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The Danger of “Vision macro” and How to Avoid It

“Our knowledge can only be finite, while our ignorance must necessarily be infinite.” ~ Karl Popper

Something that’s helped me in my trading journey is adopting the practice of multivariate critical thinking (MCT).

MCT is the application of considering multiple hypotheses and then dispassionately applying critical judgment to each. It requires updating the probabilistic weighting for each, as new information becomes available — practical Bayesian analysis.

This seems simple and easy, and on the surface it is, or at least should be. But the difficulties come in its application. Specifically concerning the “dispassionate” part.

The problem is — at least it was for me — is that we’re naturally linear thinkers inhabiting a non-linear world. And to top things off, we have a stubborn ego-sensitive brain that wants simple answers and to always “be right”.

It’s a tough circle to square for sure. But not impossible.

If you wonder if you suffer from ego dominated linear thinking (let’s call this EDLT), just think back to the last time you were wrong on a trade or a market call. And ask yourself, did you get upset? Did you not enjoy the process of “being wrong”? Did the most dangerous four-letter word in trading, “HOPE”, become a part of your thought process? If the answer is yes, and it will be for the majority of people, then you’re practicing our instinctual EDLT.

When operating in EDLT mode the brain latches onto a hypothesis and then focuses in on “being right”. It doesn’t do this through objective assessment but rather by employing a tool kit full of anchoring, confirmation biases, and heuristics to cherry-pick information that supports it in “being right”.

The irony is that by focusing on “being right”, we more often than not end up “being wrong”.

This is an important habit to break. Doing so leads to better outcomes.

Practicing MCT is invaluable because it focuses the brain on finding the errors in its own logic. Because the brain no longer has to defend a single thesis but instead is forced to continuously analyze and weight numerous possible outcomes. Which switches its focus from trying to “be right” to figuring out how it’s wrong. This makes you better at “being wrong”. And being better at “being wrong” is vital to making money.

The trader who most fully practices MCT is George Soros. He said the following about this process:

The Secret to my success is that I’m always wrong. I’m ALWAYS wrong. And I try to correct my mistakes. That’s the secret of my success.

And

My approach works not by making valid predictions, but by allowing me to correct false ones.

Most read these statements from Soros and think he’s being glib. That’s because they’re playing the “being right” game. They’re too busy looking for some “secret” that’s going to help them “be right” more. But all they need to do is “invert, always invert!” their thinking process.

Soros adopted this unnatural mode of analyzing the world from philosopher Karl Popper. Popper established the epistemological philosophy of critical rationalism. He took these ideas from David Hume, who took them from Pyrrhonian skepticism, which evolved out of the ancient school of Indian Philosophy, Carvaka. At the foundation of all of these schools of thought is that “nothing can be known for certain”.

Since nothing can be known for certain we need to be good at being uncertain. The MCT framework helps us to do do just that. The Philosopher from Drobny’s The Invisible Hands discusses how this works (emphasis mine).

I try to develop a hypothesis about how the world is working and how it could work in the future. Therefore, information to us is a collection of theories and ideas, together with evidence that either supports or falsifies these theories and ideas. Information can come from fundamental economic drivers, such as growth, inflation, and other variables, or it can come from more technical, market-based factors such as flows, liquidity, etc.

We are not engaged in what I describe as “vision macro,” whereby one tries to work out some kind of single truth about how the world works. Rather, we form a probabilistic set of hypotheses about how the world could look and what might drive markets going forward, focusing on the market impact in all scenarios and looking for good risk-versus-reward trades around these hypotheses.

“Vision macro” is standard EDLT. There’s plenty of examples of EDLT thinkers in the finance space; they are a dime a dozen on the fintwit. Their modus operandi is to lock onto a “single truth” and become a torchbearer for that narrative. They make themselves champions for their “truth”. As a result, they blind themselves stupid and end up fighting the market for LONG periods.

The old Wall St. adage that it’s not about being right but about making money is an adage for a reason. You only need to be “right” at the right time and the way to do that is by being good at “being wrong”. Critically assessing multiple hypotheses with MCT is the way you get there.

EDLT is centered around seeking out confirming evidence. MCT looks for disconfirming information. EDLT is fragile while MCT is robust. Most people utilize the former because practicing the latter is tough.

If you find yourself latched onto a “single truth”, Karl Popper would ask you “how would you disprove yourself?”

Drop the search for grand narratives and invert your thinking.

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My Notes on the Druckenmiller Real Vision Interview

Alex here.

The Druckenmiller Real Vision interview is well worth the watch if you have a subscription and 90 minutes to spare. And if you don’t, you’re in luck because I’m sharing with you my notes along with some of my thoughts on what the GOAT said.

Let’s begin…

The start of the interview was by far my favorite part and really blew me away.

Stanley Druckenmiller opened the conversation by looking straight into the screen and then spoke some words I’ll never forget. He said, “Alex Barrow, I am your real biological father…” My jaw dropped even though this was something I’ve always kind of suspected. I mean, just look at the photo of me and my dog below. The resemblance is pretty uncanny. It’s nice to finally know the truth for certain.

Now that I’m done showing off my photoshopping skills, let’s get to the real stuff.

13D founder, Kiril Sokoloff, leads the interview and he and Druck discuss a wide range of topics including his views on the current macro environment, the diminishing signal of price action due to the rise of algorithmic trading, central bank policy, and then my favorite which was his thoughts on trade and portfolio management.

Here’s Druck talking about the difficulty he’s been having in this low rate environment, and how he’s made the vast majority of his money in bear markets (with emphasis by me).

Yeah, well, since free money was instituted, I have really struggled. I haven’t had any down years since I started the family office, but thank you for quoting the 30-year record. I don’t even know how I did that when I look back and I look at today. But I probably made about 70% of my money during that time in currencies and bonds, and that’s been pretty much squished and become a very challenging area, both of them, as a profit center.

So while I started in equities, and that was my bread and butter on my first three or four years in the business, I evolved in other areas. And it’s a little bit of back to the future, the last eight or nine years, where I’ve had to refocus on the equity market. And I also have bear-itis, because I made– my highest absolute returns were all in bear markets. I think my average return in bear markets was well over 50%. So I’ve had a bearish bias, and I’ve been way too cautious the last, say, five or six years. And this year is no exception.

It’s no secret the central bank suppressed rate environment has hurt practitioners of old school macro, such as Druck and PTJ. When these guys began their careers they could park their money in 2-year rates and capture high single to double-digit rates.

Not only did this jack up their returns but higher interest rates and inflation caused more volatility and action in markets. And exploiting volatility is the lifeblood of old school macro traders. Like Druck said, he made his highest returns during bear markets.

The last decade of extremely low-interest rates and dovish Fed policy has suppressed volatility, leading to smoother trend paths. This has led to more capital flowing into passive indexing and less to active managers, which in itself helps to extend the trend of less volatile markets; at least to a point.

Eventually, this low rate regime will reverse. We’ll see higher inflation and a secular rise in interest rates. In fact, this is one my highest conviction ideas for the next secular cycle. The massive debt and unfunded obligations in developed markets, along with the secular rise in populism, nearly ensures that we’ll see profligate government spending and competitive devaluations in the decade ahead.

So we’ll see the rise of volatility and an environment conducive to old school macro once again!

Here’s Druck discussing the major macro thematics he’s been tracking this year.

I came into the year with a very, very challenging puzzle, which is rates are too low worldwide.

You have negative real rates. And yet you have balance sheets being expanded by central banks, at the time, of a trillion dollars a year, which I knew by the end of this year was going to go to zero because the US was obviously going to go from printing money and QE to letting $50 billion a month, starting actually this month, runoff on the balance sheet. I figured Europe, which is doing $30 billion euros a month, would go to zero.

So the question to me was, if you go from $1 trillion in central bank buying a year to zero, and you get that rate of change all happening within a 12-month period, does that not matter if global rates are still what I would call inappropriate for the circumstances? And those circumstances you have outlined perfectly. You pretty much have had robust global growth, with massive fiscal stimulus in the United States, where the unemployment rate is below 4. If you came down from Mars, you would probably guess the Fed funds rate would be 4 or 5/ and you have a president screaming because it’s at 175.

I, maybe because I have a bearish bias, kind of had this scenario that the first half would be fine, but then by July, August, you’d start to discount the shrinking of the balance sheet. I just didn’t see how that rate of change would not be a challenge for equities, other than PEs, and that’s because margins are at an all-time record. We’re at the top of the valuation on any measures you look, except against interest rates. And at least for two or three months, I’ve been dead wrong.

So that was sort of the overwhelming macro view. Interestingly, some of the things that tend to happen early in a monetary tightening are responding to the QE shrinkage. And that’s obviously, as you’ve cited, emerging markets.

We talked about this obvious market mispricing in our latest MIR, The Kuhn Cycle (Revisited). The old narrative of low rates for longer had become extremely entrenched. And this narrative consensus has created a certain amount of data blindness, as is typical with popular and enduring narratives. This data blindness has led to a large mispricing of interest rates, particularly in developed markets.

Where things get interesting is all the corollaries that stem from a low rate narrative like this. Think of the billions of dollars that have poured into private equity over the last decade. The current PE model is predicated on the assumption of interest rates staying low, which is needed for their businesses’ long-term funding needs and justification for their sky-high valuations etc…

A really interesting section of the interview is when Druck talks about the diminishing signaling value in price action. He says:

The other thing that happened two or three months ago, mysteriously, my retail and staple shorts, that have just been fantastic relative to my tech longs, just have had this miraculous recovery. And I’ve also struggled mightily– and this is really concerning to me. It’s about the most trouble I’ve been about my future as a money manager maybe ever is what you mentioned– the canceling of price signals.

But it’s not just the central banks. If it was just the central banks, I could deal with that. But one of my strengths over the years was having deep respect for the markets and using the markets to predict the economy, and particularly using internal groups within the market to make predictions. And I think I was always open-minded enough and had enough humility that if those signals challenged my opinion, I went back to the drawing board and made sure things weren’t changing.

There are some great nuggets in here. I’ve long thought that one of the most important skill sets of a great trader — and something Druck has in spades — is to be extremely flexible mentally; never marrying oneself to a viewpoint or thesis and continuously testing hypotheses against the price action of the market.

