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

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

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

Quant  Vs Discretionary

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

Forecasting

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

Finding Alpha

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

Red Team      

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

 

 

Ed Thorp
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Lessons From A Trading Great: Ed Thorp

Ed Thorp, the father of quant investing, might be the most impressive market wizard. He turned seemingly random processes into predictable events, transforming the art of speculation into a science decades before Wall Street’s quants became mainstream.

His domination in the financial world began in the casino. Thorp figured out how to beat the most “unbeatable” games. In roulette, he created a wearable computer that gave him a 44% edge. And in blackjack, he developed the very first card counting system that’s still widely used today.

These gambling skills transferred perfectly to markets. Thorp’s first hedge fund, Princeton Newport Partners, never had a down year. It compounded money at 19.1% for almost 20 years — destroying the S&P 500.

His second fund, which he ran from August 1992 to September 2002, performed just as well with an annualized return of 18.2%.

Thorp’s list of discoveries, inventions, and people he’s influenced and invested in is comically long:

  • He discovered an options pricing formula before the Black-Scholes model became public.
  • He started the first ever quant hedge fund.
  • He was the first to use convertible and statistical arbitrage.
  • He was the first limited partner in Ken Griffin’s Citadel — one of the most successful hedge funds ever.
  • His books on blackjack and trading heavily influenced “bond king” Bill Gross.
  • He discovered that Bernie Madoff was a fraud many years before it became public.

And the list goes on…

Thorp’s advice on approaching games of incomplete information is methodical and scientific, making it very useful to incorporate into your own trading process. The following is his most valuable wisdom with our commentary attached:

Rare Events (Fat Pitches)

Fat pitches — the types of trades Buffett, Druck, and Soros salivate over— happen seldomly. And that makes sense because is takes extraordinary circumstances to push markets far enough from equilibrium to create these opportunities.

When these dislocations occur, it pays to go on high alert. It’s possible to make your year or even your career in a few days by hitting these fat pitches on the nose.

Here are a few of Thorp’s best plays:

1987 Crash

Black Monday was a traumatizing experience for most traders… but not for Thorp. When the crash started to accelerate Thorp was having his daily lunch date with his wife Vivian. The office called to report the news and Thorp didn’t even flinch. He had already accounted for all possible market scenarios, including this one, and didn’t have any reason to panic. He calmly finished his lunch and then went home to think about how to exploit the situation. This is what he came up with:

After thinking hard about it overnight I concluded that massive feedback selling by the portfolio insurers was the likely cause of Monday’s price collapse. The next morning S&P futures were trading at 185 to 190 and the corresponding price to buy the S&P itself was 220. This price difference of 30 to 35 was previously unheard of, since arbitrageurs like us generally kept the two prices within a point or two of each other. But the institutions had sold massive amounts of futures, and the index itself didn’t fall nearly as far because the terrified arbitrageurs wouldn’t exploit the spread. Normally when futures were trading far enough below the index itself, the arbitrageurs sold short a basket of stocks that closely tracked the index and bought an offsetting position in the cheaper index futures. When the price of the futures and that of the basket of underlying stocks converged, as they do later when the futures contracts settle, the arbitrageur closes out the hedge and captures the original spread as a profit. But on Tuesday, October 20, 1987, many stocks were difficult or impossible to sell short. That was because of the uptick rule.

It specified that, with certain exceptions, short-sale transactions are allowed only at a price higher than the last previous different price (an “uptick”). This rule was supposed to prevent short sellers from deliberately driving down the price of a stock. Seeing an enormous profit potential from capturing the unprecedented spread between the futures and the index, I wanted to sell stocks short and buy index futures to capture the excess spread. The index was selling at 15 percent, or 30 points, over the futures. The potential profit in an arbitrage was 15 percent in a few days. But with prices collapsing, upticks were scarce. What to do?

I figured out a solution. I called our head trader, who as a minor general partner was highly compensated from his share of our fees, and gave him this order: Buy $5 million worth of index futures at whatever the current market price happened to be (about 190), and place orders to sell short at the market, with the index then trading at about 220, not $5 million worth of assorted stocks—which was the optimal amount to best hedge the futures—but $10 million. I chose twice as much stock as I wanted, guessing only about half would actually be shorted because of the scarcity of the required upticks, thus giving me the proper hedge. If substantially more or less stock was sold short, the hedge would not be as good but the 15 percent profit cushion gave us a wide band of protection against loss.

