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The Human Trader’s Secret Weapon

The following is an excerpt from our Macro Intelligence Report (MIR). If you’d like to learn more about the MIR, click here.

Choosing individual stocks without any idea of what you’re looking for is like running through a dynamite factory with a burning match. You may live, but you’re still an idiot. ~ Joel Greenblatt

Investing is hard.

It’s a game of relative comparisons. We have limited capital and nearly unlimited opportunities to deploy it. Our job then as traders/investors (I use the terms interchangeably but will use investor from here on out) is to use our tools to sift through the thousands of stocks, bonds, and currencies to pick and select the handful of assets we think will give us a higher return than the market. This is obviously no easy feat…

The question we get from readers more than any other is about the framework we use to identify these asymmetric opportunities. They want to know how to sift through all the noise and numbers and find the stocks that are going to make them money!

A big piece of this puzzle is by first defining what exactly it is you’re looking for so you’ll know it when you see it. Once you’ve defined it, you can create a framework and process for identifying it. Then rinse and repeat…

That’s what we’re going to do in this month’s report. We’re going to discuss the different classifications of equity investing opportunities and then focus on our favorite, that of the long-term compounder. We’ll walk you through the first principles of value investing and then go through the step-by-step process of our framework for identifying stocks with massive long-term compounding potential.

You may be asking, aren’t you guys macro traders? Why are you writing about fundamental value investing?

That’s a fair question… You see, the key point about being a macro trader is that we’re not constrained by a rigid and narrow approach to markets. Our sole guiding philosophy is to make high risk-adjusted returns using whatever means necessary.

This is a flexible and opportunistic approach. We care only about positive asymmetry and not about what tools or mental frameworks (ie, technicals, fundamentals, classical macro etc…) we need to use to find them.

In reality, nearly every investment includes some combination of different factors and drivers. The best trades are the ones where the entire Marcus Trifecta of technicals, sentiment, and fundamentals align together in a fat pitch setup.

Like a warrior going into battle we don’t see the utility in limiting ourselves to a single weapon or style of fighting. Similar to Bruce Lee’s Jeet Kune Do, we aim to use anything and everything that works to help us win.

Value investing and understanding how to discover and identify long-term compounders is an essential tool in the macro trader’s toolkit. And that’s what we’re going to give you a master class in today. We’ll conclude by using this framework to analyze two stocks that we believe have long-term multibagger potential.

And hopefully after reading this report you’ll never feel like you’re running through a dynamite factory with a burning match again…

Breaking it down to the principle level

I believe there are an infinite number of laws of the universe and that all progress or dreams achieved come from operating in a way that’s consistent with them. These laws and the principles of how to operate in harmony with them have always existed. We were given these laws by nature. Man didn’t and can’t make them up. He can only hope to understand them and use them to get what he wants. ~ Ray Dalio

Every investing framework and process we build needs to be built upon clear, simple, and universal principles. Let’s discuss what some of these are.

An investor can have any combination of the following three edges:

  1. Informational: They can be privy to information that the market is not; through proprietary data (ie, using satellites to track foot traffic at stores) or by extreme due diligence in less watched areas of the market (really digging into the micro cap space) or by less scrupulous methods (insider knowledge).
  2. Analytical: They can look at the same data but come to different and superior conclusions through greater due diligence and/or better frameworks for understanding the world.
  3. Behavioral: They have better understanding and control of their own nature and thus exploit behavioral anomalies that arise in markets largely due to short-term emotional overreactions.

We briefly touched upon in last week’s note how the informational advantage has largely been arbed away due to the wide scale availability of powerful quantitative tools and screeners and information dissemination in general. At least for the retail investor, who doesn’t have access to proprietary credit card and store receipt data, and can’t plug into their satellite that’s tracking Walmart North American store traffic, they are left with the final two edges of analytical and behavioral — we can use this fact to our advantage.

The talented hedge fund manager and value investor Scott Miller said recently in an interview that he welcomes the proliferation of quantitative investing, remarking (emphasis mine):

I actually want quantitative strategies to proliferate. I want money to pile into them, gobs and gobs of it. The more money into quant strategies the better, as I think they are likely to create distortions that I can take advantage of over time. You can have your backward looking quantitative data and use that for the foundation of your decisions. I would rather understand the product, market, and management team of the companies I am investing in.  

We agree with Scott.

Our analytical edge needs to be in seeing the same data but assembling the pieces differently, in the hopes of creating a truer representation of the underlying business and its intrinsic value.

Joel Greenblatt often mentioned in his investing class at Columbia that he believed he was only average at valuation work (he had little edge there), but where he excelled — where his edge lay — came in being able to put the information together in context; view things from the bigger picture and pinpoint the factors that really mattered.

