your turn comes up (in theory, he should be because he is the consensus 15th pick and you are picking 15th), you will pass on him, because you know he is not that good. If you had not done the scouting and done it right, you would have taken him with your 15th pick and suffered an effective loss of 15 spots by getting the 30th best player with the 15th pick. In that case, then, avoiding a mistake helped.

Moreover, and crucially, it does not matter if other teams scouted him correctly. You have dodged a bullet no matter what. Recognizing the undervalued player (being smart) only helps when you are alone in your recognition. Recognizing the overrated player (avoiding a mistake) always helps. Birnbaum’s moral: “You gain more by not being stupid than you do by being smart. Smart gets neutralized by other smart people. Stupid does not.” Thus, the importance of the error quotient becomes obvious (obviously, the lower the better).

The same principle can also be demonstrated mathematically, as Birnbaum also noted. Gather ten people and show them a jar that contains equal numbers of $1, $5, $20, and $100 bills. Pull one out, at random, so nobody can see, and auction it off. If the bidders are generally smart, the bidding should top out at just below $31.50 (how much less will depend on the extent of the group’s loss aversion), the value of the average bill {(1+5+20+100) ÷ 4}. If you repeat the process but this time let two prospective bidders see the bill you picked, what happens? If you picked a $100 bill, the insiders should be willing to pay up to $99.99 for the bill. Neither of them will benefit much from the insider knowledge. However, if it is a $1 bill, neither of the insiders will bid. Without that knowledge, each of the insiders would have had a one-in-10 chance of paying $31.50 for the bill and suffering a loss of $30.50. On an expected value basis, each gained $3.05 from being an insider. Avoiding errors matters more than being smart.

That investing successfully is really hard suggests to most of us that being really smart should be a big plus in investing. Yet while it can help, the existence of other smart people together with copycats and hangers-on greatly dilutes the value of being market-smart. On the other hand, the impact of bad decision-making stands alone. It is not lessened by the related stupidity of others. In fact, the more people act stupidly together, the greater the aggregate risk and the greater the potential for loss. This risk grows exponentially. Think of everyone piling on during the tech or real estate bubbles. When nearly all of us make the same kinds of poor decisions together – when the error quotient is especially high – the danger becomes enormous.


Science is perhaps the quintessential inversion. It is the most powerful tool there is for determining what is real and what is true, and yet it advances only by ascertaining what is false. In other words, it works due to disconfirmation rather than confirmation. As Munger observed about Charles Darwin: “Darwin’s result was due in large measure to his working method, which violated all my rules for misery and particularly emphasized a backward twist in that he always gave priority attention to evidence tending to disconfirm whatever cherished and hard-won theory he already had. In contrast, most people early achieve and later intensify a tendency to process new and disconfirming information so that any original conclusion remains intact. They become people of whom Philip Wylie observed: ‘You couldn’t squeeze a dime between what they already know and what they will never learn.’”

The Oxford English Dictionary defines the scientific method as “a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement and experiment, and the formulation, testing, and modification of hypotheses.” Science is about making observations and then asking pertinent questions about those observations. What it means is that we observe and investigate the world and build our knowledge base on account of what we learn and discover, but we check our work at every point and keep checking our work. It is inherently experimental. In order to be scientific, then, our inquiries and conclusions must be based upon empirical, measurable evidence. We will never just “know.”

The scientific method, broadly construed, can and should be applied not only to traditional scientific endeavors, but also, to the fullest extent possible, to any sort of inquiry into or study about the nature of reality, including investing. As I have noted before, the great physicist and Nobel laureate Richard Feynman even applied such experimentation to hitting on women. To his surprise, he learned that he (at least) was more successful by being aloof than by being polite or by buying a woman he found attractive a drink.

David Wootton’s brilliant book, The Invention of Science, makes a compelling case that modernity began with the scientific revolution in Europe, book-ended by Danish astronomer Tycho Brahe’s identification of a new star in the heavens in 1572, which proved that heavens were not fixed, and the publication of Isaac Newton’s Opticks in 1704, which drew conclusions based upon experimentation. In Wootton’s view, this was “the most important transformation in human history” since the Neolithic era and in no small measure predicated upon a scientific mindset, which includes the unprejudiced observation of nature, careful data collection, and rigorous experimentation. In his view, the “scientific way of thinking has become so much part of our culture that it has now become difficult to think our way back into a world where people did not speak of facts, hypotheses and theories, where knowledge was not grounded in evidence, where nature did not have laws.” I think Wootton’s claim is surely true, even if honored mainly in the breach.