Market Wizard Bruce Kovner said he owed much of his success to this, saying:

One of the jobs of a good trader is to imagine alternative scenarios. I try to form many different mental pictures of what the world should be like and wait for one of them to be confirmed. You keep trying them on one at a time. Inevitably, most of these pictures will turn out to be wrong — that is, only a few elements of the picture may prove correct. But then, all of a sudden, you will find that in one picture, nine out of ten elements click. That scenario then becomes your image of the world reality.

And Livermore noted the importance of flexibility when he wrote, “As I said before, a man does not have to marry one side of the market till death do them part.”

Now compare this to the “Fintwit experts” who have peddled a doom and gloom outlook for the last 7 years without ever taking a step back to maybe rejigger the models they use to view the world, which have been so consistently wrong.

Anyways, Druck then goes on to lay out the cause behind the diminishing power of price signals.

These algos have taken all the rhythm out of the market and have become extremely confusing to me. And when you take away price action versus news from someone who’s used price action news as their major disciplinary tool for 35 years, it’s tough, and it’s become very tough. I don’t know where this is all going. If it continues, I’m not going to return to 30% a year any time soon, not that I think I might not anyway, but one can always dream when the free money ends, we’ll go back to a normal macro trading environment.

The challenge for me is these groups that used to send me signals, it doesn’t mean anything anymore. I gave one example this year. So the pharmaceuticals, which you would think are the most predictable earning streams out there– so there shouldn’t be a lot of movement one way or the other– from January to May, they were massive underperformers. In the old days, I’d look at that relative strength and I’ll go, this group is a disaster. OK. Trump’s making some noises about drug price in the background.

But they clearly had chart patterns and relative patterns that suggest this group’s a real problem. They were the worst group of any I follow from January to May, and with no change in news and with no change in Trump’s narrative, and, if anything, an acceleration in the US economy, which should put them more toward the back of the bus than the front of the bus because they don’t need a strong economy.

They have now been about the best group from May until now. And I could give you 15 other examples. And that’s the kind of stuff that didn’t used to happen. And that’s the major challenge of the algos for me, not what you’re talking about.

Well, I’ll just, again, tell you why it’s so challenging for me. A lot of my style is you build a thesis, hopefully one that no one else has built; you sort of put some positions on; and then when the thesis starts to evolve, and people get on and you see the momentum start to change in your favor, then you really go for it. You pile into the trade. It’s what my former partner George Soros was so good at. We call it– if you follow baseball, it’s a slugging percentage, as opposed to batting average.

Well, a lot of these algos apparently are based on standard deviation models. So just when you would think you’re supposed to pile on and lift off, their models must tell them, because you’re three standard deviations from where you’re supposed to be, they come in with these massive programs that go against the beginning of the trend. And if you really believe in yourself, it’s an opportunity. But if you’re a guy that uses price signals and price action versus news, it makes you question your scenario.

So they all have many, many different schemes they use, and different factors that go in. And if there’s one thing I’ve learned, currencies probably being the most obvious, every 15 or 20 years, there is regime change. So currency is traded on current account until Reagan came in and then they traded on interest differentials. And about five years, 10 years ago, they started trading on risk-on, risk-off. And a lot of these algos are built on historical models. And I think a lot of their factors are inappropriate because they’re missing– they’re in an old regime as opposed to a new regime, and the world keeps changing. But they’re very disruptive if price action versus news is a big part of your process, like it is for me.

If you’ve been trading for any significant amount of time then you’ve certainly noticed the change in market action and tone due to the rise of algorithmic trading over the last decade. There’s often little rhyme or reason behind large inter-market moves anymore. Moves can simply happen because, as Druck said, algos that run on standard deviation models determine one sector has advanced too much relative to another, so the computers start buying one and sell another.

What we can do as traders now is to evolve and adapt. Work to understand what the popular models are that drive these algos so we can understand when they’re likely to buy and sell.

Also, I love his line about how he works to build a thesis and a position when he says:

A lot of my style is you build a thesis. Hopefully, one that no one else has built; you sort of put some positions on; and then when the thesis starts to evolve, and people get on and you see the momentum start to change in your favor, then you really go for it. You pile into the trade.

This a great lesson in trade management and how to build into a position using the market as a signal.

Druck also talked about Google (one of our largest positions) and reveals how he looks at some of the tech stocks that are popularly thought of as “overvalued” by the market.

I guess, let’s just take Google, OK, which is the new bad boy, and they’re really a bad boy because they didn’t show up at the hearing. They had an empty chair because they only wanted to send their lawyer.

But it’s 20 times earnings. It’s probably 15 times earnings after cash, but let’s just say it’s 20 times. Let’s forget all that other stuff. And they’re under earning in all these areas, and losing money they could turn it off. And then I look at Campbell’s Soup and this stuff selling at 20 times earnings.

And they’re the leaders in AI– unquestioned leaders in AI. There’s no one close. They look like they’re the leaders in driverless car. And then they just have this unbelievable search machine. And one gets emotional when they own stocks, when they keep hearing about how horrible they are for consumers.

I wish everyone that says that would have to use a Yahoo search engine. I’m 65, and I’m not too clever, and every once in a while, I hit the wrong button and my PC moves me into Yahoo. And Jerry Yang’s a close friend so I hate to say this, but these things are so bad.

And to hear the woman from Denmark say that the proof that Google is a monopoly and that iPhones don’t compete with Android is that everyone uses the Google search engine is just nonsense. You’re one click away from any other search engine.

I just I wish that woman would have to use a non-Google search engine for a year– just, OK, fine, you hate Google? Don’t use their product, because it’s a wonderful product. But clearly, they are monopolies. Clearly, there should be some regulation. But at 20 times earnings and a lot of bright prospects, I can’t make myself sell them yet.

Kiril then asks Druck about portfolio construction and how he builds positions, which was one of my favorite parts of the interview.

Kiril: When you worked with Soros for 12 years, one of the things that you said you learned was to focus on capital preservation and taking a really big bet, and that many money managers make all their money on two or three ideas and they have 40 stocks or 40 assets in their portfolio.

And it’s that concentration that has worked. Maybe you could go into that a bit more, how that works, how many of those concentrated bets did work, when you decided to get out if it didn’t work, do you add when the momentum goes up assuming the algos don’t interfere with it?

Druck: As the disclaimer, if you’re going to make a bet like that, it has to be in a very liquid market, even better if it’s a liquid market that trades 24 hours a day. So most of those bets, for me, invariably would end up being in the bond and currency markets because I could change my mind. But I’ve seen guys like Buffett and Carl Icahn do it in the equity markets. I’ve just never had the trust in my own analytical ability to go in an illiquid instrument, which in equity is if you’re going to bet that kind of size on– you just have to be right.

But to answer your question, I’ll get a thesis. And I don’t really– I like to buy not in the zero inning and maybe not in the first inning, but no later than the second inning. And I don’t really want to pile on in the third or fourth or fifth inning.

But even against the dollar, it’s not all-in right away. Normally, I’ll wait for– I’ll go in with, say, a third of a position and then wait for price confirmation. And when I get that, when I get a technical signal, I go.

I had another very close experience with the success of the Deutschmark, which was the euro. I can’t remember– I think it was 2014 when the thing was at 140, and they went to negative interest rates. It was very clear they were going to trash that currency, and the whole world was long the euro. And it would go on for years. I’d like to say I did it all at 139, and I did a whole lot, but I got a lot more brave when it went through to 135. And that’s a more normal pattern for me.

We write a lot about the importance of concentrating your bets due to the natural power law distribution of returns (here’s a link).

This part of the interview was great because it shows how Druck uses a confluence of factors to leg into a trade. He says he develops a theory then waits for the market to begin to validate that theory and he puts a small (usually ⅓ position on). He then waits for further market confirmation that he’s correct (he calls this point the second inning) at which time he piles in and goes for the jugular.

The chart below illustrates perfectly his short EURUSD trade.

Here’s Druck talking about the 2000 tech bubble and what made him turn bearish.

Then there would be this strange case of 2000, which is kind of my favorite, and involves some kind of luck. I had quit Quantum, and Duquesne was down 15%. And I had given up on the year and I went away for four months, and I didn’t see a financial newspaper. I didn’t see anything.

So I come back, and to my astonishment, the NASDAQ has rallied back almost to the high, but some other things have happened– the price of oil is going up, the dollar is going way up, and interest rates were going up— since I was on my sabbatical. And I knew that, normally, this particular cocktail had always been negative for earnings in the US economy. So I then went about calling 50 of my clients– they stayed with me during my sabbatical– who are all small businessmen. I didn’t really have institutional clients. I had all these little businessmen. And every one of them said their business was terrible.

So I’m thinking, this is interesting. And the two-year is yielding 6.04, not that I would remember, and Fed funds were 6 and 1/2. So I start buying very large positions in two and five-year US treasuries. Then, I explained my thesis to Ed Hyman, and I thought that was the end of it. And three days later, he’s run regression analysis– with the dollar interest rates and oil, what happens to S&P earnings? And it spit out, a year later, S&P earnings should be down 25%, and the street had them up 18.

So I keep buying these treasuries, and Greenspan keeps giving these hawkish speeches, and they have a bias to tighten. And I’m almost getting angry. And every time, he gives a speech, I keep buying more and more and more. And that turned out to be one of the best bets I ever made. And again, there was no price movement, I just had such a fundamental belief. So sometimes it’s price, sometimes it’s just such a belief in the fundamentals.

Higher oil, higher dollar, and higher interest rates is likely to eventually lead to a negative earnings surprise for us as well; though that’s probably at least a few quarters if not further away.

Kiril then asks Druck about how he manages a drawdown. What he does emotionally and practically to stage a comeback.

Kiril: One of the great things I understand you do is when you’ve had a down year, normally a fund manager would want to get aggressive to win it back. And what you’ve told me you do, you take a lot of little bets that won’t hurt you until you get back to breakeven. It makes a tremendous amount of sense. Maybe you could just explore that a little bit with me.

Druck: Yeah, one of the lucky things was the way my industry prices is you price– at the end of the year, you take a percentage of whatever profit you made for that year. So at the end of the year, psychologically and financially, you reset to zero. Last year’s profits are yesterday’s news.