In the end we did get roughly half our shorts off for a near-optimal hedge. We had about $9 million worth of futures long and $10 million worth of stock short, locking in $1 million profit. If my trader hadn’t wasted so much of the market day refusing to act, we could have done several more rounds and reaped additional millions.

Kovner Oil Tanker

In the 1980s, Bruce Kovner discovered a trading opportunity in buying oil tankers for scrap value. Thorp instantly recognized the fat pitch and invested.

Along with Jerry Baesel, the finance professor from UCI who joined me at PNP, I spent an afternoon with Bruce in the 1980s in his Manhattan apartment discussing how he thought and how he got his edge in the markets. Kovner was and is a generalist, who sees connections before others do.

About this time he realized large oil tankers were in such oversupply that the older ones were selling for little more than scrap value. Kovner formed a partnership to buy one. I was one of the limited partners. Here was an interesting option. We were largely protected against loss because we could always sell the tanker for scrap, recovering most of our investment; but we had a substantial upside: Historically, the demand for tankers had fluctuated widely and so had their price. Within a few years, our refurbished 475,000-ton monster, the Empress Des Mers, was profitably plying the world’s sea-lanes stuffed with oil. I liked to think of my part ownership as a twenty-foot section just forward of the bridge. Later the partnership negotiated to purchase what was then the largest ship ever built, the 650,000-ton Seawise Giant. Unfortunately for the sellers, while we were in escrow their ship unwisely ventured near Kharg Island in the Persian Gulf, where it was bombed by Iraqi aircraft, caught fire, and sank. The Empress Des Mers operated profitably into the twenty-first century, when the saga finally ended. Having generated a return on investment of 30 percent annualized, she was sold for scrap in 2004, fetching almost $23 million, far more than her purchase price of $6 million.

Sometimes the best trades aren’t on public exchanges. Looking outside traditional trading vehicles can reveal huge opportunities other traders pass up.

SPACs

An unusual opportunity to buy assets at a discount arose during the financial crash of 2008–09, in the form of certain closed-end funds called SPACs. These “special purpose acquisition corporations” were marketed during the preceding boom in private equity investing. Escrowing the proceeds from the initial public offering (IPO) of the SPAC, the managers promised to invest in a specified type of start-up company. SPACs had a dismal record by the time of the crash, their investments in actual companies losing, on average, 78 percent. When formed, a typical SPAC agreed to invest the money within two years, with investors having the choice—prior to the SPAC buying into companies—of getting back their money plus interest instead of participating.

By December 2008, panic had driven even those SPACs that still owned only US Treasuries to a discount to NAV. These SPACs had from two years to just a few remaining months either to invest or to liquidate and, before investing, offer investors a chance to cash out at NAV. In some cases we could even buy SPACs holding US Treasuries at annualized rates of return to us of 10 to 12 percent, cashing out in a few months. This was at a time when short-term rates on US Treasuries had fallen to approximately zero!

Runaway Inflation

Short-term US Treasury bill returns went into double-digit territory, yielding almost 15 percent in 1981. The interest on fixed-rate home mortgages peaked at more than 18 percent per year. Inflation was not far behind. These unprecedented price moves gave us new ways to profit. One of these was in the gold futures markets.

At one point, gold, for delivery two months in the future, was trading at $400 an ounce and gold futures fourteen months out were trading for $500 an ounce. Our trade was to buy the gold at $400 and sell it at $500. If, in two months, the gold we paid $400 for was delivered to us, we could store it for a nominal cost for a year, then deliver it for $500, gaining 25 percent in twelve months.

Notice the commonalities between Thorp’s fat pitches:

  • They’re rare and typically occur during crises. Crises create large dislocations that cause investors to act irrationally, creating huge opportunities.
  • They all have asymmetric risk/reward ratios.
  • Fast action was needed to capture each of them. Fat pitches don’t last long. Other traders will eventually find them and pounce.
  • They’re all “one of a kind” opportunities. The exact scenario had never happened before and creative thinking was needed to capitalize. Although history rhymes, it does not repeat. The next fat pitch won’t be exactly like the last one.  