The scientific approach was truly a new way of thinking (despite historical antecedents). Wootton shows that when Christopher Columbus came to the New World in 1492, he did not have a word to describe what he had done (or at least appeared to have done, with apologies to the Vikings). It was the Portuguese, the first global imperial power, who introduced the term “discovery” in the early 16th Century. There were other new words and concepts that were also important when trying to understand the scientific revolution, such as “fact” (only widely used after 1663), “evidence” (incorporated into science from the legal system) and “experiment.”

As Wootton explains, knowledge, as it was espoused in medieval universities and monasteries, was dominated by the ancients, the likes of Ptolemy, Galen, and Aristotle. Accordingly, it was widely believed that all of the most important knowledge was already known. Thus, learning was predominantly a backward-facing pursuit, about returning to ancient first principles, not pushing into the unknown. Indeed, Wootton details the emergence of fact and evidence as previously unknown terms of art. The modern scientific pursuit is the “formation of a critical community capable of assessing discoveries and replicating results.”

In its broadest context, science is the careful, systematic and logical search for knowledge, obtained by examination of the best available evidence and always subject to correction and improvement upon the discovery of better or additional evidence. That is the essence of what has come to be known as the scientific method, which is the process by which we, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world. Otherwise (per James Randi), we are doing magic, and magic simply does not work.

Aristotle, brilliant and important as he was, posited, for example, that heavy objects fall faster than lighter objects and that males and females have different numbers of teeth, based upon some careful – though flawed – reasoning. But it never seemed to have occurred to him that he ought to check. Checking and then re-checking your ideas or work offers evidence that may tend to confirm or disprove them. By collecting “a long-term data set,” per field biologist George Schaller, “you find out what actually happens.” Testing can also be reproduced by any skeptic, which means that you need not simply trust the proponent of any idea. You do not need to take anyone’s word for things — you can check it out for yourself. That is the essence of the scientific endeavor.

Science is inherently limiting, however. We want deductive proof in the manner of Aristotle, but have to settle for induction. That is because science can never fully prove anything. It analyzes the available data and, when the force of the data is strong enough, it makes tentative conclusions. Moreover, these conclusions are always subject to modification or even outright rejection based upon further evidence gathering. The great value of facts and data is not so much that they point toward the correct conclusion (even though they do), but that they allow us the ability to show that some things are conclusively wrong.

Science progresses not via verification (which can only be inferred) but by falsification (which, if established and itself verified, provides relative certainty only as to what is not true). That makes it unwieldy. Thank you, Karl Popper. In investing, as in science generally, we need to build our processes from the ground up, with hypotheses offered only after a careful analysis of all relevant facts and tentatively held only to the extent the facts and data allow.

In investing, much like science generally and as in life, if we avoid mistakes we will generally win. We all want to be Michael Burry, an investor who made a fortune because he recognized the mortgage bubble in time to act accordingly. However, becoming Michael Burry starts by not being Wing Chau, an investor of Lawn Chair Larry foolishness who got crushed when the mortgage market collapsed. In fact, we all suffered when the real estate bubble burst. When the error quotient is especially high, our risks grow exponentially. Success starts with avoiding errors and looking at problems and situations differently.

Invert. Always invert.


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What’s neat about this isn’t what’s changed. It’s what’s stayed the same.

The line, “One million titles, consistently low prices” seems like marketing guff. But it helps explain why Amazon has dominated where others have failed.

The allure of the Internet in 1995 was betting on change. New paradigms born. Old strategies discarded. Something requiring radically different thinking.

Yet Amazon’s focus from day one was as old as it gets. Selection and price. Businesses have pursued the idea for millennia.

Jeff Bezos once explained why this was critical:

I very frequently get the question: “What’s going to change in the next 10 years?” That’s a very interesting question.

I almost never get the question: “What’s not going to change in the next 10 years?” And I submit to you that that second question is actually the more important of the two.

