The Distribution Of Returns
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The Distribution Of Returns & The Randomness Embedded In Them

Daily Speculations are for me (Alex) to share some quick thoughts on charts/trades I’m looking at, books and articles I find interesting, or maybe just some photos of my dog Mars. As the name states, I’ll be sharing something daily except for some days when I don’t.

As traders, one of the most important traits we can adopt is humility. We have to embrace our fallibility.

Markets are complex systems. We cannot know all the relevant variables and causal relationships. Therefore, when we make a market prediction or place a trade, we can’t truly know if the subsequent outcome occurred for the reasons we believed, for reasons we’re unaware of, or if it was all just random noise.

This presents us with a dilemma: We must act on incomplete information and then iterate off results where we can’t fully know the causes.

So we never really know if we were right for the right reasons, right for the wrong reasons (i.e., lucky), or a little bit of both. Hence the need to stay humble.

Where traders get into trouble is when they’re right a lot for the wrong reasons but they think they’re right for all the right reasons. They think they’re making money because they’re skilled but really they’re just lucky.

Nassim Taleb writes about this problem, saying:

At any point in time, the richest traders are often the worst traders. This, I will call the cross-sectional problem: At a given time in the market, the most successful traders are likely to be those that are best fit to the latest cycle.

Being aware of the randomness embedded in the distribution of markets returns keeps us from falling for the ego trap where we mistake skill for luck or information for noise. And since the two are difficult to distinguish over a short time frame, it forces us to focus on managing our risk in a way that tilts the odds for success in our favor over the longer term.

In his book Fooled by Randomness, Nassim Taleb offers a useful analogy of the “dentist investor” on the difference between noise and information. Here it is:

The wise man listens to meaning; the fool only gets the noise. The modern Greek poet C.P. Cavafy wrote a piece in 1915 after Philostratus’ adage “For the gods perceive things in the future, ordinary people things in the present, but the wise perceive things about to happen.” Cavafy wrote:

“In their intense meditation the hidden sound of things approaching reaches them and they listen reverently while in the street outside the people hear nothing at all.”

I thought hard and long on how to explain with as little mathematics as possible the difference between noise and meaning, and how to show why the time scale is important in judging a historical event. The Monte Carlo simulator can provide us with such an intuition. We will start with an example borrowed from the investment world, as it can be explained rather easily, but the concept can be used in any application.

Let us manufacture a happily retired dentist, living in a pleasant, sunnytown. We know a priori that he is an excellent investor, and that he will be expected to earn a return of 15% in excess of Treasury bills, with a 10% error rate per annum (what we call volatility). It means that out of 100 sample paths, we expect close to to 68 of them to fall within a band of plus and minus 10% around the 15% excess return, i.e. between 5% and 25% (to be technical: the bell-shaped normal distribution has 68% of all observations falling between -1 and 1 standard deviations). It also means that 95 sample paths would fall between -5% and 35%.

Clearly, we are dealing with a very optimistic situation. The dentist builds for himself a nice trading desk in his attic, aiming to spend every business day there watching the market, while sipping decaffeinated cappuccino. He has an adventurous temperament, so he finds this activity more attractive than drilling the teeth of reluctant old Park Avenue ladies.

He subscribed to a web-based service that supplies him with continuous prices, now to be obtained for a fraction of what he pays for his coffee. He puts his inventory of securities in his spreadsheet and can thus instantaneously monitor the value of his speculative portfolio. We are living in the era of connectivity.

A 15% return with a 10% volatility (or uncertainty) per annum translates into a 93% probability of making money in any given year. But seen at a narrow time scale, this translates into a mere 50.02% probability of making money over any given second as shown in Table 3.1. Over the very narrow time increment, the observation will reveal close to nothing. Yet the dentist’s heart will not tell him that. Being emotional, he feels a pang with every loss, as it shows in red on his screen. He feels some pleasure when the performance is positive, but not in equivalent amount as the pain experienced when the performance is negative.

Probability of Making Money

At the end of every day the dentist will be emotionally drained. A minute-by-minute examination of his performance means that each day (assuming eight hours per day) he will have 241 pleasurable minutes against 239 unpleasurable ones. These amount to 60,688 and 60,271, respectively, per year. Now realize that if the unpleasurable minute is worse in reverse pleasure than the pleasurable minute is in pleasure terms, then the dentist incurs a large deficit when examining his performance at a high frequency.

Consider the situation where the dentist examines his portfolio only upon receiving the monthly account from the brokerage house. As 67% of his months will be positive, he incurs only four pangs of pain per annum and eight uplifting experiences. This is the same dentist following the same strategy. Now consider the dentist looking at his performance only every year. Over the next 20 years that he is expected to live, he will experience 19 pleasant surprises for every unpleasant one!

This scaling property of randomness is generally misunderstood, even by professionals. I have seen Ph.D.s argue over a performance observed in a narrow time scale (meaningless by any standard). Before additional dumping on the journalist, more observations seem in order.

Viewing it from another angle, if we take the ratio of noise to what we call nonnoise (i.e., left column/right column), which we have the privilege here of examining quantitatively, then we have the following. Over one year we observe roughly 0.7 parts noise for every one part performance. Over one month, we observe roughly 2.32 parts noise for every one part performance. Over one hour, 30 parts noise for every one part performance, and over one second, 1,796 parts noise for every one part performance.