Gambling As Training

Understanding gambling games like blackjack and some of the others is one of the best possible training grounds for getting into the investment world. You learn how to manage money, you learn how to compute odds, you learn how to reason what to do when you have an advantage.

Gambling is investing simplified. Both can be analyzed using mathematics, statistics, and computers. Each requires money management, choosing the proper balance between risk and return. Betting too much, even though each individual bet is in your favor, can be ruinous.

Notice how Thorp didn’t say anything about MBAs, economic degrees, or finance classes. Those don’t prepare you for the core challenges you’ll face as a trader like position sizing and risk management.

Our favorite cross-training exercise at Macro Ops is poker. Poker forces you to think in terms of probabilistic outcomes while managing your risk and establishing an edge.

Edge

You can’t succeed in trading without an edge. And a good way to find that edge is by asking yourself how the market is inefficient and how you can exploit it.

In A Man For All Markets Thorp details sources of inefficiency:

In our odyssey through the real world of investing, we have seen an inefficient market that some of us can beat where:

  1. Some information is instantly available to the minority that happen to be listening at the right time and place. Much information starts out known only to a limited number of people, then spreads to a wider group in stages. This spreading could take from minutes to months, depending on the situation. The first people to act on the information capture the gains. The others get nothing or lose. (Note: The use of early information by insiders can be either legal or illegal, depending on the type of information, how it is obtained, and how it’s used.)
  2. Each of us is financially rational only in a limited way. We vary from those who are almost totally irrational to some who strive to be financially rational in nearly all their actions. In real markets the rationality of the participants is limited.
  3. Participants typically have only some of the relevant information for determining the fair price of a security. For each situation, both the time to process the information and the ability to analyze it generally vary widely.
  4. The buy and sell orders that come in response to an item of information sometimes arrive in a flood within a few seconds, causing the price to gap or nearly gap to a new level. More often, however, the reaction to news is spread out over minutes, hours, days, or months, as the academic literature documents.

He then explains how to exploit these inefficiencies (emphasis mine):

  1. Get good information early. How do you know if your information is good enough or early enough? If you are not sure, then it probably isn’t.
  2. Be a disciplined rational investor. Follow logic and analysis rather than sales pitches, whims, or emotion. Assume you may have an edge only when you can make a rational affirmative case that withstands your attempts to tear it down. Don’t gamble unless you are highly confident you have the edge. As Buffett says, “Only swing at the fat pitches.”
  3. Find a superior method of analysis. Ones that you have seen pay off for me include statistical arbitrage, convertible hedging, the Black-Scholes formula, and card counting at blackjack. Other winning strategies include superior security analysis by the gifted few and the methods of the better hedge funds.
  4. When securities are known to be mispriced and people take advantage of this, their trading tends to eliminate the mispricing. This means the earliest traders gain the most and their continued trading tends to reduce or eliminate the mispricing. When you have identified an opportunity, invest ahead of the crowd.

Pay special attention to his second point: Don’t trade unless you’re sure you have an edge that’ll create better than random outcomes.

An easy way to do this is by backtesting or paper trading your strategy before investing in it.

It’s also a good idea to try finding a solid trading edge in markets you love. As Thorp explains:  

To beat the market, focus on investments well within your knowledge and ability to evaluate, your “circle of competence.”

If you love following small companies then just trade those. If you come from an energy background then focus on crude oil and natural gas. And if you like math and volatility, options are a good place to start. Only venture into a new market after spending a significant amount of time studying it!

Efficient Markets

Anyone who’s actually traded knows the Efficient Market Hypothesis is bogus. It’s a poor mental model used by lazy academics. Thorp has a much better take:

When people talk about efficient markets they think it’s a property of the market. But I think that’s not the way to look at it. The market is a process that goes on. And we have, depending on who we are, different degrees of knowledge about different parts of that process.

. . . market inefficiency depends on the observer’s knowledge. Most market participants have no demonstrable advantage. For them, just as the cards in blackjack or the numbers at roulette seem to appear at random, the market appears to be completely efficient.