You can build a business strategy around the things that are stable in time. In our retail business, we know that customers want low prices, and I know that’s going to be true 10 years from now. They want fast delivery; they want vast selection. It’s impossible to imagine a future 10 years from now where a customer comes up and says, “Jeff I love Amazon, I just wish the prices were a little higher.” Or, “I love Amazon, I just wish you’d deliver a little slower.” Impossible.

So we know the energy we put into these things today will still be paying off dividends for our customers 10 years from now. When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.

This is one of those important things that’s too basic for most smart people to pay attention to.


Things that change are amazing. They can fuel massive growth.

But change by itself is hard. Investors have to spot it before it’s obvious. Consumers have to change their behaviors to make it viable. Those two points repel each other like magnets. And things that change tend to keep changing. A company whose pitch is “We’re doing this entirely new thing” likely has to reinvent itself and its product line every year, maybe more. Each iteration is a front-line battle where you’re exhausted from the last war but overconfident from its victory. So the odds keep stacking against you. An investor hoping to ride successive changes in multiple industries over a 40-year career faces tenth-degree difficulty. Practically a claim of clairvoyance.

Change often creates bursts of opportunity. Huge opportunity, yes. But businesses and their investors need more than slippery bursts to succeed. They need endurance. And endurance resides in long-term bets. Things you can pour energy and capital into today with a reasonable chance of still bearing fruit ten years from now. Which tend to be things that are stable in time.

This might seem heretical to venture capital. Marc Andreessen was once asked how his investment style compared with Warren Buffett. He replied:

[Warren is] betting against change. We’re betting for change. When he makes a mistake, it’s because something changes that he didn’t expect. When we make a mistake, it’s because something doesn’t change that we thought would. We could not be more different in that way.

Seems directionally true. But I don’t think it’s that black and white. Both investors pursue the same things; they just weight them differently.

Every successful investment is some combination of change that drives competition and things staying the same that drives compounding. There are so few exceptions to this, regardless of size or industry.

Buffett has owned GEICO stock since 1951. During that time the company went from exclusively selling auto insurance to government employees in cafeterias, to selling several kinds of insurance to everyone on their iPhones. Analytics went from abacus to AI. These are not small changes. But one thing stayed the same, which is that an insurance company selling directly would have a cost and convenience advantage over those paying brokers. That’s been the driver of Buffett’s GEICO bet for 66 years. It’s timeless.

Andreessen Horowitz partner Frank Chen recently talked about two trends in insurance startups. One is better software. “Software will rewrite the entire way we buy and experience our insurance products,” he said. Second is capital structure. “We expect to see more crowdsourced insurance companies … it should be a cheaper way to pool capital.” Both innovations promise lower cost and added convenience. Which is as timeless as GEICO’s edge.

Investors weigh the importance of change and timelessness differently, but every great company has some element of both. The extremes are where things don’t work.

Take three companies in the 1990s: Sears, Beenz, and Amazon.

Sears bet the Internet changed nothing, to its detriment. Beenz bet the Internet changed everything – creating a points-based currency valid only at online merchants – to its detriment. Amazon bet the Internet changed distribution, but rooted its strategy in things that have never, and will never, change. It nailed the center of the Venn diagram of change on one side and timeless on the other. One drove competition, the other drove compounding. Every successful company does this.


In the last 100 years we’ve gone from horses to jets and mailing letters to Skype. But every sustainable business is accompanied by one of a handful of timeless strategies:

  • Lower prices.
  • Faster solutions to problems.
  • Greater control over your time.
  • More choices.
  • Added comfort.
  • Entertainment/curiosity.
  • Deeper human interactions.
  • Greater transparency.
  • Less collateral damage.
  • Higher social status.
  • Increased confidence/trust.

You can make big, long-term bets on these things, because there’s no chance people will stop caring about them in the future.


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Speaking in London, Federal Reserve chair Janet Yellen Tuesday predicted that the “the system is much safer and much sounder” and explained that the Federal Reserve is prepared to deal with numerous enormous shocks to the economy.

In her conversation with Lord Nicholas Stern, Yellen also went on to list the reasons that, thanks to central bank intervention, there is unlikely to be another financial crisis “in our lifetimes.”