So I would always be a crazy person when I was down end of the year, but I know, because I like to gamble, that in Las Vegas, 90% of the people that go there lose. And the odds are only 33 to 32 against you in most of the big games, so how can 90% lose? It’s because they want to go home and brag that they won money. So when they’re winning and they’re hot, they’re very, very cautious. And when they’re cold and losing money, they’re betting big because they want to go home and tell their wife or their friends they made money, which is completely irrational.

And this is important, because I don’t think anyone has ever said it before. One of my most important jobs as a money manager was to understand whether I was hot or cold. Life goes in streaks. And like a hitter in baseball, sometimes a money manager is seeing the ball, and sometimes they’re not. And if you’re managing money, you must know whether you’re cold or hot. And in my opinion, when you’re cold, you should be trying for bunts. You shouldn’t be swinging for the fences. You’ve got to get back into a rhythm.

So that’s pretty much how I operated. If I was down, I had not earned the right to play big. And the little bets you’re talking about were simply on to tell me, had I re-established the rhythm and was I starting to make hits again? The example I gave you of the Treasury bet in 2000 is a total violation of that, which shows you how much conviction I had. So this dominates my thinking, but if a once-in-a-lifetime opportunity comes along, you can’t sit there and go, oh well, I have not earned the right.

Now, I will also say that was after a four-month break. My mind was fresh. My mind was clean. And I will go to my grave believing if I hadn’t taken that sabbatical, I would have never seen that in September, and I would have never made that bet. It’s because I had been freed up and I didn’t need to be hitting singles because I came back, and it was clear, and I was fresh, and so it was like the beginning of the season. So I wasn’t hitting bad yet. I had flushed that all out. But it is really, really important if you’re a money manager to know when you’re seeing the ball. It’s a huge function of success or failure. Huge.

This is perhaps the most important section of the interview. So much of being a great trader is learning to arbitrage time and I mean that in a number of different ways.

First, it means to analyze things on a longer timescale, to be able to pull back and look at the bigger picture, the broader trends, and not get hung up on a missed earnings or the latest news cycle. And secondly, it’s to have enough experience to be able to trust your process to the point that you know returns will eventually come to you if you just stick to your game. This form of time arbitrage means that you’re focusing on having a good return record over a 3, 5, and 10 year time period and you won’t go full-tilt if you’re down for a quarter.

Capital preservation always comes first and a strict adherence to a solid process produces good outcomes over the long pull.

That’s it for my notes. I tried to include all the sections that I thought were worth sharing though I’m sure I missed some stuff. Watch the interview yourself if you can. Here’s a snapshot of Quantum Funds returns; Druck took over in 88’.

 

 

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Probabilities Not Predictions

Markets are context dependent, their behavior is a function of the particular circumstances that exist and how those circumstances are expected to or do change. The trick is not to predict an unknowable future, but to try to understand the present and the probabilities of the various paths that may evolve from it. ~ Bill Miller

We often write that we’re not in the business of making predictions. Rather, our job is to gauge the asymmetry of outcomes.

We do this by determining what the consensus beliefs and positioning are by triangulating the macro, sentiment, and technicals. This helps us paint a picture of what expectations are already embedded in the price. Then we just weight these against possible future paths.

The larger the disparity between consensus and potential outcomes, the greater the asymmetry and the more attractive the bet (trade).

There are additional benefits to using this mental model versus the typical one of making predictions.

  1. It helps protect you from yourself. Certainty is a killer in this game. When we play the prediction game, we put ourselves at risk of becoming champions to a cause and slipping into the pull of our ego driven tribal nature. This distorts our perception of the world and blinds us to new information.
  2. Prediction making is linear and bimodal in nature. Markets are non-linear and endlessly dynamic. This fact causes prediction makers to live in friction and disharmony with markets — think the perma bears who’ve been on the wrong side of the market for years. They become stuck when their view of the world does not match up with how things actually are.
  3. Focusing on asymmetry of outcomes versus predictions frees the speculator from the psychologically destructive game of trying to be right over wrong. Instead, the speculator lives in a world of various shades of grey (50 shades maybe?) where they’re always some mix of both right and wrong. In this way, the objective becomes not to form an opinion and stick with it. But rather, to apply Bayesian analysis and continuously update their views as new information comes in — this puts the focus on making money versus being right.

Bennett Goodspeed put it like this, “Why do investment professionals get such poor marks? The main reason is that they are victims of their own methodology. By making a science out of an art, they are opting to be precisely wrong rather than generally correct.”

This year, we’ve been more generally correct than we’ve been generally wrong. We’ve handedly beaten the market, as a result.

But we can’t rest on our laurels. We must constantly check for holes in our own assumptions. Like a good writer, we must be willing to kill our darlings and be ready to flip our positioning should the triangulation of the macro, sentiment, and technicals tell us to do so.

The Art of Totis Porcis
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The Difference Between Great Traders and Good Traders: The Art of Totis Porcis

The following is an excerpt from Barton Bigg’s book, Hedgehogging, where he relates a conversation with “Tim”, a successful macro investor (emphasis mine).

Tim works out of a quiet, spacious office filled with antique furniture, exquisite oriental rugs, and porcelain in a leafy suburb of London with only a secretary. My guess is he runs more than $1 billion, probably half of which is his. On his beautiful Chippendale desk sits a small plaque, which says totis porcis—the whole hog. There is also a small porcelain pig, which reads, “It takes Courage to be a Pig.” I think Stan Druckenmiller, who coined the phrase, gave him the pig.

To get really big long-term returns, you have to be a pig and ride your winners… When he lacks conviction, he reduces his leverage and takes off his bets. He describes this as “staying close to shore… When I asked him how he got his investment ideas, at first he was at a loss. Then, after thinking about it, he said that the trick was to accumulate over time a knowledge base. Then, out of the blue, some event or new piece of information triggers a thought process, and suddenly you have discovered an investment opportunity. You can’t force it. You have to be patient and wait for the light to go on. If it doesn’t go on, “Stay close to shore.”

What separates the great traders from those who are just good?

The answer is knowing when to size up and eat the whole hog.

Let me explain.

To become a good trader you have to master risk management. Managing risk is the foundation of successful speculation. It’s the core of ensuring your long-term survival.

After risk, there’s trade and portfolio management. These are not wholly separate from managing risk. But they have the added complexity of things like thinking about when to take profits on a trade or how the drivers of your book correlate across positions etc…

Risk and trade management are absolute critical skills to becoming a good trader. All good traders are masters in these two areas.

But the thing that makes great traders head and shoulders above the rest, is the skill in knowing when to go for the jugular. In sizing up and aggressively going for Totis Porcis, the full hog.

Great traders know how to exploit fat tail events — the large mispricings that only come around once in a blue moon. They swing for the fences when fat pitches come across their plate.

Examples of this are Livermore making a fortune shorting the 29’ crash. PTJ doing the same in the 87’ rout and the Nikkei fallout in 1990. Druck and Soros when they took down the Bank of England in 92’. Buffett, who’s a master of exploiting fat tails, did it when he put nearly half his capital into AXP when it was selling for dirt cheap prices.

This is something we at MO call FET which is just short for Fat-tail Exploitation Theory.

Markets and investor returns follow a power law. Similar to Pareto’s law, returns adhere to an extreme distribution of 90/10. This means, that amongst great traders and investors, 90% of their profits on average come from only 10% or less of their trades.

Let’s look at the following from Ken Grant (who’s worked with traders such as Cohen, PTJ et al.) in his book Trading Risk (emphasis mine):

Some years ago in my observation of P/L patterns, I noticed the following interesting trend: For virtually every account I encountered, the overwhelming majority of profitability was concentrated in a handful of trades. Once this pattern became clear to me, I decided to test the hypothesis across a large sample of portfolio managers for whom transactions-level data was available. Specifically, I took each transaction in every account and ranked them in descending order by profitability. I then went to the top of the list of trades and started adding the profits for each transaction until the total was equal to the overall profitability of the account.

What I found reinforced this hypothesis in surprisingly unambiguous terms. For nearly every account in our sample, the top 10% of all transactions ranked by profitability accounted for 100% or more of the P/L for the account. In many cases, the 100% threshold was crossed at 5% or lower. Moreover, this pattern repeated itself consistently across trading styles, asset classes, instrument classes, and market conditions. This is an important concept that has far reaching implications for portfolio management, many of which I will attempt to address here.

To begin with, if we accept the notion that the entire profitability of your account will be captured in, say, the top 10% of your trades, then it follows by definition that the other 90% are a break-even proposition. Think about this for a moment: Literally 9 out of every 10 of your trades are likely to aggregate to produce profits of exactly zero.

This power law for investment returns is ironclad. Like Grant notes, it’s consistent “across trading styles, asset classes, instrument classes, and market conditions.”

And here’s where we get to the crux of the matter. Good traders don’t know how to harness this power law. While great traders do. They exploit it, using it to their full advantage.

Here’s Druckenmiller on the subject (emphasis mine):

The first thing I heard when I got in the business, from my mentor, was bulls make money, bears make money, and pigs get slaughtered.

I’m here to tell you I was a pig.

And I strongly believe the only way to make long-term returns in our business that are superior is by being a pig. I think diversification and all the stuff they’re teaching at business school today is probably the most misguided concept everywhere. And if you look at all the great investors that are as different as Warren Buffett, Carl Icahn, Ken Langone, they tend to be very, very concentrated bets. They see something, they bet it, and they bet the ranch on it. And that’s kind of the way my philosophy evolved, which was if you see – only maybe one or two times a year do you see something that really, really excites you… The mistake I’d say 98% of money managers and individuals make is they feel like they got to be playing in a bunch of stuff. And if you really see it, put all your eggs in one basket and then watch the basket very carefully.

But how can one be a pig while still being a good manager of risk. It kind of seems like a paradoxical statement doesn’t it?

Here’s how.

Your average trader picks trades that have symmetrical potential outcomes. This means that the market pricing is on average, correct. It’s efficient. And the distribution of returns for these trades will fall randomly within the cone of future possibilities.

On average, these trades don’t produce alpha.

Using trade and risk management, a good trader can take this symmetric futures cone and produce positive returns by reducing the downside of return outcomes through trade structure and stop losses. But their upside is limited to the average distribution of outcomes.

But great traders and investors are different. They are skilled at identifying highly asymmetric outcomes.

These trades have the potential to massively reprice in their favor. The distribution of future outcomes for these trades looks more like this.

And not only are they skilled at identifying these skewed setups but when all the stars align they go for the whole hog and exploit the market’s error. They know that these rare asymmetric opportunities don’t come around often.