Markets aren’t actually random. They only appear random to those without expertise. The right knowledge transforms the market from a sequence of random numbers into a predictable process.

Combining Technicals With Fundamentals

In the mid 2000s Thorp developed a trend following futures strategy. During the process he discovered that combining fundamental information with technical signals was superior to just technicals alone.

Here he is in Hedge Fund Market Wizards:  

The fundamental factors we took into account varied with the market sector. In metal and agricultural markets, the spread structure—whether a market is in backwardation or contango—can be important, as can the amount in storage relative to storage capacity. In markets like currencies, however, those types of factors are irrelevant.

Combining technicals with fundamentals can boost your win rate. Find the key fundamental drivers in your market and add them into your process.

Anchoring

In A Man For All Markets Thorp describes his first ever trade buying a company called Electric Autolite. In the subsequent two years the stock declined 50%. He decided to hold out, hoping it would return to his entry point so he could break even. The stock eventually did rebound and Thorp got out for a scratch, but he later realized how stupid that was. Here’s Thorp reflecting on it:

What I had done was focus on a price that was of unique historical significance to me, only me, namely, my purchase price.

Thorp’s early mistake is called anchoring. Humans tend to place special significance on price levels they originally entered at. But in reality, these prices have little significance. Don’t ever emotionally attach yourself to any price.

Interpreting Financial Headlines

It’s important to take news headlines with a grain of salt. Journalists build narratives behind every market move because it’s their job. Thorp warns about getting caught up in the noise:

Routine financial reporting also fools investors. “Stocks Slump on Earnings Concern” cried a New York Times Business Day headline. The article continued, “Stock prices fell as investors continued to be concerned about third-quarter results.” A slump? Let’s see. “The Dow Jones Industrial Average (DJIA) declined 2.96 points, to 10,628.36.” That’s 0.03 percent, compared with a typical daily change of about 1 percent. Based on the historical behavior of changes in the DJIA, a percentage change greater than this happens more than 97 percent of the time. The Dow is likely to be this close to even on fewer than eight days a year, hardly evidence of investor concern.

One way to separate signal from noise is to track the market’s expected move for the day. To calculate the expected percentage move of the S&P 500, take the VIX and divide it by the the square root of 252. If price stays within that band, any “news” for the day is likely not worth paying attention to.

Correlation

All the trading greats stress the importance of correlation. A low correlation among positions diversifies the portfolio and creates a much better risk/reward profile.

Here’s Thorp from HFMW:

We tracked a correlation matrix that was used to reduce exposures in correlated markets. If two markets were highly correlated, and the technical system went long one and short the other, that was great. But if it wanted to go long both or short both, we would take a smaller position in each.

A common problem traders face when monitoring correlation is the lookback period. Thorp found that 60 days was best. A shorter window is too noisy and a longer one will produce correlations that aren’t relevant anymore.

The Moore Research Center has a free to use correlation matrix for all major macro markets. Check it here.

Leverage

Use leverage incorrectly and you’ll blow up. But properly harness it and you can engineer a risk to reward ratio that perfectly suits you.

Heres Thorp:

The lesson of leverage is this: Assume that the worst imaginable outcome will occur and ask whether you can tolerate it. If the answer is no, then reduce your borrowing.

Don’t rely on a risk control model that uses probability to estimate your max loss. Always assume the absolute worst case and manage from there.

Position Sizing

Thorp popularized the position sizing formula called the Kelly Criterion. Here he is from Hedge Fund Market Wizards:

The Kelly criterion is the bet size that will produce the greatest expected growth rate in the long term. If you can calculate the probability of winning on each bet or trade and the ratio of the average win to average loss, then the Kelly criterion will give you the optimal fraction to bet so that your long-term growth rate is maximized.

Here’s the version of the formula that works best for trading:

So for example, if a trade has a 1:1 reward to risk ratio, with a 60% chance of winning, you would bet:

((1)(.6)-(.4))/1 = .2 or 20% of your account

The one issue with Kelly sizing is that we’ll never know our true win rate or reward to risk ratio in markets. The best we can do is estimate.

Also, the effectiveness of a trading edge changes over time. Because markets evolve, the same edge won’t work forever.