A few conclusions:

  1. Over a short time increment, one observes the variability of the portfolio, not the returns. In other words, one sees the variance, little else. I always remind myself that what one observes is at best a combination of variance and returns, no just returns (but my emotions do not care about what I tell myself).
  2. Our emotions are not designed to understand the point. The dentist did better when he dealt with monthly statements rather than more frequent ones. Perhaps it would be even better for him if he limited himself to yearly statements. (If you think that you can control your emotions, think that some people also believe that they can control their heartbeat or hair growth.)
  3. When I see an investor monitoring his portfolio with live prices on his cellular telephone or his handheld, I smile and smile.

George Soros once wrote, “I contend that taking fallibility as the starting point makes my conceptual framework more realistic. But at a price: the idea that laws or models of universal validity can predict the future must be abandoned.”

And market wizard Mark Weinstein would say, “Don’t be arrogant. When you get arrogant, you for sake risk control. The best traders are the most humble.”

So be humble. Take fallibility as your starting point. Be aware of the random nature of markets over various temporal cycles. Don’t mistake noise for information or skill for luck. And always focus on protecting your capital first.

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

 

 

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Ray Dalio’s Portfolio Allocation Strategy: The Holy Grail

Daily Speculations are for me (Alex) to share some quick thoughts on charts/trades I’m looking at, books and articles I find interesting, or maybe just some photos of my dog Mars. As the name states, I’ll be sharing something daily but some days I won’t because I’m lazy.

Here’s an excerpt from Ray Dalio’s recent book Principles recounting his biggest aha!” moment in investing. This epiphany is what helped Dalio develop the unmatched asset allocation strategy he uses in his investment portfolios (emphasis is mine): 

From my earlier failures, I knew that no matter how confident I was in making anyone bet I could still be wrong — and that proper diversification was the key to reducing risks without reducing returns. If I could build properly diversified (they zigged and zagged in ways that balanced each other out), I could offer clients an overall portfolio return much more consistent and reliable than what they could get elsewhere.

Decades earlier, the Nobel Prize-winning economist Harry Markowitz had invented a widely used model that allowed you to input a set of assets along with their expected returns, risks, and correlations (showing how similarly those assets have performed in the past) and determine an “optimal mix” of those assets in a portfolio. But his model didn’t tell you anything about the incremental effects of changing any one of those variables, or how to handle being uncertain about those assumptions. By then I was terribly fearful about what would happen if my assumptions were wrong, so I wanted to understand diversification in a very simple way. I asked Brian Gold, a recently graduated math major from Dartmouth who’d joined Bridgewater in 1990, to do a chart showing how the volatility of a portfolio would decline and its quality (measured by the amount of return relative to risk) would improve if I incrementally added investments with different correlations. I’ll explain it in more detail in my Economic and Investment Principles.

That simple chart struck me with the same force I imaging Einstein must have felt when he discovered E=MC2: I saw that with fifteen to twenty good, uncorrelated return streams, I could dramatically reduce my risks without reducing my expected returns. It was so simple but it would be such a breakthrough if the theory worked as well in practice as it did on paper. I called it the “Holy Grail of Investing” because it showed the path to making a fortune. This was another key moment in our education.

ray dalio's asset allocation strategy

As the Holy Grail chart showed, an equity manager could put a thousand 60 percent-correlated stocks into their portfolios and it wouldn’t provide much more diversification than if they’d picked only five. It would be easy to beat those guys by balancing our bets in the way the chart indicated.

Thanks to my process of systematically recording my investment principles and the results they could be expected to produce, I had a large collection of uncorrelated return streams. In fact, I had something like a thousand of them. Because we traded a number of different asset classes, and within each one we had programmed and tested lots of fundamental trading rules, we had many more high-quality ones to choose from than a typical manager who was tracking a smaller number of assets and was probably not trading systematically.

I worked with Bob and Dan to pull our best decision rules from the pile. Once we had them, we back-tested them over long time frames, using the systems to simulate how the decision rules would have worked together in the past.

We were startled by the results. On paper, this new approach improved our returns by a factor of three to five times per unit of risk, and we could calibrate the amount of return we wanted based on the amount of risk we could tolerate. In other words, we could make a ton more money than the other guys, with a lower risk of being knocked out of the game — as I’d nearly been before. I called it the “killer system” because it would either produce killer results for us and our clients or it would kill us because we were missing something important.

The success of this approach taught me a principle that I apply to all parts of my life: Making a handful of good uncorrelated bets that are balanced and leveraged well is the surest way of having a lot of upside without being exposed to unacceptable downside.

This is an important concept to understand.

Diversifying with over 15 uncorrelated return streams and balancing out your return per unit of risk, through sizing and leverage (ie, leveraging bonds to equal equity on a return per risk unit basis), can get you to a balanced global or market-neutral position.

This is where your risk is balanced out and you’re effectively clipping beta coupons from global markets and various asset classes.

An important note I should make is that to build this market-neutral book you really have to understand cross-asset correlation. It’s not correlation in the typical sense, where you use a market lookback period of say three years to see how much in line those asset classes have moved together.