Great traders have this ability not because they are any better at predicting the future. Prediction is a fool’s errand.

It’s because they have built up a store of knowledge and context and pattern recognition skills. This allows them to more effectively assess the range of possibilities for an outcome set and identify one’s that are highly skewed to the upside.

They have the experience base that allows them to aggressively size up while at the sametime properly manage their risk. Simply put, they’ve earned the right to have conviction. And the vast majority of good traders haven’t earned this right. So they’re better off sticking with consistent and manageable bet sizing.

Tim from Hedgehogging stated it perfectly in saying that “the trick was to accumulate over time a knowledge base. Then, out of the blue, some event or new piece of information triggers a thought process, and suddenly you have discovered an investment opportunity.”

The evolutionary process of a trader should be to focus on mastering risk management. Then trade management — riding winners to their full potential. All the while building up a library of experience and useful context that will give them tools to identify asymmetric opportunities down the road. And once they’ve earned the right to have conviction, they can go for Totis Porcis.

Until then, “stay close to shore”.

Some final words from Druckenmiller.

The way to build superior long-term returns is through preservation of capital and home runs…When you have tremendous conviction on a trade, you have to go for the jugular. It takes courage to be a pig.

 

 

Another Lesson In Position Sizing From The Volpocalypse of 2018
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Another Lesson In Position Sizing From The Volpocalypse of 2018

Most famous fund failures have leverage at their core. That’s the true culprit for disaster —  not the actual trade ideas. Bad position sizing kills.

Long Term Capital Management’s strategy involved scanning the world for bond spreads that diverged from historical values — something known as convergence trading. When spreads diverged from their means, LTCM would buy the cheap and sell the expensive bond. Then wait for prices to revert back to their “theoretical efficient” market price and make a small profit.

But LTCM wasn’t satisfied with the tiny profits on the spread. They were “Masters of the Universe” and wanted to put up bigly numbers that smoked the S&P. So they took this simple strategy and leveraged up to high heaven.

Before LTCM was incinerated they had a portfolio market value of $129 billion. Of which, $125 billion was borrowed money. That’s a leverage ratio of 32:1.

Once old lady volatility hit the market, those bond spreads that LTCM had leveraged to infinity betting that they would quickly converge just like all previous times… kept diverging… and diverging.  Until eventually LTCM was forced into liquidation.

Leverage and crowding caused the forced unwind of the trade. Not the strategy of buying cheap bonds and selling expensive bonds. LTCM had a good strategy that they ruined with excessive leverage.

The exact same leverage issue happened to Victor Niederhoffer in 1997.

After suffering from a huge loss on Thai stocks during the Thai Baht crisis, Niederhoffer turned to aggressive S&P 500 put writing in order to “make back” his losses.

Over the summer of 1997 he shorted out-of-the-money November 830 puts for prices between $4 and $6.

By October these puts were trading for just $0.60 and Niederhoffer had a large gain. But the Asian Contagion spread and eventually hit the S&P.

On Thursday October 23rd, 1997 the puts rose to $1.20. On Friday the S&P dropped further but closed well above Niederhoffer’s option strike. Niederhoffer still wasn’t worried — his puts were trading for $2.40.

Over the weekend Asian markets continued to sell off. Hong Kong dropped 5% during its session which triggered a risk off move in the US markets driving stocks down 7%. This rout continued into the next morning sending the S&P spiraling into the 800s.

Volatility skyrocketed. And Niederhoffer’s puts shot up to $16. That’s 300% higher than the price he sold them for.

Refco, Niederhoffer’s broker at the time, was not happy. They called in his puts mid-morning on Tuesday October 28th for a loss of $90 million. Niederhoffer’s $70 million fund turned into a capital blackhole of -$20 million.

The market bottomed right after Niederhoffer was margin called. By November, the market was back near highs. His 830 puts went on to expire worthless — meaning his trade, had he been able to hold on, turned out to be profitable.

But his leverage forced his liquidation. He was oversized and couldn’t ride the trade out.

Niederhoffer had shorted so many puts that a run of the mill two-day market selloff sent him out on a stretcher.  

If he had sized the trade correctly, he would have survived the ride and took home a small profit. But the guy was playing on tilt, got greedy, maybe a bit arrogant, and lost all of his client’s money. (Here’s an interesting clip of him post blow up)

With the trading history books filled with examples like these where hubris and stupid sizing led to catastrophe you’d think trader’s today would maybe, learn from the past. But of course that’s not the case. Human nature is after all, human nature. And “easy” money is quite effective at clouding our better judgement.

The Volpocalype of 2018 showed us that both amateur and professional traders are still making the same mistakes with position size and leverage.

LJM partners, a mutual fund that sells options just like Niederhoffer did and whose tagline was “superior returns for the patient investor” followed the LTCM playbook.

A not-out-of-the-ordinary 10% fall in the S&P forced the fund to close positions at extremely unfavorable prices.

And then there was this amatuer XIV trader from Reddit who lost nearly $4 million dollars in XIV.

XIV goes up over time. But it also has incredibly nasty drawdowns that can exceed 90%. XIV trades more like an option than a stock. It has the ability to go up 100s of percent but also the ability to go down 90-100%.

Knowing this it’s crazy to think that anyone would allocate 100% of their portfolio into this exotic product.

So why did this Redditor do it?

Why did LTCM, and Niederhoffer and LJM carry such large positions?

Why do we constantly have a steady stream of stories about traders leveraging to the hilt and blowing out their accounts?

At the end of the day it all comes down to greed and hubris. It’s because traders want to turn a sound strategy that can produce 10% per annum returns into something that generates 30% per annum. And of course the only way to do this is to leverage the capital.

But as we’ve seen time and time again, the more you leverage the higher your chances are of ending the game bankrupt. And at a certain level of leverage your chances of going bankrupt actually converge to 100%.

That’s why it’s crucial to get position sizing right alongside a solid trading edge.

The late John Bender explains this perfectly in his interview inside Stock Market Wizards.

It might seem that if u have an edge, the way to maximize the edge is to trade as big as you can. But that’s not the case, because of risk. As a professional gambler or as a trader, you are constantly walking the line between maximizing edge & minimizing your risk of tapping out. ~ Market Wizard John Bender

I can illustrate this concept with a simple example.

Here’s a decent “good enough” trading strategy that starts with $10,000 in account equity.

From mid-2004 to mid-2010 it did pretty well. $10,000 turned into around $33,000.

Now here’s the exact same trading strategy, with the exact same starting equity, but with 10x the position size.

The allure of leverage is obvious. The 10x model at its height had a 1000x gain. $10k turned into $10 million. The prospect of outsized returns is what lures traders to lever.

But it’s a farce. Using this amount of leverage guarantees a blow up will occur at some point. And unfortunately for this trader he didn’t stop and ended the game with a $3 million debt to the broker.

To position size correctly you need robust risk management assumptions. That means assuming any product can trade at any price at any time.

If your trading VIX for example, assume it can go from 10 to 100 overnight. That might sound asinine but in reality it keeps you safe. Because at the end of the day anything can happen.

By putting extreme scenarios in your universe you can devise a way to survive should it occur. This way of thinking will keep you from botching position size.

Traders that blow up and over lever don’t think like this. Instead they use models that rely on past data to estimate “probable” risk.

Throw those methods out. Historical data means nothing when it comes to risk management. The future will always bring something more intense than the past.

Another thing to be cognizant of is that strategies with negative skewness (frequent small wins and large rare losses) are especially tricky to position size correctly.

LTCM, Niederhoffer, LJM, and that XIV Redditor were all implementing strategies with negative skewness.

If you’re strategy has these characteristics it’s even more important to run extreme stress testing on your process and assume that your trading vehicles can trade at any price at any time. Size your positions from that extreme stress test. Never rely on past historical moves to define your risk in a negative skew trading strategy. The traders that rely on past data to size up risk always blow up.

Please think deeply about your position sizing process. If you don’t get it right you’ll end up in Taleb’s Turkey Graveyard with the rest of the traders who flew too close to the sun.

 

 

Bruce Kovner On Listening To The Market, Politics, & Risk Control
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Bruce Kovner On Listening To The Market, Politics, & Risk Control

The following is a fantastic speech from Bruce Kovner on Caxton Associates’ 20th anniversary. Kovner shares a plethora of trading wisdoms including the three most important contributors to his hedge fund’s success. You can read the original speech from 2003 here.

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To all my colleagues, to friends and associates who have worked and invested with us over the years, may I say welcome to this 20th anniversary party. This is the first time Caxton has thrown a party for our extended family and I am happy and thrilled to welcome all of you. If we are able to catch Peter D’Angelo in a generous moment, we may not have to wait another twenty years for the next event!

This particular room has always been one of my favorites – combining a modern space with the elegant and mysterious beauty of this ancient Egyptian temple. But this setting also suggests another theme: that nothing is permanent in human affairs, that the apparent solidity of these stones of Egypt counted for nothing as dynasties came and went and especially when the Pharaoh of the 1950’s and 60’s, Gamal Abdul Nasser, now himself long gone, determined that the great Aswan Dam would flood the plains of the Nile, submerging much of Egypt’s history in the vast lake then to be formed.

The World changes. In some small sense, Caxton’s story has its roots in similar observations. Caxton was born twenty years ago amid extraordinary changes in the world of money, finance, and politics, changes that have helped transform the world economy. Tonight, I would like to talk briefly about the circumstances both in the markets and in my own life and experience that have shaped Caxton’s performance over the last two decades. I would like to start with events that predated the founding of Caxton but which are important in understanding the origins of the company. Then I would like to describe important periods in Caxton’s evolution and close with some thoughts about the future.

Caxton was started in a period of economic transition – a time of creative destruction, as fans of Professor Schumpeter would say – when the old order of fixed exchange rates and fixed gold prices ($35 per ounce, I believe) could no longer contain the enormous pressures of the 1960’s and 1970’s. We wanted guns and butter, Vietnam and the Great Society. Around the world there were vast and differentiated changes in inflation, productivity, and wealth. So, the Gold window was shut in 1968. Three years later, Richard Nixon abandoned fixed exchange rates and let the dollar float. A new economic era had begun.