This is why Thorp only uses the Kelly number as a reference. In practice it’s better to bet around half-Kelly because you get about three-quarters of the return with half the volatility.

If you’re less certain of your edge, you should bet an even smaller amount. When Thorp was working on his trend following model in the mid 2000s, he was simulating 1/10 of the Kelly number.

Thorp also has advice on drawdowns. He suggests lowering your position size during rough patches and then ramping up again as you come out of them.

If we lost 5 percent, we would shrink our positions. If we lost another few percent, we would shrink our positions more. The program would therefore gradually shut itself down, as we got deeper in the hole, and then it had to earn its way out. We would wait for a threshold point between a 5 percent and 10 percent drawdown before beginning to reduce our positions, and then we would incrementally reduce our position with each additional 1 percent drawdown.

This is an extremely robust risk management technique used by almost all the trading greats. To read more about them, download our special report by clicking here.

 

 

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

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

Enjoy…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Experience is another over-rated thing.

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

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

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

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

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

Trait #1

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

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

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

Trait #2

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

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

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

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

Trait #3

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

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

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

Trait #4

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

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

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

Trait #5

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

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

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

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

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

Trait #6

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

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

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

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

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

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

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

Trait #7

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

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

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

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

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

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

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

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

The seven traits above can’t be.

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

Do you possess these 7 traits?

 

 

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

 

 

Best Global Macro Podcasts
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The Best Trading Podcasts For Global Macro Investors

These days we’re flooded with information with barely enough time to digest it all.

To help solve this, our team at Macro Ops tunes into trading podcasts.

With trading podcasts, all those hours you spend driving, doing chores, or working out can also be used to catch up on markets and learn something new.

Below you’ll find a list of the best trading podcasts we listen to on a monthly basis to keep our game sharp. We’ve separated them into two categories — process based and news/commentary/narrative based.

The process based podcasts help us develop our macro trading strategies while the commentary helps round out our global macro research. We find both very useful.   

Best Process Based Trading Podcasts

#1 Chat With Traders     

Chat With Traders

Chat With Traders is the leading process podcast for anyone interested in markets. In each episode, host Aaron Fifield invites a guest trader onto the show and breaks down their strategies. The focus is always on their mental models and trading framework — not their current trade ideas.

One of the best ways to improve your own trading is to listen to how others approach markets. You can take their experience and advice and apply it to your own unique style.

The show’s guest list is diverse and ranges from HFT all the way to global macro traders. You’ll get as taste of everything.

Recommended Episode

Chat With Traders, EP. 79 featuring Raoul Pal

I don’t know any of the better known traders who didn’t start with a chart. Paul Tudor Jones always a chart, Stan Druckenmiller always a chart, George Soros always a chart. Many of these guys, most of these guys are all chart based. – Raoul Pal

Raoul cut his teeth advising the biggest macro names in the industry including Paul Tudor Jones, Stan Druckenmiller, and George Soros. He then used his knowledge and experience to run a macro fund before retiring at the ripe age of 36.

Aaron does a great job digging into Raoul’s trading process to figure out how he plays the global macro chess game.

Key areas discussed include:

  • The definition of global macro trading
  • Combining technicals and fundamentals to create better trade signals
  • How to quantify the business cycle using the ISM number
  • How to trade around a position as a macro theme plays out

This is a must-listen episode to see how a veteran global macro investor thinks.

 

#2 Masters In Business

Masters In Business

Masters In Business is a podcast hosted by Barry Ritholtz — a wealth manager and popular financial blogger. Barry has deep domain expertise that makes him an incredible interviewer. He uses his experience to ask the right questions and pull as much as possible out of his guests.

Recommended Episode

Masters In Business featuring Ed Thorp

When people talk about efficient markets they think it’s a property of the market. But I think that’s not the way to look at it. The market is a process that goes on. And we have, depending on who we are, different degrees of knowledge about different parts of that process. – Ed Thorp

Ed Thorp has proved the Efficient Market Hypothesis incorrect over and over again throughout his career. He’s shown that it’s possible to win if you understand the concepts of edge and probability. Thorp does a fantastic job breaking these concepts down into digestible bite-sized pieces.

Key areas discussed include:

If you’re looking to beef up your “quant chops”, listen to this now.