The correlation has to do with the fundamental drivers of each asset class (ie, what are the economics that drive investors to buy and sell each asset class). At its most basic, this idea can be boiled down to two inputs of growth and inflation which when combined give you four stages.

Different asset classes will perform well in some stages and less well in others, as the graph to the right shows.

You can dive really deep into this and begin to do some interesting stuff — I’ll save that for another post on another day.

But, as a global macro trader, I love the idea of having a market neutral book that collects beta in a smart risk-adjusted way.

This gives me an excellent base from which to operate off of and build an alpha overlay by making what are typically low probability but high expected value (EV+) convex bets, preferably using leverage on top of my beta.

This takes the pressure off me as the trader to always be in the market with a position and allows me to focus my time on seeking out the fat pitches and asymmetric trades that only come around every so often.

I’ve long been a proponent of the old-school macro approach used by Soros and Druck where you’re highly concentrated. You have just a few eggs in your basket that you watch intensely. But a more optimal approach is a combination of the two. Use diversification to collect your beta and overlay that with a concentrated book for your alpha.

Make sense?

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 and email below.

Thanks for reading,

Alex

 

 

 

Liquidity The Most Important Fundamental
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What Market Liquidity Is & Why It’s The Most Important Fundamental

(Note: If you’re interested in learning how to gauge market liquidity and sidestep the next market selloff, then download our Liquidity Tracking Guide here.)

I’m sure you’ve heard analysts, financial pundits, and other babbling heads yabber on and on about how these markets don’t reflect the “fundamentals”.

They’ve ranted non-stop about how the fundamentals prove that a bear market is around the corner.

They’ve raved about valuations being stretched and how stocks will collapse any day now…

If you’ve been taking investment advice from these doomsdayers, then please accept my condolences for your portfolio loss.

These broken clocks should heed the words of Mark Twain:

Denial ain’t just a river in Egypt.

No, denial is not just a river in Egypt, it’s also the perpetual state most market participants live in.

Now I’m not bashing the usefulness of what are commonly thought of as fundamentals. Things like earnings per share, book value, and revenue growth are indeed important.

What I’m saying is that these are only a few pieces of a much larger puzzle.

The dictionary defines the word fundamental as, “a central or primary rule or principle on which something is based.”

If there’s one “central or primary rule” on which all fundamentals are based, it’s market liquidity. Liquidity is the Mac-Daddy of fundamental inputs. And not surprisingly, it’s the least known and understood.

(Note: If you want to learn how to track liquidity to identify the next market crash, then check out this guide right now.)

Here’s one of the greatest of all time, Stanley Druckenmiller, on the importance of liquidity (emphasis mine):

Earnings don’t move the overall market; it’s the Federal Reserve Board… focus on the central banks and focus on the movement of liquidity… most people in the market are looking for earnings and conventional measures. It’s liquidity that moves markets.

So what is liquidity exactly?

In simple terms, liquidity is demand, which is the willingness of consumers to purchase goods and other assets. This demand is driven by the tightening and easing of credit.

What we usually think of as money (the stuff we use to buy things) is comprised of both hard cash + credit. The amount of hard cash in the system is relatively stable. But credit is extremely elastic because it can be created by any two willing parties. It’s this flexibility that makes it the main factor in driving liquidity/demand.

The majority of credit, and therefore money, is created outside the traditional banking sector and government. Most is created between businesses and customers. When businesses purchase wholesale supplies on credit; money is created. When you open a Best Buy credit card to purchase that new flat screen TV; money is created. And when you purchase stocks on margin from your broker; money is created.

The logic is simple. The more liquidity and credit in the system, the more demand, which in turn pushes markets higher.

Which leads us to our next question: What are the largest levers that affect the amount of credit, money, and liquidity in the system?

The answer to that is interest rates. These are set by both central banks and the private market.

The primary rate set by central banks is the largest factor in determining the cost of money. And the cost of money in turn determines liquidity/demand in the system.  

When the cost of money is low (low interest rates) more demand is created in two ways: [1] it makes sense to exchange lower yielding assets for riskier, higher yielding ones and [2] more people are willing to borrow and spend (money is created) because credit is cheaper.

This affects the stock market in two ways: [1] share prices rise as investors trade up to riskier assets and [2] companies’ total sales increase because of higher consumer demand caused by cheaper credit. Liquidity affects both the denominator (earnings) and numerator (price per share) in stock valuations as it drives markets higher.

You may be asking yourself, “well, if the primary rates set by central banks are this important, then will markets stay forever inflated as long as they keep rates low?”

No, they won’t.

Though central bank rates are the largest influence on demand and the cost of money, they are not the only influence.

The private sector assigns its own rates based off the central bank rate, but also includes an additional premium (or spread) that fluctuates according to the credit risks they see in the market.

For instance, even though the Fed Funds rate has remained near zero over the last two years, interest rates on high-yield loans (the primary lending market to the energy sector) ballooned during the recent oil collapse because of increased perceived risks. Money tightened and became more expensive as liquidity became constrained in that sector. This type of liquidity tightening is what causes markets to fall, regardless of whether the primary rate is low or not.

The way liquidity ebbs and flows directly affects market narratives.  