The inflation, volatile exchange rates, rising commodity prices and high nominal interest rates that followed in the 1970’s created an environment in which the old ways of investing no longer functioned well. Long-only stock and bond trading were not the optimum ways to capture the opportunities that the 1970’s created. On the contrary, between 1968 and the early 1980’s, stocks and bonds suffered through a long bear market, destroying the value of equity and bond portfolios and undermining confidence in traditional investing styles. On the other hand, opportunities to profit from being long or short in currencies, fixed income, stocks and commodities abounded. The stage was set for active ‘macro trading’ as the increasingly popular term would label it.

New York, with its long-only equity culture and preponderance of establishment institutions, was not a congenial host for the new trading culture. That had to emerge in Chicago where people like Leo Melamed, a former egg broker, became head of the Chicago Mercantile Exchange and initiated trading of financial futures. The Chicago Board of Trade followed suit with the establishment of a market for trading Ginnie Mae futures. The process of creative destruction operated on the structure of financial markets far more effectively on the frontier in Chicago than it did in New York.

Cambridge, Massachusetts in the 1960’s was also not a bad place to learn that innovation and change are at the heart of survival. I had learned some of that from the history of my family – Jewish refugees from Czarist Russia. But I learned more after I enrolled as a freshman at Harvard College in 1962, reading Schumpeter on “creative destruction”, Keynes and Samuelson on counter cyclical fiscal policy, Tocqueville on the Ancien Regime, Fainsod on the Russian Revolution, Keynes (again) on the Economic Consequences of the Peace (meaning the punitive Treaty of Versailles), and a range of historians on the two World Wars. All were lessons on the impermanence of institutions and on the unintended consequences of government policy.

I did not know that these lessons were going to be put to any practical use by me. I had thought I would enter government service, not the financial world, when I left graduate school in 1970 and began to wander around the world for a few years. When I finally moved back to the United States in 1974, I was not seeking a Wall Street career (and I had no qualifications to begin one). I taught politics during the day and began to study financial markets at night. And by 1977, when I made my first tentative steps into financial markets, the financial world had already begun its remarkable transformation.

In my one-bedroom apartment on 57th Street, down the block from Carnegie Hall, I was only four miles away from Wall Street but a universe away in terms of my approach to markets. The new world of financial futures reduced the barriers to entry to currency and interest rate markets. Perhaps, I thought, efforts to understand what moved these markets might be well rewarded. I speculated on commodity prices, interest rates, currencies and was gratified to make money. And I found my way to Commodities Corporation, started by economists from MIT and Princeton, to learn more of my newly chosen trade. After five and a half years, with the blessings of my former employers, I decided to establish a company that reflected my own particular vision of how to adapt to this new financial world. With $7MM from investors and $5MM of my own funds, I started Caxton in March of 1983.

The new company’s operations were guided by principles and observations that helped us to pursue successful trading.

Barriers to entry to financial markets of all kinds were coming down. New markets were developing for the new financial instruments. Early providers of liquidity and expertise were likely to find excess returns.

Analysis of macro conditions was not being done systematically or well in most large money market institutions! Good analysis would provide excess returns.

Exogenous shocks – say, for example, oil price shocks – to the economies of the world were likely to be numerous. Being a quick responder to these shocks would provide excess returns.

Most investment managers and operators in equity and debt markets had institutional and cultural restraints on the kind of trading they could do – and tended heavily to favor long-only approaches. Few traders were highly skilled in the use of derivatives. Excess returns were therefore more likely to be earned by those who could go short as well as long, and who could use derivatives well.

More generally, Caxton adopted an institutional model that built in more flexibility in the creation of optimal portfolio mix than was normal on Wall Street. We had three structural advantages: First, there were no institutional limits on the range and style of our trading. We would go to any asset class where we saw opportunity, and we would trade in a range of trading styles (long, short, differential based, trend-following, mean-reversion, or arbitrage to name a few). Second, we chose to target risk levels, not nominal dollar levels, to calibrate our trading size. This enabled us to use leverage and portfolio theory to optimize our risk profile. There is plenty of opportunity to do risk management badly, of course; but the advantages of doing it well were enormous. And thirdly, we believed in a process of dynamic risk allocation – that is, we would put more capital at risk when either market conditions or macro economic conditions convinced us of the possibility of excess return. Caxton wouldn’t be stuck with a fixed asset allocation to stocks or bonds or currencies or commodities. We would change our capital allocation with conditions. Do that well and returns would not be a passive captive of the business cycle. On the contrary, dynamic capital allocation could turn what was for more inflexible institutions a source of difficulty into an advantage for a young firm capable of adapting to changing conditions.

The first ten years of Caxton’s existence certainly provided ample opportunity for us to test both premises of our new model and our skills in deploying it. We had macroeconomic shocks aplenty – rising and falling rates, oil shocks, the Plaza accord, the market crash of 1987, the Iran-Iraq War, not to mention the first US-Iraq War. We had new markets, new financial instruments, new Presidents, new Prime Ministers, currency unions … No shortage of fun, for we dyed-in-the wool macro traders. And, fortunately, although we made many, many mistakes, we managed to execute well enough to have good returns. During the first ten years of its existence, starting with about $10MM in capital, Caxton earned some $3 billion in profits, with a gross trading return of 55.6 percent per year. In the same period (although it included nearly all of the first ten years of a great bull market) the S&P 500 grew at the rate of about 15.7 percent per year. And even though Caxton’s trading during those years felt, to me, a little too much like Coney Island’s Cyclone roller coaster, the quality of our return, with a Sharpe ratio of 1.68, was about three teams higher than the S&P’s .54 during the same period.

Our success notwithstanding, by the mid-nineties, several of the opportunities which had facilitated Caxton’s success had changed. A large number of new players – hedge funds, prop desks at banks, speculators – had entered the market, reducing the advantages of early entrants. Macro analysis had become routine in wire houses, investment banks, and prop desks. And Caxton, with $1.6 billion in assets, was no longer small enough to make its returns with quick trades in smaller markets. We felt stale. Our performance – down 2.4% in 1994 – felt awful. It was time for a change. We sent our investors 60% of their funds, reduced our capital to $650MM, and went back to the drawing boards.

Prior to 1994, Caxton was largely focused on top-down macro trading. By 1994, we had concluded that we needed more tools than those that macro trading provided. Nothing works all the time. We wanted a variety of trading strategies across all liquid asset classes. And we didn’t want to be confined to one trading style, such as momentum-based trend following. So we began a systematic process of searching for new strategies, new styles, new markets and new traders. Let 1000 flowers bloom, it was said. But make sure they bloom with good risk management, and with low correlation! In the years since 1995, Caxton has spent a lot of time searching for these techniques, strategies and traders. We kissed a lot of frogs, as my good friend Joe Grundfest might say. (Actually, does say.) And we wound up with some 50 trading centers covering virtually all liquid asset classes, employing a multiplicity of approaches – trend following, of course, but also mean reversion strategies, fundamentals based models, arbitrage, computer-based approaches to markets, discretionary trading of equities, active trading of mortgage markets and related instruments, and many others.

We wouldn’t be here tonight, having this rather wonderful celebration in the Metropolitan Museum, if the results of that effort had not been acceptable. In fact, since January 1995, Caxton has earned $8.5 billion in trading profits, starting with a base of $650MM. Our average annual return has been 33.1 percent, and our Sharpe ratio rose from the first decades 1.68 to just below 2.00. In the same period, the S&P’s annual return was 12.7% for the S&P, with a Sharpe ratio of .50.

Today, Caxton has nearly 50 trading centers, divided among:

Macro oriented centers, which deploy about 35% of the risk of the company;

Equity oriented centers, which deploy about 25% of our risk;

Quantitative systems, deploying another 25% of our risk; and

Fixed income strategies which deploy another 15% of risk.

My own role in all of this has changed substantially over these years. Whereas in the first years of Caxton, I had tactical responsibility for almost everything in the portfolio, my trading accounts for something like 10% to 15% of the company’s risk presently. I spend a great deal of time on strategic development. It is more important for me to help find and develop areas of opportunity than it is for me to trade them. And it is more important to have a robust process of strategic development than it is to have one dependent on one human being. That is why Caxton devotes many millions of dollars a year to research and development aimed at finding new quantitative techniques or new areas of trading likely to yield high returns, and developing information technology and risk control techniques. I spend most of my time on these efforts, and on working with traders when they need advice, encouragement or help.

In these efforts, I try to pass on something of the proverbs, ethos and culture of trading that I have regarded as essential to Caxton’s success. Of these, I will mention three:

  1. Listen to the market. Close observation of price behavior is always necessary for the discipline of successful trading and it is very often very helpful in providing evidence about the nature of current conditions. If we can understand what the market is telling us, we will most likely be able to understand how to trade it. Listen to the market, hear it, don’t tell it what to do. Listen.
  2. Take politics and policy seriously. Changes in policy matter. Changes in leadership matter. Study them seriously. This doesn’t mean that politicians and policy makers will get it right – indeed very often they will get it wrong and these ‘mistakes’ in and of themselves may be important to markets. But ignore them at your peril. Policy matters. Politics matter.
  3. Above everything else, never let the discipline of risk control become lax. Those 100-year storms have a way of coming round every few years. If in real estate it’s “location, location, location”, in leveraged trading it is “risk control, risk control, risk control”. It is surprising how often this focus is lost.

Listening to the market. Taking politics and policy seriously. Risk control – to these we need to add one more on the level of the firm. And that is our old friend “creative destruction”: the process of creative destruction operates on trading techniques (and their embodiment in individual traders) as much as it does on any market structure. Any trading technique has a finite life in which it yields extraordinary returns. As more capital and knowledge are applied, markets become more efficient and rates of return drop – until eventually high risk adjusted returns disappear. Then it is time to retire the technique and move on.

The flexibility and learning that must be applied to trading also should be applied to capital allocation. We can’t be static in how we allocate capital to a particular strategy, nor to all of them together. So Caxton only wants the amount of capital it can deploy successfully. We are not asset gatherers. Despite a long list of investors who want to get in to Caxton funds, we will most certainly send money back to investors again in the near future. Barring unforeseen developments, we will return between 10 and 20% of our capital to investors at the end of this year. Flexible capital allocation. When rate of return drops, send capital back home to our investors.

In the meantime, we will continue to develop new techniques and new trading centers. As they bear fruit, we will nurture them and introduce them into our portfolio as they are proven and as conditions permit.