He was quoted as saying:

Explain the big picture. Your predecessors (MBAs) failed over a long period of time. It has nothing to do about their ability to do a spreadsheet. It has more to do with the big picture. I focus on the big picture. Think of the logic, not just the formula.

He only had access to the information everyone else had but he was able to piece it together to come to a completely different and more true conclusion — develop a variant perception. This is what an analytical edge is.

So we know that our value investing framework needs to include mental models for viewing and interpreting data in a more useful way. It needs to help give us a variant perception of reality and strengthen our analytical edge.

There are a number of ways to think about the behavioral edge. One being the emotionally driven overreactions to certain events (could be a missed earnings, negative press, or a broader market selloff) that create large valuation gaps. Long-time hedge fund manager, Bill Miller, puts it like this:

The securities we typically analyze are those that reflect the behavioral anomalies arising from largely emotional reactions to events. In the broadest sense, those securities reflect low expectations of future value creation, usually arising from either macroeconomic or microeconomic events or fears. Our research efforts are oriented toward determining whether a large gap exists between those low embedded expectations and the likely intrinsic value of the security. The ideal security is one that exhibits what Sir John Templeton referred to as “the point of maximum pessimism.”

Which brings us to another foundational principle about value investing: The best value investments will always have a well articulated and very convincing logic as to why they’re priced the way they are. These bearish arguments will always be predicated on a certain amount of truth. It’s this convincing narrative that creates the large mispricing. The thing is, these narratives tend to build on themselves. As they become more popular they tend to extrapolate the negative data points on which they’re built, further and further out the left tail, driving the price lower and further away from probable outcomes.

And like Howard Marks likes to say, there’s no such thing as a good or bad stock just good or bad prices.

A value investor must use their analytical edge to develop a variant perception in order to capitalize off the market’s behavioral overreaction.

Another aspect of behavioral edge is one of timeframe. The market which is becoming increasingly quantitatively focused has gotten very good at predicting earnings 1 to 2 quarters out. But with this short-term quantitative edge, comes the loss of long-term context and so the players in the market have become more and more myopic and short-term focused.

This trend towards market myopia widens the behavioral edge for those willing to peer a little further into the future and play the long game in their investing. This is a kind of time arbitrage that allows a patient investor to capitalize on the market’s broader short-termism.

To turn back to Bill Miller who said this about time arbitrage:

For the market broadly, the recent trends are toward shorter investing time horizons and less active stock selection, which gives us confidence in our competitive advantages of long-term, actively managed investing. The average holding period for mutual funds is now down to just six months, compared to our time horizon of three to five years. We believe that the one constant in the markets is the behaviors of groups of people and the advantages provided by a focus of behavior inefficiencies. The broad features of human behavior have not changed, and social psychologists have mapped pretty well how large numbers of people behave under various conditions. We try to arbitrage between perception and reality in behavior.

Our value investing framework needs to capitalize on our behavioral edge by objectively exploiting market overreactions — letting the fundamentals dictate our actions and not be reactive to short-term price moves —  and arbitraging time by peering further into the future and being more patient with our investments.

And so we have some clear foundational principles on which our framework can be built. We need to:

  • Utilize an analytical edge to arrive at a variant perception.
  • Exploit behavioral driven market overreactions that result in large mispricings.
  • Arbitrage time by playing the long game of peering further into the future and practicing infinite patience.

Moving on…

In our quest to further define what it is we’re looking for we can bucket equity investments into two broad categories:

  1. Macro: These are trades where the primary driver of returns is from macro inputs and not due to individual stock specifics. Cyclical commodity stocks fall under this category where their returns are driven by the capital cycle and the price of the underlying commodity. Market timing and sentiment driven trades also fall under this category.
  2. Fundamental Value:  These trades are primarily driven by the conditions and valuation of the underlying company. Fundamental value trades can be bucketed further into three separate categories.
    1. Classic value: These are the deep value sum of the part investments and the classic Graham net-net plays where the investment thesis rests on the mispricing of the company’s current intrinsic valuation; a valuation which depends less on the company’s future growth and more on the price given to its current assets and earnings stream.  
    2. Special situation: These are Joel Greenblatt style anomalous mispricings caused by spinoffs or a host of other reasons. These aren’t typically long-term plays but are held until the valuation gap caused by an event is closed.
    3. Long-term compounders: These are the real money makers. These are the special stocks that grow in value exponentially over long periods of time. They are run by skilled capital allocators, typically with large amounts of skin in the game, and are companies with wide moats that allow for enduring returns above the cost of capital.