The 2008 financial crisis occurred because central banks cranked up liquidity to jumpstart the economy after the 2000 tech bust. All this extra money got dumped into housing. That’s how the bullish real estate narrative was born. Eventually a bubble formed and later popped as liquidity dried up.   

And of course the central bank’s response was to ease even more. They’ve now kept the liquidity spigots blasting longer than any other time in history. As long as liquidity conditions stay positive, we can expect the bulls to keep running.

Like Druck said “It’s liquidity that moves markets”. Bull markets, bear markets, everything.

Knowing how to gauge liquidity is the number one thing you can do protect your capital and profit.

To learn how to gauge liquidity and sidestep the next market crash, download our liquidity tracking guide and cheat sheet here.

 

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Do Buyers Of Options Benefit From High Volatility?

Search “options and volatility” in Google and you’ll get a dozen websites that say the same thing: option buyers want high volatility and option sellers want low volatility. Oddly enough, this old and relied upon rule of thumb isn’t completely correct.

In reality, as an option trade plays out, trend has a far more powerful effect than volatility on final P&L.

There are four scenarios that can occur before an option expires:

  • Low Vol, Low Trend
  • Low Vol, High Trend
  • High Vol, Low Trend
  • High Vol, High Trend

The charts below illustrate the P&L of a long put in each scenario.

Example 1 — Low Vol, Low Trend

Low Vol, Low Trend

The red path is the price action of the underlying stock over the course of the trade. Notice how it moves along smoothly with little volatility.

In this example, the long put would expire at a low price. The underlying (red line) isn’t far enough away from the strike price at expiration. A trader who bought this put would’ve lost money.

Example 2 — Low Vol, High Trend

In this situation the put holder would be sitting on a huge gain! The underlying trended far away from the put strike and the option expired well into the money. Just like the last example, the red line moved smoothly. But in this case it happened to trend down instead of sideways. Despite low volatility, the put holder made some serious money. The “rule of thumb” requiring high vol broke down.

Example 3 — High Vol, Low Trend

High Vol, Low Trend

The put buyer lost money here. The underlying didn’t trend far enough away from the strike to overcome the option premium. There was high volatility throughout the life of the trade, but the put buyer still got taken to the woodshed. The price of the underlying at expiration was all that mattered here. The volatility “rule of thumb” broke down again.

Example 4 — High Vol, High Trend

High Vol, High Trend

In this final example, the put buyer received a nice profit. The underlying trended downward with steep pullbacks, but price was far enough away from the put strike to deliver gains. In this situation many traders would think the high vol over the course of the trade contributed to profits. But in reality it didn’t. The trend created the profits.

Do you see a pattern here?

Volatility didn’t have any effect on the final P&L of these scenarios. The trend is what mattered.

In examples 1 and 3, the put buyer lost money in both low and high volatility. The underlying had no trend.

In examples 2 and 4, the put buyer booked some nice gains because the underlying trended. Again, the volatility of the trend did not matter.

If you do not delta hedge over the course of a trade, all that matters is where the underlying expires relative to the strike price.

Delta hedging consists of buying and selling stock against an option to cancel out its directional bias. Without offsetting delta (direction), an option trade becomes a bet on trend vs. consolidation, not high vs. low volatility. If you want the “rule of thumb” to hold true, you need to delta hedge over the course of your trade.

Market makers and investment banks typically delta hedge to create a pure play on volatility rather than direction. Here’s how they’d handle the same long put as above, but this time with delta hedging.

Example 5 — High Vol, Low Trend, Delta Hedging

High Vol, Low Trend, Delta Hedging

If the market maker is long a put, he’ll immediately buy some stock against it to hedge out the directional component. As the stock starts falling the put’s short delta will increase. This will cause the market maker to buy more stock during the dip to offset it.

If the stock starts to rally back near the put’s strike, the put’s short delta will decrease. Now the market maker will sell some of his long stock to balance things out. This is how he keeps his position “delta neutral.”

As the trade goes on, the market maker will keep buying stock on dips and selling on rips. These delta hedges end up being profitable trades because he’s buying low and selling high. By expiration he’ll generate a significant amount of gains from just buying and selling the stock.

The value of the option decayed to almost nothing by expiration (the underlying expired close to the strike) but the profitable hedge trades made up for that loss and then some.

The market maker made money!

Notice how different this trade turned out compared to example 3 with no delta hedging. In the delta hedging example here, the volatility of the underlying had a much bigger impact on the P&L than the trend.

So the “rule of thumb” — option buyers want high vol — only applies if the trader is hedging delta. If he isn’t hedging delta, then high vol won’t do much for him. He’ll still lose on the trade if the underlying expires near the strike.

Now let’s see how the market maker’s trade fairs in a low vol, low trend scenario:

Example 6 — Low Vol, Low Trend, Delta Hedging

Low Vol, Low Trend, Delta Hedging

Here the peaks and valleys of the underlying aren’t nearly as intense as in the prior high vol example. The market maker still buys low and sells high each time the underlying moves, but with much less profit than before.

By expiration the delta hedging trades aren’t profitable enough to offset the option premium (which was lost because price was too close to the strike at expiration). The market maker ends up losing money.