There is little doubt in my mind that the financial markets will remain as dynamic in the next twenty years as they were in the last. Look at the enormous changes in Asia, where China is emerging as a giant already. One cannot understand world commodity markets today without understanding China – and therein, of course, lies much opportunity as well as risk. Look at the extraordinary long cycle in Japan. Twelve years of recession and decline, of policy mistakes, of opportunities lost. And perhaps now, finally, of some policy initiatives which may finally lift Japan deflation and seemingly unending recession.

And one doesn’t have to look only across the oceans for big structural changes that make markets different in this decade than in the last. Look at the fixed income market, where mortgages and floating rates have changed the character of supply and demand and market behavior in recent years. The mortgage market now dwarfs the U.S. Treasury market. Outstanding mortgage debt now exceeds total marketable Treasury debt to 50 percent. A decade ago that rate was reversed. No one can understand the path of yields in the United States without understanding these new conditions.

And look at the universe of possible exogenous shocks we confront today. Perhaps we all have some sense of the potential disturbance of terror attacks. But have we correctly priced them in the market? How do you price low probability high damage events? And how do you know the right probabilities?

And what about technological change, which still promises to create enormous values (I am thinking now of biotech as well as information technology)? And what about new oil shocks? Or geo-political conflict, such as India-Pakistan?

Clearly, this list could go on and on.

There is no certainty that Caxton will be able to adapt successfully to these and other risks and changes, but we do structure ourselves to be able to analyze such changes and risks and try to respond to them. And certainly those institutions which do not have the capability to respond leave themselves at the mercy of events. The premise of our operational philosophy was always and still is: The World Changes. Nothing is Permanent. Those who fail to adapt to change risk everything. Look around you, here in this beautiful room. Look at the magnificence of the Temple of Dendur and of the civilization of Egypt. The World Changes and nothing is permanent. If that is one of the lessons that family history and education taught me, it is also the lesson that imbues the practice of Caxton. Study the world. Study markets. Listen to the markets. Then, perhaps, with a little luck and skill, you may be able to find ways not simply to be a victim of circumstances but to profit from them.

Thank you.

 

 

Trading Wisdom From A JP Morgan Vet
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Trading Wisdom From A JP Morgan Vet

Every once in awhile a few gems will surface through Wall Street’s noise. Such is the case with a departing note from Jan Loeys, a senior strategist from J.P. Morgan who has been at this game for over 30 years.

In his final note, Loeys shares his thoughts on all the hot issues in finance and trading. The following are his most relevant comments to what we do at Macro Ops. The bolded emphasis is ours.   

Quant  Vs Discretionary

  • Quantitative techniques are indispensable, though, to deal with the complexity of financial instruments and the overload of information we face. Empirical evidence counts for more than theory, but you need theory to constrain empirical searchers and avoid spurious correlations.
  • Rules versus discretion? You need both. I have tried to have logical arguments to buy or sell certain assets, based on Finance. And I have tried to corral evidence that the signals I use have in the past had the assumed impact on asset prices. Each of these then became a rule, of the form: If X>0, buy A, and vice versa. As we collected these rules, and published them in our Investment Strategies series, the question came up naturally whether we should not simply make our investment process driven by a number of empirically proven rules, and to banish any discretion (emotion?) from the process. Over time, we converged on a mixture of the two as pure rules ran into the problem that the world is forever changing, partly as everyone else figures out the same rule and then arbitrages away the profit, and partly as economic structures and regimes similarly change over time in a way that we cannot capture with simple rules.
  • Much as I have been talking a lot about cycles, I do not think of the world as a stationary system described by a set of parameters that we steadily get to know more about. Instead, as economists we think of people constantly optimizing their objectives, under the constraints they face. Aside from truly exogenous shocks to the system, the main difference between today and yesterday is that today, we know what happened yesterday and that information allows us to constantly fine tune and thus change our behavior. That is, we constantly learn from the past, much to try to avoid making the same mistakes. At the macro level, this means that the system is constantly evolving. As Mark Twain said, “History doesn’t repeat itself, but it often rhymes”. As investors, we should look at the market as billions of people all learning and adapting. The best investors are those who get ahead of this by learning faster and understanding better how others are learning.

Forecasting

  • The starting point of Finance is the Theorem of Market Efficiency which posits that under ideal conditions what we all know should be in the price. Only new information moves the price. Hence, it is changes in expectations about the future that drive asset prices, not the level of anything.
  • How to forecast view changes? The good news is that changes in opinions about fundamentals such as growth and inflation tend to repeat. This is one driver of momentum in asset prices, and is likely driven by the positive feedback between risk markets and the economy that forecasters naturally find very difficult getting ahead of.
  • There is a fundamental difference between an asset price and a forecast. A forecast is a single outcome that you consider the most likely, among many. In statistics, we call this the mode. An asset price, in contrast, is closer to the probability-weighted mean of the different scenarios you consider possible in the future. When our own probability distribution for these different outcomes is not evenly balanced but instead skewed to, say, the upside, the market price will be above our modal view. Asset prices can thus move without a change in modal views if the market perceives a change in the risk distribution. An investor should thus monitor changing risk perceptions as much as changing modal views.
  • Do markets get ahead of reality? They do, yes, exactly because asset prices are probability-weighted means and the reality we perceive is coded as a modal view. Information arrives constantly and almost always only gently moves the risk distribution around a given modal view. Before we change our modal view of reality, the market will have seen the change in risk distribution and will have started moving already.
  • Levels or direction? In our business, we are asked to forecast asset prices and returns. I have found this very hard but fortunately have had the luxury to be able to stick to forecasting market direction rather than outright asset price levels. In markets that are close to efficiently priced, what we know is already in the price and we cannot really use that same information to make a coherent case for an asset price level that much different from today. All I have been able to do is to make a case that there are mild-to-decent odds in favor of the market going in one direction rather than the other. We have been much more successful in forecasting direction than actual asset price levels, and it is the direction that is more important for strategy.

Finding Alpha

  • The Theorem of Market efficiency, which implies investors can’t beat the market, implies that asset prices will follow random walks, with drift and that asset price changes will be white noise, with no serial correlation. There are thus only two possible inefficiencies to be exploited: positive serial correlation, which we call Momentum, or negative serial correlation, which we call mean reversion, or Value (to become valuable, asset prices need first to go down, or fundamentals need to improve faster than the price). It is an empirical question which dominates where. At the asset class and sector level, we have found that Momentum dominates, while within the fixed income world, Value is more important.
  • Across time, market momentum at the macro level has been the best way to earn excess returns. I discussed above how some of this is due to the momentum in view changes. More fundamentally, in open markets, we frequently face a Fallacy of Composition according to which rational and equilibrating behavior at the micro level becomes destabilizing at the macro level. The free market is very good at motivating entrepreneurship and rational behavior at the micro level, but is subject to constant booms and busts at the macro level. Central banks try to control this instability through counter-cyclical policies but can’t undo it all.
  • Trade the risk bias. Even when markets price in exactly our modal views, I find it useful to consider how prices will move on new information and then try to position on any skew in the outlook. If I find that a particular price or spread will move a lot more on bullish than on bearish news, then I will position bullishly. This works at the portfolio level if I can combine different unrelated risk biases.

Red Team      

  • Cherish your errors. I have learned ten times more from being wrong than being right. Once you make a mistake, go public with it, analyze it in detail, and learn from it.
  • Be your own devil’s advocate, and spend most time with people who do not agree with you, or who have a different way of looking at things. Not always easy as being with like-minded people is more comforting.

 

 

Advice From Mark Sellers
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Hedge Fund Manager Mark Sellers On Becoming A Great Investor

This is a killer talk I came across from Hedge Fund manager Mark Sellers, speaking to some Harvard MBA kids on what it takes to make it in markets. Regardless of whether you consider yourself a trader or investor, Mark’s “seven traits” apply.

Enjoy…

First of all, I want to thank Daniel Goldberg for asking me to be here today and all of you for actually showing up. I haven’t been to Boston in a while but I did live here for a short time in 1991 & 1992 when I attended Berklee School of Music.

I was studying to be a jazz piano player but dropped out after a couple semesters to move to Los Angeles and join a band. I was so broke when I lived here that I didn’t take advantage of all the things there are to do in Boston, and I didn’t have a car to explore New England. I mostly spent 10-12 hours a day holed up in a practice room playing the piano. So whenever I come back to visit Boston, it’s like a new city to me.

One thing I will tell you right off the bat: I’m not here to teach you how to be a great investor. On the contrary, I’m here to tell you why very few of you can ever hope to achieve this status.

If you spend enough time studying investors like Charlie Munger, Warren Buffett, Bruce Berkowitz, Bill Miller, Eddie Lampert, Bill Ackman, and people who have been similarly successful in the investment world, you will understand what I mean.

I know that everyone in this room is exceedingly intelligent and you’ve all worked hard to get where you are. You are the brightest of the bright. And yet, there’s one thing you should remember if you remember nothing else from my talk: You have almost no chance of being a great investor.

You have a really, really low probability, like 2% or less. And I’m adjusting for the fact that you all have high IQs and are hard workers and will have an MBA from one of the top business schools in the country soon. If this audience was just a random sample of the population at large, the likelihood of anyone here becoming a great investor later on would be even less, like 1/50th of 1% or something.

You all have a lot of advantages over Joe Investor, and yet you have almost no chance of standing out from the crowd over a long period of time.

And the reason is that it doesn’t much matter what your IQ is, or how many books or magazines or newspapers you have read, or how much experience you have, or will have later in your career. These are things that many people have and yet almost none of them end up compounding at 20% or 25% over their careers.

I know this is a controversial thing to say and I don’t want to offend anyone in the audience. I’m not pointing out anyone specifically and saying that you have almost no chance to be great. There are probably one or two people in this room who will end up compounding money at 20% for their career, but it’s hard to tell in advance who those will be without knowing each of you personally.

On the bright side, although most of you will not be able to compound money at 20% for your entire career, a lot of you will turn out to be good, above average investors because you are a skewed sample, the Harvard MBAs. A person can learn to be an above-average investor. You can learn to do well enough, if you’re smart and hardworking and educated, to keep a good, high-paying job in the investment business for your entire career.

You can make millions without being a great investor. You can learn to outperform the averages by a couple points a year through hard work and an above average IQ and a lot of study. So there is no reason to be discouraged by what I’m saying today. You can have a really successful, lucrative career even if you’re not the next Warren Buffett.