For those who have lived through more than one business cycle, however, alarm bells tend to go off every time an economist, central banker or high-ranking government official declares that there’s little to no danger of economic turmoil in the near future.

There is a long history of spectacularly bad predictions being made shortly before economic crises. Famously, shortly before the Crash of 1929 — one of the earlier crises that occurred on the Federal Reserve’s watch — Herbert Hoover proclaimed that “We in America today are nearer to the final triumph over poverty than ever before in the history of any land.”

But, we certainly don’t have to go back that far.

Indeed, in the late 1990s, it became nearly routine to hear economists announce that “the internet changes everything” and “the business cycle is dead.”

Economist Rudi Dornbusch — a close associate of current Fed vice chair Stanley Fischer — even wrote a July 1998 column in the Wall Street Journal titled “Growth Forever.” Dornbusch concluded that the possibility of an imminent recession “is remote” and the country “will not see a recession for years to come.” So sure of the benefits of the “new economy” was Dornbusch, in fact, that he declared, “This expansion will run forever.”

Then came the dot-com bust of 2001. After that came a short expansion from 2002 to 2007. After that came the Great Recession.

Meanwhile, from 2000 to 2015, according to the federal government’s data, real median household income was flat. Only over the past two years have we seen any of that expansion that many were venturing to say was permanent back in the late 1990s.

Economists and policymakers were no more insightful when examining the possibility of a new crisis post-2007.

In 2005, for example, Milton Friedman could have been paraphrasing Yellen’s Tuesday comments when he concluded that “the stability of the economy is greater than it has ever been in our history. We really are in remarkable shape.” Friedman went on to give Alan Greenspan credit for the expansion.

In early 2007, Ben Bernanke predicted, “We’ll see some strengthening in the economy sometime during the middle of the new year.”

As late of mid-2007, Bernanke was downplaying any problems associated with the sub-prime housing market, allaying any fears of a bubble or bust and claiming, “I don’t know whether prices are exactly where they should be, but I think it’s fair to say that much of what’s happened [i.e, enormous home price growth during the housing bubble] is supported by the strength of the economy.”

If housing bubbles do prove to be a problem, Bernanke concluded, it’s “mostly a localized problem and not something that’s going to affect the national economy.”

The US would officially begin to contract in December 2007, followed by a financial crisis the following autumn.

Even on the eve of the crisis — in September 2008 — John McCain announced that “the fundamentals of our economy are strong.”

A year later, the unemployment rate would reach 10 percent, foreclosure rates were surging and total employment would collapse from 116 million to 107 million. Employment would not return to pre-crisis levels until late 2013.

Millions of workers would need to change careers, be retrained, scratch for other forms of income to avoid foreclosure or eviction and put off retirement indefinitely. The economy was so weak for so long, in fact, that the Fed felt it necessary to keep the key target interest rate near zero for seven years to add “stimulus.”

Of course, just because Janet Yellen says the economy won’t experience a crisis anytime soon doesn’t mean a crisis is imminent. A truly strong economy isn’t going to be “jinxed” by a declaration that things are fine. On the other hand, given the record of eminent economists and Fed board members in the past, Yellen’s predictions are hardly anything that should inspire confidence.

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There is a set of trading rules that only fails [true positive] 1/1000 times. However, when entering the trade, there is a 5% false positive rate in screening trades, which can take you out of the trade based on your risk management rules, thereby missing the return. You are only authorised by your partner/manager/firm to take a maximum of 10 trades each year. Trades are screened through this rule set randomly from all manner of securities, [stocks, futures, options]. A trade is screened and it is positive. What is the probability that the trade will fail? Will you take the trade?

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Back in the days of printed newspapers, magazines, and newsletters the acquisition of news and information was easier, or so it seemed.  The reason it seemed easier is that there was much less of it.  Today, with the internet, 24-hour financial media, blogs, and every conceivable method of acquisition, information is overwhelming.  Once I realized that some information was actionable and most of the rest was categorized as observable, then things became greatly simplified.  Hopefully this article will shed some light on how to separate actionable information from the much larger observable information.  As you can see from the Webster definitions below they initially do not seem that different.