By now it should be a little easier to see why option buyers who delta hedge want wild, highly volatile oscillations. Without the high vol, their hedges don’t make enough money to make the trade profitable.

Example 7 — Low Vol, High Trend

Low Vol, High Trend

Most people who buy puts are used to winning big when the underlying has a large downward trend. But this isn’t the case for a market maker who’s delta hedging.

The downward low vol trend had the market maker buying dips with no rips to sell into. The result — a long stock hedge that was continually averaged down over the life of the trade.

In this case, the long put had a nice gain because of how far away the underlying trended from the strike price. But the hedge ended up being a huge loser that negated the option gains. Unlike the trader who didn’t hedge and let the trend work for him, the market maker didn’t do so well. The market maker’s position was more reliant on the volatility of the underlying rather than the trend. Low vol meant a poor end result.

Example 8 — High Vol, High Trend

High Vol, High Trend.A

In this last example, we still have the same downtrend, but this time with more spikes along the way for the market maker to sell into. These spikes allow the delta hedges to make money. And at the same time, the long put has a nice gain because the underlying expired far away from the strike. The high volatility of the trend made it possible for the market maker to profit.

The key takeaway here is:

Traders who don’t hedge delta rely on the trend of the underlying more than its volatility to profit.

Traders who do hedge delta rely on the volatility of the underlying more than its trend.

It usually doesn’t make sense to hedge your delta unless you have a professional commission structure. This is why so few do it. All those hedging trades rack up commissions. And the execution of those hedges requires a lot of screen time or advanced software than can do it automatically.  

So if you’re not delta hedging, a better question to ask yourself before placing an option trade is:

Do I believe the underlying will trend or consolidate over the life of the option?

If your answer to that question is “I think the underlying will trend”, you should buy optionality. The underlying will trend away from the strike price and you’ll make money on the option you purchased.

If your answer to that question is “I think the underlying will consolidate”, you should sell optionality. The underlying will stay close to the strike price and you’ll collect the premium from the option you sold.

For more information on how we like to trade options at Macro Ops, check out our special report here.

 

 

Traders As Modern Day Hunter-Gatherers
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Traders As Modern Day Hunter-Gatherers

Entering the market is like entering an entirely different world. This world functions nothing like modern society.

Modern society has laws and social norms in place to protect you. If you screw up, you have a safety net. People will generally reach out and help.

Modern society is cushy. Even people who are disabled, suffering mental illness, or are unable to care for themselves in other ways still get by. (Maybe not as well as we’d always like, but far better than at any other time in the past.)

Markets aren’t like modern society.

They’re much closer to being like the world in its most natural state.

The strong prey on the weak.

You live or die based on your own ability.

While the system can sustain a vast array of life and species for large spans of time, each player constantly faces the risk of death. Markets are unforgiving and brutal, yet incredibly abundant places to thrive — just like natural ecosystems.

In this world… traders are the modern day hunter-gatherers.

Hunter-gatherers sustain themselves by staying in sync with the large, complex ecosystem they’re apart of. This ecosystem is full of both opportunities and threats. The hunter-gatherers don’t control anything, but instead seek to achieve harmony with the changes around them.

A key component of long-term survival in the wild is adapting and evolving. Failure to adapt to the changing landscape means you won’t be fed.

The same goes in markets. Markets trade a lot differently now than they did in 1980. If you don’t adapt your strategy along the way you’ll become irrelevant. You’ll starve.

Hunter-gatherers don’t try to extract every last bit of value from a situation. They wouldn’t kill all the deer in a herd even if they could. What would they do with all that meat? There’s no fear of “leaving money on the table” as long as they get what they need to thrive.

Instead of maximizing return, hunter-gatherers seek to achieve satisfactory returns while minimizing risk. They focus on risk-adjusted return. Risk is easily forgotten in a civilized society where nearly everything is already idiot-proofed. But risk has to be at the front of a hunter-gatherer’s mind.

Hunter-gatherers don’t have to win all the time. They just need to win often enough to stay fed. In the markets, you can make billions by calling it right just 55% of the time.

Since the world is abundant, hunter-gathers’ timeframes are shortened. Unlike farmers or industrialists, hunter-gatherers focus on the next season (or maybe next few if they build up stores to ride out the lean times). But beyond that, why worry? The world will provide endless opportunities for someone who can live off the land. There’s never a “fear of missing out”.

As a trader, embracing this hunter-gatherer mindset will align you with the reality of markets. Doing so will help you take your returns to the next level.

The above passage is straight from Operator Biren, a member of the Macro Ops Hub. To learn more about the Hub and how you can join our Operator team, click here.

 

Markets as a Range of Reasonable Opinions
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Markets as a Range of Reasonable Opinions

The following is from The Philosopher in Drobny’s classic The Invisible Hands (emphasis mine):

Some people can trade markets using only numbers, prices on a screen but this approach does not work for me. The numbers have to mean something — I have to understand the fundamental drivers behind the numbers. And while fundamentals are important, they are only one of many important inputs to the process. Just as a Value-at-Risk (VaR) model alone cannot tell you what your overall risk is, economic analysis alone cannot tell you where the bond market should be.