But you can’t compound money at 20% forever unless you have that hard-wired into your brain from the age of 10 or 11 or 12.

I’m not sure if it’s nature or nurture, but by the time you’re a teenager, if you don’t already have it, you can’t get it. By the time your brain is developed, you either have the ability to run circles around other investors or you don’t.

Going to Harvard won’t change that and reading every book ever written on investing won’t either. Neither will years of experience. All of these things are necessary if you want to become a great investor, but in and of themselves aren’t enough because all of them can be duplicated by competitors.

As an analogy, think about competitive strategy in the corporate world. I’m sure all of you have had, or will have, a strategy course while you’re here. Maybe you’ll study Michael Porter’s research and his books, which is what I did on my own before I entered business school. I learned a lot from reading his books and still use it all the time when analyzing companies.

Now, as a CEO of a company, what are the types of advantages that help protect you from the competition?

How do you get to the point where you have a wide economic moat, as Buffett calls it?

Well one thing that isn’t a source of a moat is technology because that can be duplicated and always will be, eventually, if that’s the only advantage you have. Your best hope in a situation like this is to be acquired or go public and sell all your shares before investors realize you donít have a sustainable advantage.

Technology is one type of advantage that’s short-lived. There are others, such as a good management team or a catchy advertising campaign or a hot fashion trend. These things produce temporary advantages but they change over time, or can be duplicated by competitors.

An economic moat is a structural thing. It’s like Southwest Airlines in the 1990s, it was so deeply ingrained in the company culture, in every employee, that no one could copy it, even though everyone kind of knew how Southwest was doing it.

If your competitors know your secret and yet still can’t copy it, that’s a structural advantage. That’s a moat.

The way I see it, there are really only four sources of economic moats that are hard to duplicate, and thus, long-lasting. One source would be economies of scale and scope. Wal-Mart is an example of this, as is Cintas in the uniform rental business or Procter & Gamble or Home Depot and Lowe’s.

Another source is the network affect, ala eBay or Mastercard or Visa or American Express.

A third would be intellectual property rights, such as patents, trademarks, regulatory approvals, or customer goodwill. Disney, Nike, or Genentech would be good examples here. A fourth and final type of moat would be high customer switching costs. Paychex and Microsoft are great examples of companies that benefit from high customer switching costs.

These are the only four types of competitive advantages that are durable, because they are very difficult for competitors to duplicate. And just like a company needs to develop a moat or suffer from mediocrity, an investor needs some sort of edge over the competition or he’ll suffer from mediocrity.

There are 8,000 hedge funds and 10,000 mutual funds and millions of individuals trying to play the stock market every day. How can you get an advantage over all these people? What are the sources of the moat?

Well, one thing that is not a source is reading a lot of books and magazines and newspapers. Anyone can read a book.

Reading is incredibly important, but it won’t give you a big advantage over others. It will just allow you to keep up. Everyone reads a lot in this business. Some read more than others, but I don’t necessarily think there’s a correlation between investment performance and number of books read.

Once you reach a certain point in your knowledge base, there are diminishing returns to reading more. And in fact, reading too much news can actually be detrimental to performance because you start to believe all the crap the journalists pump out to sell more papers.

Another thing that won’t make you a great investor is an MBA from a top school or a CFA or PhD or CPA or MS or any of the other dozens of possible degrees and designations you can obtain.

Harvard can’t teach you to be a great investor. Neither can my alma mater, Northwestern University, or Chicago, or Wharton, or Stanford. I like to say that an MBA is the best way to learn how to exactly, precisely, equal the market return. You can reduce your tracking error dramatically by getting an MBA.

This often results in a big paycheck even though it’s the antithesis of what a great investor does. You can’t buy or study your way to being a great investor. These things won’t give you a moat. They are simply things that make it easier to get invited into the poker game.

Experience is another over-rated thing.

I mean, it’s incredibly important, but it’s not a source of competitive advantage. It’s another thing that is just required for admission. At some point the value of experience reaches the point of diminishing returns. If that wasn’t true, all the great money managers would have their best years in their 60s and 70s and 80s, and we know that’s not true. So some level of experience is necessary to play the game, but at some point, it doesn’t help any more and in any event, itís not a source of an economic moat for an investor.

Charlie Munger talks about this when he says you can recognize when someone gets it right away, and sometimes it’s someone who has almost no investing experience.

So what are the sources of competitive advantage for an investor?

Just as with a company or an industry, the moats for investors are structural. They have to do with psychology, and psychology is hard wired into your brain. It’s a part of you. You can’t do much to change it even if you read a lot of books on the subject.

The way I see it, there are at least seven traits great investors share that are true sources of advantage because they canít be learned once a person reaches adulthood. In fact, some of them can’t be learned at all; you’re either born with them or you aren’t.

Trait #1

Is the ability to buy stocks while others are panicking and sell stocks while others are euphoric.

Everyone thinks they can do this, but then when October 19, 1987 comes around and the market is crashing all around you, almost no one has the stomach to buy. When the year 1999 comes around and the market is going up almost every day, you can’t bring yourself to sell because if you do, you may fall behind your peers.

The vast majority of the people who manage money have MBAs and high IQs and have read a lot of books. By late 1999, all these people knew with great certainty that stocks were overvalued, and yet they couldn’t bring themselves to take money off the table because of the ìinstitutional imperative, as Buffett calls it.

Trait #2

The second character trait of a great investor is that he is obsessive about playing the game and wanting to win.

These people don’t just enjoy investing; they live it. They wake up in the morning and the first thing they think about, while they’re still half asleep, is a stock they have been researching, or one of the stocks they are thinking about selling, or what the greatest risk to their portfolio is and how they’re going to neutralize that risk.

They often have a hard time with personal relationships because, though they may truly enjoy other people, they don’t always give them much time. Their head is always in the clouds, dreaming about stocks.

Unfortunately, you can’t learn to be obsessive about something. You either are, or you aren’t. And if you aren’t, you can’t be the next Bruce Berkowitz.

Trait #3

A third trait is the willingness to learn from past mistakes. The thing that is so hard for people and what sets some investors apart is an intense desire to learn from their own mistakes so they can avoid repeating them.

Most people would much rather just move on and ignore the dumb things they’ve done in the past. I believe the term for this is repression.

But if you ignore mistakes without fully analyzing them, you will undoubtedly make a similar mistake later in your career. And in fact, even if you do analyze them it ís tough to avoid repeating the same mistakes.

Trait #4

A fourth trait is an inherent sense of risk based on common sense.

Most people know the story of Long Term Capital Management, where a team of 60 or 70 PhDs with sophisticated risk models failed to realize what, in retrospect, seemed obvious: they were dramatically over leveraged. They never stepped back and said to themselves, “Hey, even though the computer says this is ok, does it really make sense in real life?”

The ability to do this is not as prevalent among human beings as you might think. I believe the greatest risk control is common sense, but people fall into the habit of sleeping well at night because the computer says they should. They ignore common sense, a mistake I see repeated over and over in the investment world.

Trait #5

Great investors have confidence in their own convictions and stick with them, even when facing criticism. Buffett never get into the dot-com mania though he was being criticized publicly for ignoring technology stocks.

He stuck to his guns when everyone else was abandoning the value investing ship and Barron’s was publishing a picture of him on the cover with the headline “What’s Wrong, Warren?”

Of course, it worked out brilliantly for him and made Barron’s look like a perfect contrary indicator.

Personally, I’m amazed at how little conviction most investors have in the stocks they buy. Instead of putting 20% of their portfolio into a stock, as the Kelly Formula might say to do, they’ll put 2% into it.

Mathematically, using the Kelly Formula, it can be shown that a 2% position is the equivalent of betting on a stock has only a 51% chance of going up, and a 49% chance of going down. Why would you waste your time even making that bet? These guys are getting paid $1 million a year to identify stocks with a 51% chance of going up? It’s insane.

Trait #6

Sixth, it’s important to have both sides of your brain working, not just the left side (the side that’s good at math and organization.)

In business school, I met a lot of people who were incredibly smart. But those who were majoring in finance couldn’t write worth a damn and had a hard time coming up with inventive ways to look at a problem. I was a little shocked at this.

I later learned that some really smart people have only one side of their brains working, and that is enough to do very well in the world but not enough to be an entrepreneurial investor who thinks differently from the masses.

On the other hand, if the right side of your brain is dominant, you probably loathe math and therefore you don’t often find these people in the world of finance to begin with. So finance people tend to be very left-brain oriented and I think that’s a problem. I believe a great investor needs to have both sides turned on.

As an investor, you need to perform calculations and have a logical investment thesis. This is your left brain working.

But you also need to be able to do things such as judging a management team from subtle cues they give off. You need to be able to step back and take a big picture view of certain situations rather than analyzing them to death. You need to have a sense of humor and humility and common sense. And most important, I believe you need to be a good writer.

Look at Buffett; he’s one of the best writers ever in the business world. It’s not a coincidence that he’s also one of the best investors of all time. If you can’t write clearly, it is my opinion that you don’t think very clearly. And if you don’t think clearly, you’re in trouble. There are a lot of people who have genius IQs who can’t think clearly, though they can figure out bond or option pricing in their heads.

Trait #7

And finally the most important, and rarest, trait of all: The ability to live through volatility without changing your investment thought process.

This is almost impossible for most people to do; when the chips are down they have a terrible time not selling their stocks at a loss. They have a really hard time getting themselves to average down or to put any money into stocks at all when the market is going down.

People don’t like short term pain even if it would result in better long-term results. Very few investors can handle the volatility required for high portfolio returns.

They equate short-term volatility with risk. This is irrational; risk means that if you are wrong about a bet you make, you lose money. A swing up or down over a relatively short time period is not a loss and therefore not risk, unless you are prone to panicking at the bottom and locking in the loss.

But most people just can’t see it that way; their brains won’t let them. Their panic instinct steps in and shuts down the normal brain function.

I would argue that none of these traits can be learned once a person reaches adulthood. By that time, your potential to be an outstanding investor later in life has already been determined.

It can be honed, but not developed from scratch because it mostly has to do with the way your brain is wired and experiences you have as a child. That doesn’t mean financial education and reading and investing experience aren’t important.

Those are critical just to get into the game and keep playing. But those things can be copied by anyone.