Actionable – able to be used as a basis or reason for doing something or capable of being acted on.

Observable – possible to see or notice or deserving of attention; noteworthy.

However, when real money gets involved the difference can be significant.  Let me give you my definition and then follow up with some scenarios.  The world is full of observable information being dispensed as if it is actionable.  All the experts, television pundits, talking heads, economists (especially them), most newsletter writers, most blog authors, in fact most of the stuff you hear in regard to the markets is rarely actionable.  Actionable means that you, upon seeing it, can make a decision to buy, sell, or do nothing – period.

I’ll start by mentioning Japanese candle patterns, a subject I beat to death in this blog over the past few months.  I have never stated anything other than the fact that Japanese candle patterns should never be used in isolation; you should always use them in concert with other technical tools.  Hence, Japanese candle patterns for me are observable information; not actionable.  Only when backed up by Western technical tools can they become actionable.  I demonstrated with in my article Candlestick Analysis – Putting It All Together.

Too often I hear the financial media discussing economic indicators and how they affect the stock market.  Initially it seems they forget the stock market is one of the components of the index of LEADING indicators; in other words, the stock market is better at predicting the economy.  First of all, economics can never be proved right or wrong since it is an art, just like technical analysis.  Economic data is primarily monthly, often quarterly, and occasionally weekly.  It gets rebased periodically and often gets adjusted for seasonal affects and everything else you can think of.  It just cannot reliably provide any valuable information to a trader or investor.  However, boy does it sound really good when someone creates a great story around it and how at one time in the past it occurred at a market top; it is truly difficult to ignore.  Ignore you should!  The beauty of the data generated by the stock market, mainly price, is that it is an instantaneous view of supply and demand.  I have said this a lot on these pages, but it needs to be fully understood.  The action of buyers and sellers making decisions and taking action is determined by price, and price alone.  The analysis of price at least is a first step to obtaining actionable information.  Using technical tools that help you reduce price into information that you can rely upon is where the actionable part surfaces.

I also seriously doubt anyone relies totally upon one technical tool or indicator.  If they do, then probably not for long.  I managed a lot of money using a weight of the evidence approach which means I used a bunch of indicators from price, breadth, and relative strength (called it PBR – see graphic).  Each individual indicator could be classified as observable, but when used in concert with others, THEY became actionable.

I think the point of this entire article is to alert or remind you that there is a giant amount of information out there and that most of it is not actionable; it is only observable.  Sometimes it is difficult to tell the difference so just think about putting real money into a trade based upon what you hear or read.  Real money separates a lot of people from making decisions based upon observable information, no matter how convincing it is.

I am really looking forward to speaking at ChartCon 2016.  The schedule shows me on at 10:30am PT where I’ll talk about the marketing of Wall Street disguised as research and show a couple of things about Technical Analysis that annoy me.

Dance with the Actionable Information,

Greg Morris


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Stats in law has been one of my bug-bears for quite some time. Lawyers, being very language based [as a rule] don’t seem to have any mathematical skills. My own law class, struggle to work out a basic % calculation.

Statistical significance, particularly when looking at ‘causation’ in law, has been a killing field for accurate legal argument.

As the American Statistical Association said,

Researchers often wish to turn a p-value into a statement about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself. …

Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply a lack of importance or even lack of effect. Any effect, no matter how tiny, can produce a small p-value if the sample size or measurement precision is high enough, and large effects may produce unimpressive p-values if the sample size is small or measurements are imprecise.


The Court in the In re Chantix litigation got it exactly right:

While the defendant repeatedly harps on the importance of statistically significant data, the United States Supreme Court recently stated that “[a] lack of statistically significant data does not mean that medical experts have no reliable basis for inferring a causal link between a drug and adverse events …. medical experts rely on other evidence to establish an inference of causation.” Matrixx Initiatives, Inc. v. Siracusano,––– U.S. ––––, 131 S.Ct. 1309, 1319, 179 L.Ed.2d 398 (2011). The Court further recognised that courts “frequently permit expert testimony on causation based on evidence other than statistical significance.” Id.; citing Wells v. Ortho Pharmaceutical Corp., 788 F.2d 741, 744–745 (11th Cir.1986). Hence, the court does not find the defendant’s argument that Dr. Furberg “cannot establish a valid statistical association between Chantix and serious neuropsychiatric events” to be a persuasive reason to exclude his opinion, even if the court found the same to be true.