Let us use an interest rate trade around central bank policy as a straightforward example to illustrate my process. Economic drivers will create the framework: What is the outlook for growth, inflation, employment, and other key variables? What will the reaction of the central bank be? We then build a model of the potential outcomes of these economic drivers, weighting them according to probabilistic assumptions about our expectations. We look at what the central bank could do in each scenario, comparing this with market prices to see if there are any interesting differences. When differences exist, we then think about what can drive those differences to widen or converge.

It is important to note that a key element to this exercise is the fact that what other people believe will happen is just as important as the eventual outcome. A market is not a truth mechanism, but rather an interaction of human beings whereby their expectations, beliefs, hopes, and fears shape overall market prices.

A good example of this psychological element can be seen in inflation. At the end of 2008, U.S. government fixed income was pricing in deflation forever. At that point, the only thing of interest to me was the question of whether people might think that there could be inflation at some point in the future. Quantitative easing made it easy to answer this question affirmatively, because there are many monetarists in the world who believe that the quantity of money is the driver of inflation. Whether they are right or not is a problem for the future — what is important to me is that such people exist today. Their existence makes the market pricing for U.S. long bonds completely lopsided. Such pricing only makes sense if you are a died-in-the-wool output gapper who believes that when unemployment goes up, inflation goes down, end of story. Market prices reflect the probability of potential future outcomes at that moment, not the outcomes themselves. Some people do not believe in the output gap theory of inflation, and these people believe that pricing for U.S. bonds should be somewhere else. Because these two divergent schools of thought exist, it is possible that market sentiment can shift from deflation to inflation and that pricing will follow.

One way to think about my process is to view markets in terms of the range of reasonable opinions. The opinion that we are going to have declining and low inflation for the next decade is entirely reasonable. The opinion that we are going to have inflation because central banks have printed trillions of dollars is also reasonable. While most pundits and many market participants try to decide which potential outcome will be the right one, I am much more interested in finding out where the market is mispricing that skew of probabilities. If the market is pricing that inflation will go to the moon, then I will start talking about unemployment rates, wages going down, and how we are going to have disinflation. If you tell me the markets are pricing deflation forever, I will start talking about the quantity theory of money, explaining how this skews outcomes the other way. Most market participants I know do not think in these terms. The market is extremely poor at pricing macroeconomics. People always talk about being forward-looking, but few actually are. People tell stories to rationalize historical price action more frequently than they use potential future hypotheses to work out where prices could be.

Beauty contestsPlaying the player… Second level thinking… Viewing markets as a range of reasonable outcomes… These are points we write about over and over. And that’s because the overarching concept is so important and yet so misunderstood.

Let me give you an example.

Your average retail trader (and even most “professionals”) read in the paper, magazines, blogs, etc. that Europe is on the brink of collapse. Deutsche bank is teetering on insolvency… populism is rising… the UK is leaving… it’s all going to hell in a handbasket.

They think to themselves, “Man, Europe is in trouble. I need to short some European banks and sell the euro.”

But those playing the game at the second level and above read the same articles and come away with a completely different train of thought:

Bearish sentiment on Europe is really reaching a zenith… Every market pundit and blogger is railing about how bad Europe is… Bearish positioning is extremely one-sided as there’s definite market consensus… Which means this narrative is likely baked into the price as everybody who’s going to sell has already sold… And if the public narrative is this bearish then the central bankers will be too… So they’ll err on the dovish side for the foreseeable future… Which means that the entire market is standing on the wrong side of the boat… I need to buy Deutsche call options and go long the euro.

The first level thinkers are part of the herd and the second level thinkers are the wranglers anticipating where the dumb herd will swing to next.

First level traders believe trading is about correctly predicting the future. They are wrong.

Successful trading is about understanding prevailing market expectations.

Understand the narrative and you can understand the key drivers. Understand the key drivers and you can identify the fulcrum point of the narrative (the data point that if changed, will force a new narrative to be adopted).

Then you take this understanding and closely watch how reality unfolds in comparison to expectations, all while keeping an eye on divergences (mispricings) that create asymmetric trade opportunities.  

Here’s the Palindrome (George Soros) on the topic (emphasis mine).

There is always a divergence between prevailing expectations and the actual course of events. Financial success depends on the ability to anticipate prevailing expectations and not real-world developments. But, as we have seen, my approach rarely produces firm predictions even about the future course of financial markets; it is only a framework for understanding the course of events as they unfold. If it has any validity it is because the theoretical framework corresponds to the way that financial markets operate. That means that the markets themselves can be viewed as formulating hypotheses about the future and then submitting them to the test of the actual course of events. The hypotheses that survive the test are reinforced; those that fail are discarded. The main difference between me and the markets is that the markets seem to engage in a process of trial and error without the participants fully understanding what is going on, while I do it consciously. Presumably that is why I can do better than the market.

Understand that the things you read in the paper, see on Twitter, or hear on TV, are all popular knowledge — in game theory this is knowns as common or mutual knowledge. The more widely known the information, the more likely it’s already been discounted by the market.

Markets lead the news… not the other way around.

Truly understanding this and applying it is how you become an effective contrarian. And operating as an effective contrarian is the only way you can win in the game of speculation.

If you want to learn how to become an effective contrarian, then check out our Trading Instructional Guide here.