These graphs below from Hayden Capital show the different intrinsic value growth curves and stock price path.

It’s the graph over on the right hand side where we want to focus the majority of our time and which we’re going to discuss today.

Long-term compounders are the stocks that can create generational wealth — if held on to. The problem is that they can be difficult to identify a priori but that’s what we’re going to solve for today.

First, let’s start with a simple math exercise from Scott Miller that shows the incredible power of compounding.

Example:  We underestimate the power of compounding and the impact of difference in return rates over a long period of time.

Question: What is the difference in ending capital between $100K that grows at 10% for 30 years vs. $100K that grows at 20% for 30 years?

Answer: $21M+

10% -> $1,744,940

20% -> $23,373,631

$21M+ dollars is quite a lot from just a 10% difference in annual returns over a long period. George Soros and Stanley Druckenmiller are both worth billions of dollars because they compounded money at an average of 30% return over decades!

This brings us to another foundational principle in markets and the people who play in them:

Humans are inherently bad at understanding the scale of exponential growth and the power of compounding…

We’re linear creatures who think in logarithmic terms. But if we want to harness the 90/10 distribution of market returns and put the power of compounding to work then we need to think in and seek out exponential growth opportunities for our capital.

Investing in a long-term compounder is essentially like allocating your capital to a compounding wizard like Druck or Soros. You can think of these companies almost as the best private equity firms, but ones with access to niche markets and the best information and deal flow available; along with an appropriate incentive structure that creates the opportunity for extraordinary alpha.

William Thorndike’s excellent book The Outsiders is a case study of the 8 best long-term compounders and the operators who ran them. Below are graphs to show the difference in returns over long periods of time that identifying and investing in a long-term compounder, an Outsider stock, can provide.

The differences in return outcomes are extraordinary… they’re exponential…

The market’s inability to properly comprehend and analyze exponential growth is one of our biggest analytical edges. It’s the reason why you have many self proclaimed “value” guys shorting high growth stocks — stocks with super high ROICs — and essentially throwing themselves on the burning pyre as sacrificial lambs because they’re doing linear math in a geometric world *cough Einhorn cough*…

Hopefully, you now get my point about the power of long-term compounding and exponential growth and how finding these stocks can be life changing. Understanding the power of compound growth and factoring that into your value analysis makes for a big analytical edge.

Okay, great! So now how do we find them… What makes one stock a long-term compounder and another just average?

For the answer, click here to sign up for the MIR. The latest issue includes our Macro Ops Long-term Compounders Identification Framework (MOLCIF) that you can use to find exponential growth.

 

 

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The Principle of Bubble Rotation

In the book Business cycles: history, theory and investment reality, the author Lars Tvede talks briefly about a cycle phenomenon he calls The Principle of Bubble Rotation. He writes:

There is one further common aspect of all these asset classes. We have seen that business cycles from time to time create monetary environments that are conductive to asset bubbles. However, people will recall past crashes for a while, and this means that whatever asset people bought in the last bubble will rarely be chosen for the next. This leads to a systematic bubble rotation. There was a bubble in precious metals/diamonds in 1980, for instance, and then in collectibles (and Japanese land) in 1990, and then in equities in 2000.

Essentially, what Lars is saying boils down to, “what outperformed in the last cycle will not outperform in the next.”

Since trading and investing is a game of comparisons, we evaluate all assets on a relative basis and then choose to buy one thing over another. Using The Principle of Bubble Rotation we can underweight assets/sectors/industries that may look attractive at first glance but are unlikely to outperform for the simple reason that they did so in the prior cycle.

Let’s look at the outperformers from the last cycle and see how they’ve done in the current one.

The top performing assets/sectors/industries in the 02’ to 08’ cycle were:

  • Emerging markets
  • Homebuilders
  • Financials
  • Commodities

So far each of these assets/sectors/industries have adhered to The Principle of Bubble Rotation.

The reasons why this cycle skip exists are three fold:

  1. Psychological: Investors who were burned buying into a bubble in the previous cycle are likely to be hesitant to buy into those same assets in the next. We call these “event echoes” where the psychological scarring from a jarring market event affects investor behavior well into the future. This usually takes two cycles to reset because most investing careers don’t last much longer than that.
  2. Capital Cycle: Asset bubbles are born from overoptimism. This optimism attracts capital and competition which leads to large amounts of capital expenditure into future supply. This leads to over-capacity which takes the subsequent cycle to clear.
  3. Regulatory: There’s a regulatory cycle that is always fighting the last war and which typically goes into motion following the bust process where many investors were hurt or financial instability occurred. Take banks following the GFC or cryptos following the current bust process as an example. These regulations typically take the completion of another cycle before deregulation occurs.