IN THE SEARCH FOR THE NEW and different indexes that will power a new and different ETF, back-testing plays a critical role. Index providers, including S&P, Dow Jones, MSCI, Russell, Zacks and others can index just about anything. You want to rank the companies in the S&P 500 by earnings growth, then take the 50 top firms and weight them equally? Weight them by market capitalization? Go long the top 50 and short the bottom 50? They can build it. And then they back-test it. Indexes that look good in hindsight have a shot to become ETFs. Those that don’t, don’t.

Given our quantitative roots, we are sympathetic to the fact that backtests are often used as an input into making investment decisions. But past returns, as we all know, do not predict the future. And we think backtested results may be particularly problematic today. Very little fundamental data for US equities extends back more than 30 years, but the last 30 years were a period generally accompanied by two related phenomena: increasingly easy monetary policy and falling interest rates. In particular, the wave of liquidity and stimulus provided in the wake of the Tech Bubble coincided with unprecedented levels of credit expansion, rising asset correlations and record earnings volatility.




I’ve been in Court for the last few days sitting on a jury. Yesterday, Counsel for the defense initiated their open, and called an expert witness: he was an image analyst. His CV, which he read out in Court was very impressive, and I was looking forward to an interesting and technical discussion: to this point the Crown and Police evidence had been a shambles of inepeptitude.

Essentially we were told the following: [i] there are 3 tests performed in an image analysis [ii] the methodology is specific in each test [iii] a conclusion is reached that rates from 1-6: 1 = no correlation, 6 = high correlation

The images were of an aggravated robbery of a petrol station by two armed robbers, and the analysis was designed to ascertain whether the defendent was in point of fact one of the robbers pictured.

Test 1 is the measurement of anatomical landmarks of the face: it is a quantitative test. Test 2 is an analyst’s visual subjective analysis of facial features. Test 3 is manipulation of the images: the images taken from the robbery, plus control images taken from the defendent. The two images are mixed and matched via technology and computers.

Due to hats and bandana’s worn by the robbers, only partial facial features were visible, thus, Test 1 and Test 3 could not be performed. This left only Test 2 the subjective assessment.

The Crown Prosecutor had a chance to cross examine after the presentation of the defense evidence. Did the Crown ever drop the ball. The problem was that the Crown had no idea of statistical investigation and probabilities.

The defense evidence resulted in a conclusion of 1 on the 1-6 scale, based on the following 4 significant differences. [i] the sideburns were different [ii] the robber had some undefined mark/depression on his right cheek [iii] the bridge of the nose appeared farther forward [iv] differences in the shape of various points of the ear.

This analysis could have been attacked immediately on the following basis: what is your confidence level in your conclusion? Now anyone who is vaguely versed in statistics knows that this is a basic, basic question.

The confidence level is calculated from your margin of error, or Z-score, your population or sample size, the distribution, which will provide you with your confidence level expressed as a +/-

The ideal outcome being a high confidence level at least 95%+ and a small margin of error, which you generate from your sample size and selected Z-score.

When you want to estimate a probability, a quick and dirty formula for the required sample size you need to get within the margin of error you need is n = 1/[MOE]*

The problem that the Crown sensed, but couldn’t quite get to, was the sample size of 1. Obviously a sample size this small is going to, particularly in light of the utter subjectiveness of the only Test applied, is going to provide a very low confidence level, and thus a very low probability of the conclusions drawn being statistically significant, and thus the testimony of the expert being taken with a large pinch-of-salt. Certainly his conclusion of 1 on his scale I feel was untenable.

Nonetheless, an interesting couple of days to observe the State in action. If I was concerned before, I have to say that now I am absolutely terrified. The level of ignorance, incompetence and total arrogance has to be witnessed first hand to appreciate just how bad things really are.

Wood has crunched the numbers on the distribution of closing prices. Guess what? They’re not a Gaussian distribution…what a shocker!

Read Mandelbrot for the research and mathematics into “Power laws” that can explain the distributions that occur on a fairly regular basis on Wall St.

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