 

 

The Capital Cycle
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How The Capital Cycle Works

The following is an excerpt from our monthly Macro Intelligence Report (MIR). If you’re interested in learning more about the MIR, click here.

If you’ve been following Macro Ops for a while, then you know the Bridgewater Debt Cycle model is the foundation for how we view larger market movements. The debt cycle drives the short-term business cycle (5-8 years) as well as the longer-term secular cycle (50-75 years).

Here’s how it works:

  1. The central bank lowers interest rates, bringing down the cost of money
  2. This lower rate feeds into the rest of the economy, bringing down lending rates
  3. Borrowing becomes cheaper and more attractive, driving consumers and businesses to borrow and spend more (boosting demand)
  4. Existing debt becomes cheaper to service, leaving consumers and businesses with more income to spend (boosting demand)
  5. The discount rate at which businesses and financial assets (risk-premia spread) are valued is lowered, increasing the present value of assets, which creates a flow into riskier assets (boosting demand)
  6. Since one person’s spending is another’s income, a wealth effect is created and credit profiles improve, allowing consumers/businesses to borrow and spend more, creating a virtuous demand cycle

Eventually, central banks raise interest rates and the feedback loop shifts into reverse, until interest rates are lowered once again and the cycle starts anew. Short-term debt cycles compound into long-term debt cycles. This is how demand spawns and how bull and bear markets are born and die.

Again, if you’ve been following us for some time, then you know that we’re in the tail end of the current short-term debt cycle. And this short-term debt cycle is on the backend of the long-term debt cycle. This means we’re in the early stages of a secular deleveraging, which is why growth has been so elusive and also why Western politics have been so populous (a period not unlike the last secular deleveraging in the 1930’s).

The Debt Cycle model looks at everything from a demand perspective. But we can also look at these cycles from the viewpoint of supply. Doing so gives us greater granularity of the forces at work. Read more

Understanding The Difference Between Alpha, Beta, & Cash Returns
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Understanding The Difference Between Alpha, Beta, & Cash Returns

At their highest level, investment returns can be subdivided into three components: the cash rate, beta, and alpha.

return = cash + beta + alpha

The cash rate is the base interest rate controlled by central banks. Every other asset is priced off this rate, including stocks and bonds.

A majority of the time stocks and bonds return more than the cash rate to incentivize investors to take risk. This makes intuitive sense. Why would someone buy risky assets if they could earn the same return in their checking account? Read more

How Short-Term and Long-Term Debt Cycles Work
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How Short-Term and Long-Term Debt Cycles Work

Conventional economic “wisdom” fails to understand the role of credit/debt in our market based system. Mainstream economics completely neglects to understand not only credits affect on demand, but also how this credit demand fluctuates in both short and long-term cycles. Read more

George Soros
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Lies, Untruths, and False-Trends: George Soros on what really moves markets

George Soros was quoted in a speech he gave to the Committee for Monetary Research and Education back in the early 90’s as follows:

Economic history is a never-ending series of episodes based on falsehoods and lies, not truths. It represents the path to big money. The object is to recognize the trend whose premise is false, ride that trend, and step off before it is discredited.

“Falsehoods and lies…,” these are some striking words from one of the greatest traders of all time. It’s also profound insight into how markets really work.

What the Palindrome (his name is spelled the same forward and backwards) is really talking about here is his theory on false trends.

The idea of false trends in markets is predicated on the belief that contrary to common Western thinking, reality cannot be neatly packaged into true and false; black and white.

Rather, Soros believes that reality (and markets) should be classified into three categories:

● Things that are true
● Things that are untrue
● Things that are reflexive

He noted the importance of differentiating between these when he said:

The truth value of reflexive statements is indeterminate. It is possible to find other statements with an indeterminate truth value, but we can live without them. We cannot live without reflexive statements. I hardly need to emphasize the profound significance of this proposition. Nothing is more fundamental to our thinking than our concept of truth.

For those of you not familiar with the concept of reflexivity, go and read our explanation here, it’ll be worth your time.

The benefit of judging truth and untruth on a sliding scale versus fixed one has also been discussed by Nassim Taleb:

Since Plato, Western thought and the theory of knowledge have focused on the notions of True-False; as commendable as it was, it is high time to shift the concern to Robust- Fragile, and social epistemology to the more serious problem of Sucker-Nonsucker.

False trends arise when a dominant belief (what we refer to as a narrative) is founded on untrue assumptions, but the narrative is so strong it moves price action anyway. The false narrative’s effect on the market actually acts to reinforce the strength of the belief that its initial assumptions are correct; thus driving price action further away from reality (what is true) in a reflexive loop. This is how bubbles are created.

Soros discussed the large impact false trends can have on markets in his 2010 “Act II of the Drama” speech. Below is an excerpt and the full text can be found here:

Let me briefly recapitulate my theory for those who are not familiar with it. It can be summed up in two propositions. First, financial markets, far from accurately reflecting all the available knowledge, always provide a distorted view of reality. This is the principle of fallibility. The degree of distortion may vary from time to time. Sometimes it’s quite insignificant, at other times it is quite pronounced. When there is a significant divergence between market prices and the underlying reality I speak of far from equilibrium conditions. That is where we are now.