The Principle of Bubble Rotation isn’t a hard and fast rule. There’s examples where it didn’t hold true and certain industries are susceptible to their own unique capital cycles which affect the length of their boom/bust process.

Still, it’s a useful heuristic to use for filtering down your universe of potential trades. It would have kept you from buying financials this cycle, which has been a popular but dead money trade. Also, it would have alerted you to areas of the market that were more likely to outperform since they underperformed in the previous cycle; the technology sector being a perfect example.

 

 

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High Quality Trading Is Episodic, Not Continuous

There’s two types of market returns. Alpha and beta. Beta is what you get for diversifying and passively holding the market. Alpha is the opposite. It requires an edge, of which there are three: informational, analytical, and behavioral.

And as Ray Dalio says, “Alpha is zero sum. In order to earn more than the market return, you have to take money from somebody else.”

Harvesting alpha takes significant work because it involves separating someone else from their capital. And that someone else is trying to do the same to you. Most traders and active investors are in the game to produce alpha.

The competition among alpha players is what creates mostly efficient markets.

Once in awhile, Mr. Market throws a tantrum (or gets too excited) and a mispricing occurs. This opens up an opportunity for alpha players to profit. These opportunities often don’t last long. Other alpha players swarm to take advantage the second they detect blood in the water. Once enough catch on the market returns to an efficient state i.e. random forward returns.

Using this mental model of the game we can deduce that high quality trading is episodic, not continuous.

Trying to capture alpha continuously would be like playing every starting hand in Texas Hold’em. Expert poker players know that it’s virtually impossible to win long-term with the bottom 80% of starting hands no matter how good your post-flop play is.

In trading, it’s impossible to harvest alpha every single day. The market is highly competitive and Mr. Market rarely screws up with such high frequency.

Being a trader, you need to learn to patiently sit through long stretches of getting dealt duds. In poker we call this “sitting in Siberia.” This is when you have to sit and fold for hours and hours waiting for cards that have a positive expectation while the rest of the table has fun pushing chips into the middle. Trying to trade during these “Siberia moments” in markets is a profitless endeavor over the long haul.

Continuous trading creates subpar performance because exposure to inefficient market states get mixed in with exposure to efficient market states.

If you take the right side of the market during an inefficient state you will make money long-term. But when you initiate a trade in an efficient market your expected return is 0. And you still have to suffer through the volatility of each trade. It’s a waste of time, resources, and energy. You have to go through all of the work for no reward.

That’s why it’s important to think of trading episodically and not continuously. You don’t want to mix the good with the bad. Structure your trading similar to how a sniper goes about his business on the battlefield — a series of high impact and deadly episodic strikes.

The corollary to “high quality trading is episodic not continuous” is the rarer the market dislocation the greater the edge.

There’s a few reasons for this.

First off, an event that occurs seldomly is less understood than an event that happens frequently.

Uncertainty and confusion in the market is what creates an edge for the alpha players who are able to make sense of things.

Second, the professional quant community ignores rare events as sources of edge — which creates less competition.

Conventional quant techniques look for statistical significance. That means quants need to see lots of historical occurrence to prove that their trading methodology is legit. If there aren’t enough historical occurrences, they will write off the approach as spurious.

The ‘professional’ quant methodology guarantees that they won’t and can’t act on the highest alpha opportunities in the marketplace, leaving the lion’s share to human traders utilizing intuition and experience. Trader intuition and experience is powerful because it enables traders to identify rare alpha opportunities despite a low number of historical occurrences.

So if you’re an independent trader who

  1. Believes that high alpha trading is episodic not continuous
  2. The rarer the dislocation the more alpha

Here’s what you can do to shift your approach to produce better risk adjusted returns.

Start by weed wacking your trade “setups.”

Take the bottom 50% of your trading opportunities and cut them out. Then take the remaining trade setups and cut them by 50% again. This will align you with the philosophy of rare events (the most optimal setups) and make your trading episodic rather than continuous.

Then consider trades that make logical sense to you but don’t have many historical occurrences.

These trades will always have the fattest edge and the least amount of competition because other traders will pass them up.

Finally, expand your playing field as much as possible.

This is in line with our global macro approach at Macro Ops. Because high alpha opportunities are rare, a particular market will only generate a few quality signals a year. That puts a cap on your earning potential. The only way to make more money is to increase your discovery space. That means getting involved with other markets like currencies, rates, grains, meats, softs, volatility, crypto, energy, micro-caps and metals. Hopefully over the course of the year these markets will generate additional rare alpha opportunities that you can capitalize on.

 

 

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