The seven traits above can’t be.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Do you possess these 7 traits?

 

 

Separating signals from noise in markets
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Separating Signal From Noise In Markets

There’s not much information to get. The most important variables in global macro are the economic conditions and how central banks respond to those conditions.

To get that information, I read the paper, look at the data, watch what the officials say, and try to read between the lines. From an actual trade point of view, it’s price action that determines when and where to put on a trade.

Otherwise, there’s a huge amount of noise in the world. Other people’s opinions are irrelevant. I can’t bear talking to salespeople because all they want to do is tell you something new, and things don’t change enough to warrant that. I don’t pick up the phone if I can avoid it.

– The Currency Specialist, from Drobny’s Inside the House of Money

My take:

A big part of successful trading is learning how to effectively separate wheat from chaff and signal from noise.

This is no easy task.

We live in a world of 24/7 information flow. It’s easy to drown in the constant firehosing of data, opinions, and hot takes.

Traders get into trouble when they mistake the noise for actionable intelligence. This happens because they don’t yet have a framework for which to filter out what’s useful and what’s not.

All a framework is is a set of mental models, broad first principles, and general truths that you stress test for robustness and which hold up through time.

The key is to start with the simple truths and build, slowly, while never sacrificing veracity for complexity.

Munger gave a killer speech about how to do this back in the 90’s. Here’s a cut from it (emphasis mine).

Well, the first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form.

You’ve got to have models in your head. And you’ve got to array your experience both vicarious and direct on this latticework of models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and in life. You’ve got to hang experience on a latticework of models in your head.

What are the models? Well, the first rule is that you’ve got to have multiple models because if you just have one or two that you’re using, the nature of human psychology is such that you’ll torture reality so that it fits your models, or at least you’ll think it does. …

And the models have to come from multiple disciplines because all the wisdom of the world is not to be found in one little academic department. That’s why poetry professors, by and large, are so unwise in a worldly sense. They don’t have enough models in their heads. So you’ve got to have models across a fair array of disciplines.

You may say, “My God, this is already getting way too tough.” But, fortunately, it isn’t that tough because 80 or 90 important models will carry about 90% of the freight in making you a worldly-wise person. And, of those, only a mere handful really carry very heavy freight.

For the Currency Specialist, the framework he carried was focused on a few global macro and economic variables, how central bankers responded to these conditions, and then constantly testing his resulting assumptions against the truth mechanism that is the market.

At MO we apply a range of mental models from Capital Cycles to Investment Clocks to Game Masters and Liquidity Levers to name a few.

Like the Currency Specialist, these models help us distinguish the signal from the noise while keeping us from chasing something shiny that’s of little value.  

And more importantly, it gives us a framework from which to value these inputs and our responses to them.

This allows us to continuously refine our tools and mental models which hopefully drives us closer and closer to truth and profits.

Market Wizard Michael Marcus said the most valuable thing he learned at Commodity Corp (CC) was how “to see the signal, like the signal, follow the signal.”

Focus on developing your latticework of mental models and you’ll get better and better at seeing the signal.

Drop any questions/comments in the comment section below. And if you’d like to get my thinking, ramblings, and occasional trade ideas, then just put in your John Hancock along with your email below.

Thanks for reading,

Alex

 

 

 

The Label Stupidity Loop
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The Label Stupidity Loop

“I’m a Democrat… I’m an Evangelical Christian… I’m a Trump Supporter… I’m a Value Investor… I’m a Bernie Bro… I’m a Neo-Keynesian… I’m a Scientologist… I’m a… I’m a…”

Humans love adopting labels.

We don them proudly, wearing them like sports jerseys. We proclaim our chosen identity to anybody that’ll listen. We want everyone to know what we stand for… and that we’re on the “right” side.

It’s impossible to know everything, and we hate that fact. People abhor uncertainty.

Labels fix this.

They give us pre-packaged sets of beliefs/heuristics/frameworks for dealing with and understanding the world.

Labels also give us a tribe.

By declaring yourself a Republican, you’re signaling to other Republicans that you’re “one of them”. You’re letting them know that you share the same beliefs and values. You’re part of the team. The same goes for Democrats, Libertarians, and every other label.

Having a tribe is important to us. It gives us a sense of safety and community — two things we’re evolutionary wired to crave.

So adopting labels is great.

They help us operate in a complex world and make it easier to choose the people we want to identify with — our tribe mates.

The only thing is… labels make us stupid.

Really, really stupid.

Because once we don a label, it becomes part of our identity.

And there are few things we hate more than attacks on our identity.

Ideas, evidence, events, or questions that don’t confirm our adopted labels feel like personal attacks.  

Humans will do just about anything to repel these attacks and maintain their belief in their beliefs.

And since no packaged belief set is 100% correct, it means all of us carry belief structures that fall somewhere on the scale between somewhat wrong and very wrong.

We employ mental contortions that would impress the US Olympic gymnast team… all to avoid acknowledging the wrongness of these beliefs.

Psychologists call this, “Identity Protective Cognition”.

Vox described IPC as:

A way of avoiding dissonance and estrangement from valued groups, individuals subconsciously resist factual information that threatens their defining values… What we believe about the facts, tells us who we are… And the most important psychological imperative most of us have in a given day is protecting our idea of who we are, and our relationships with the people we trust and love.

Why does this matter?

Well, if we all carry some number of untrue beliefs, yet hold them with high conviction, it’ll inevitably lead to friction when our packaged beliefs don’t conform to how reality actually is.

It also makes getting at truth (finding out how things really are) much more difficult.

Take politics for example.

All 200 million US registered voters are pretty evenly split amongst Democrats and Republicans.

These political labels have become a big part of our identities. The sense of tribe within each is strong.

Most of us passively adopted these labels.

We may like to think we actively chose them because of their superiority, but that’s generally not the case.

Most of us adopted the political label of the family we were born into, of the friends we grew up with, of the community we live within.

Just like our religion, this belief set was given to us. Just as it was given to those who gave it to us and on and on. So we passively adopt most of our labels and beliefs without any true stress testing.

The problem is that it’s our default condition to value knowing and “feeling right” over actually being right and getting at truth.

We adopt and cling to false beliefs because we’re afraid of accepting that we don’t know, that we don’t have an answer. We don’t want to acknowledge that the world is infinitely complex and uncertain, and that maybe the things our identities are built upon are wrong.

So we all live in varying degrees of an illusory reality dominated by cognitive dissonance and kept unaware by pride.

And with the rise of the digital era and the constant algorithmic selected news flow, that’s mathematically curated to cater to our labels and flip our emotional switches, we’re becoming more and more attached to these belief sets.

To manage the information/stimulus overload we more tightly cling to our labels to help manage the uncertainty. And the more we do this, the more our belief sets are likely to drift further from reality, from being true.

So we live in an increasingly vicious “Label Stupidity Loop.”

Where the more large and complex the things we’re supposed to care about become, and the more information we’re inundated with, the more fiercely we defend our packaged belief sets and the more wrong we all become.

Blogger Paul Graham wrote about this phenomenon in a post titled “Keep Your Identity Small.” He concludes the piece with:

If people can’t think clearly about anything that has become part of their identity, then all other things being equal, the best plan is to let as few things into your identity as possible.

He’s saying that the way to align more closely with reality and to end up with better outcomes is to be wary of the labels you adopt. And try to keep these labels to a minimum.

This requires a big shift in the way you interact with the world.

You’ll have to transition from a state of false-certainty to one of accepted unknowing.

You’ll need to embrace your fallibility and acknowledge that the world is large and that human nature is complex and that no one has all the answers.

You need to realize that humans will always operate at a knowledge deficit. Where the amount of things we don’t know will always outweigh the things we do. And even those things we “think” we know, need to be held with suspicion, because it’s just as likely that we’re wrong on them too.

In short, be humble and open minded… about everything.

Now since this is a trading blog about markets and such, here’s how this is relevant to you.

Do you classify yourself as a Value Investor? Technical trader? Macro guy? Elliott Wave Theorist?

Do you believe that your method is the best and the other methods are lacking?

Do you identify with your market approach?

Do you think that there’s nothing to gain by objectively studying and putting to the fire the validity of the other styles?

Is it not the most important thing to find any tool or method that you can logically understand and apply to extract money from the markets?

So why would you care what label it falls under?

I’ve long called myself a Macro trader. But that doesn’t mean that I only take what’s considered typical macro trades or only apply macro tools and thinking.

I say macro because I have to give a response. But really, all macro means to me, is to be unconstrained, not boxed in, capable of using all available and effective means to win.

I like the idea of being able to go anywhere and trade anything for whatever compelling reason that makes me think there’s asymmetry.

A trade is a trade is a trade in my book.

And I study all the disciplines that logically make sense to me and which I know others have successfully employed.

These are tools that I get to add to my trading kit.

Value investing? Yeah it works, tons of evidence of it working and lots of successful value only investors out there.

Technical analysis? Of course, PLB is living proof it’s effective… Top down classical macro? Absolutely… another valuable approach to understand and implement.

If it works I want to study it. I want to study the best and I want to know what they know. I want to dive into it with an open mind and find what makes sense, what doesn’t, and what maybe can be approved upon.

We should seek to imitate Bruce Lee and his approach to martial arts and living. Lee explains:

Each man belongs to a style which claims to possess truth to the exclusion of all other styles. These styles become institutes with their explanations of the “Way,” dissecting and isolating the harmony of firmness and gentleness, establishing rhythmic forms as the particular state of their techniques.

Instead of facing combat in its suchness, then, most systems of martial art accumulate a “fancy mess” that distorts and cramps their practitioners and distracts them from the actual reality of combate, which is simple and direct. Instead of going immediately to the heart of things, flowery forms (organized despair) and artificial techniques are ritualistically practiced to simulate actual combat. Thus, instead of “being” in combat these practitioners are “doing” something “about” combat.

Worse still, super mental power and spiritual this and spiritual that are desperately incorporated until these practitioners drift further and further into mystery and abstraction. All such things are futile attempts to arrest and fix the ever-changing movements in combat and to dissect and analyze them like a corpse.

Set patterns, incapable of adaptability, of pliability, only offer a better cage. Truth is outside of all patterns.

What labels do you wear?

What type of belief cage have you built for yourself?

Are you willing to step outside of all patterns, into uncertainty, in order to move closer to truth?