Second, financial markets do not play a purely passive role; they can also affect the so-called fundamentals they are supposed to reflect. These two functions that financial markets perform work in opposite directions. In the passive or cognitive function, the fundamentals are supposed to determine market prices. In the active or manipulative function market, prices find ways of influencing the fundamentals. When both functions operate at the same time, they interfere with each other. The supposedly independent variable of one function is the dependent variable of the other, so that neither function has a truly independent variable. As a result, neither market prices nor the underlying reality is fully determined. Both suffer from an element of uncertainty that cannot be quantified. I call the interaction between the two functions reflexivity. Frank Knight recognized and explicated this element of unquantifiable uncertainty in a book published in 1921, but the Efficient Market Hypothesis and Rational Expectation Theory have deliberately ignored it. That is what made them so misleading.

Reflexivity sets up a feedback loop between market valuations and the so-called fundamentals which are being valued. The feedback can be either positive or negative. Negative feedback brings market prices and the underlying reality closer together. In other words, negative feedback is self-correcting. It can go on forever, and if the underlying reality remains unchanged, it may eventually lead to an equilibrium in which market prices accurately reflect the fundamentals. By contrast, a positive feedback is self-reinforcing. It cannot go on forever because eventually, market prices would become so far removed from reality that market participants would have to recognize them as unrealistic. When that tipping point is reached, the process becomes self-reinforcing in the opposite direction. That is how financial markets produce boom-bust phenomena or bubbles. Bubbles are not the only manifestations of reflexivity, but they are the most spectacular.

In my interpretation equilibrium, which is the central case in economic theory, turns out to be a limiting case where negative feedback is carried to its ultimate limit. Positive feedback has been largely assumed away by the prevailing dogma, and it deserves a lot more attention.

I have developed a rudimentary theory of bubbles along these lines. Every bubble has two components: an underlying trend that prevails in reality and a misconception relating to that trend. When a positive feedback develops between the trend and the misconception, a boom-bust process is set in motion. The process is liable to be tested by negative feedback along the way, and if it is strong enough to survive these tests, both the trend and the misconception will be reinforced. Eventually, market expectations become so far removed from reality that people are forced to recognize that a misconception is involved. A twilight period ensues during which doubts grow and more and more people lose faith, but the prevailing trend is sustained by inertia. As Chuck Prince, former head of Citigroup, said, “As long as the music is playing, you’ve got to get up and dance. We are still dancing.” Eventually a tipping point is reached when the trend is reversed; it then becomes self-reinforcing in the opposite direction.

Typically bubbles have an asymmetric shape. The boom is long and slow to start. It accelerates gradually until it flattens out again during the twilight period. The bust is short and steep because it involves the forced liquidation of unsound positions. Disillusionment turns into panic, reaching its climax in a financial crisis.

The simplest case of a purely financial bubble can be found in real estate. The trend that precipitates it is the availability of credit; the misconception that continues to recur in various forms is that the value of the collateral is independent of the availability of credit. As a matter of fact, the relationship is reflexive. When credit becomes cheaper, activity picks up and real estate values rise. There are fewer defaults, credit performance improves, and lending standards are relaxed. So at the height of the boom, the amount of credit outstanding is at its peak, and a reversal precipitates false liquidation, depressing real estate values.

The bubble that led to the current financial crisis is much more complicated. The collapse of the subprime bubble in 2007 set off a chain reaction, much as an ordinary bomb sets off a nuclear explosion. I call it a superbubble. It has developed over a longer period of time, and it is composed of a number of simpler bubbles. What makes the superbubble so interesting is the role that the smaller bubbles have played in its development.

The prevailing trend in the superbubble was the ever-increasing use of credit and leverage. The prevailing misconception was the belief that financial markets are self-correcting and should be left to their own devices. President Reagan called it the “magic of the marketplace,” and I call it market fundamentalism. It became the dominant creed in the 1980s. Since market fundamentalism was based on false premises, its adoption led to a series of financial crises. Each time, the authorities intervened, merged away, or otherwise took care of the failing financial institutions, and applied monetary and fiscal stimuli to protect the economy. These measures reinforced the prevailing trend of ever-increasing credit and leverage, and as long as they worked, they also reinforced the prevailing misconception that markets can be safely left to their own devices. The intervention of the authorities is generally recognized as creating amoral hazard; more accurately it served as a successful test of a false belief, thereby inflating the superbubble even further.

If nothing else, these words from Soros should impart a deep respect for the complexity of the trading game we play. It should also explain why, like the Palindrome, our approach to markets should start with the full acceptance of our own fallibility, first and foremost.

The occurrence of false trends will only rise as global information and interpretation flow increases and narratives become more uniformed and accordant. Taleb put it well, when he said:

The mind can be a wonderful tool for self-delusion – it was not designed to deal with complexity and nonlinear uncertainties. Counter to the common discourse, more information means more delusions: our detection of false patterns is growing faster and faster as a side effect of modernity and the information age: there is this mismatch between the messy randomness of the information-rich current world with complex interactions and our intuitions of events, derived in a simpler ancestral habitat – our mental architecture is at an increased mismatch with the world in which we live.

Look around you… do you see any false trends in the markets at the moment?