 

#3 Invest Like The Best       

Invest Like The Best

Invest Like The Best is hosted by Patrick O’Shaughnessy — a portfolio manager at O’Shaughnessy Asset Management and blogger at http://investorfieldguide.com/. His show focuses on longer-term investing rather than shorter-term trading. Patrick invites guests with a variety of investing backgrounds in areas like cryptocurrencies, venture capital, and equities. The best episodes are equity related as Patrick always brings on the sharpest guys in the world of stock picking. If you’re into analyzing specific companies, this show is a must.

Recommended Episode

Invest Like the Best, EP.54 featuring David Gardner

Many of the really important things that determine what wins in business are not captured on the financial statements. – David Gardner

This episode does a great job explaining investment narratives. Successful investing, especially in global macro, isn’t always about the numbers. Narratives and investor expectations often drive the macro landscape more than the data itself.

David Gardner, co-founder of the Motley Fool, talks with Patrick about how he invests using compelling narratives instead of traditional valuation metrics.

Key areas discussed include:

  • Narratives over numbers
  • Wisdom of crowds
  • How visionary leadership is key
  • How “overvalued” can actually be a buy signal

This conversation ties in well with Soros’ theory of reflexivity. Positive beliefs about a company can influence its underlying fundamentals to create a positive feedback loop that sends prices higher and higher. Naive traders that short these positive feedback loops end up paying the price.

 

#4 Better System Trader       

Better System Trader

Better System Trader is a show primarily focused on short to mid-term systematic trading. If you’re a discretionary trader looking to get into systems, this podcast provides a great look into that world. The show is also good for more experienced system traders as Andrew Swanscott does a fine job balancing system building basics with more advanced topics.

Recommended Episode

Better System Trader, Episode 59 featuring Scott Phillips

The first fix [for drawdowns] is to bank partial profits along the way at a point that doesn’t sacrifice your total return too much but reduces the standard deviation of your winners. – Scott Phillips

Scott Phillips has an incredible trading journey that began as a recovering meth addict in prison. While inside, Scott read every trading book he could get his hands on. He even had people send him high, low, open, and close data that he would record by hand in a scrap book! Scott is a high energy, no-bullshit guy that delivers brutal truths about the difficulties of system building.

Key areas discussed include:

I really enjoyed this episode because of Scott’s directness. There’s no fluff at all in this conversation. Every minute is well worth your time.

 

#5 Futures Radio Show 

Futures Radio Show

Futures Radio Show is hosted by Anthony Crudele. Anthony got his start on the floor of the CME back in 1999. He’s traded millions of futures contracts over his lifetime and was also one of the first to trade the E-mini S&P electronic contract. Since Anthony’s show revolves around futures, it has a macro fundamental bias. You’ll find some newsy segments here and there but the majority of the content focuses on trading process.

Recommended Episode

Futures Radio Show, Minisode 16: Kevin Muir

All those gaps add up, and they actually cause the futures chart, when you are looking at the continuous chart over a longer period, to not be indicative of what the asset return is during that timeframe. – Kevin Muir

Anthony Crudele invites Kevin Muir aka “The Macro Tourist” on the show to talk process and current events. Kevin is a veteran macro trader who now trades for himself. He specializes in derivative products like futures and options. Since he’s no longer in the institutional game, all his advice is very applicable to the retail trader.

Key areas discussed include:

  • Thinking about carry in the futures markets
  • Adjusting futures charts to see the carry
  • How to think about implied volatility vs realized volatility when trading options

I thought Kevin did a great job breaking down these complex concepts. You’ll learn a whole lot about the derivative markets in just a half hour.

 

#6 The Tim Ferriss Show        

The Tim Ferriss Show

This is the one podcast out of the entire list that isn’t finance focused. It’s on here because Tim specializes in process building across all disciplines. Successful trading and investing is a lifelong journey that requires optimization in every area — health, fitness, business, etc. Tim talks to top performers across a variety fields to help you build great systems and habits into your life.

Recommended Episode

The Tim Ferriss Show, Episode 264: Ray Dalio, The Steve Jobs of Investing

Every buyer has behavioral characteristics for certain reasons, let’s say stocks, to use a very simple example, a typical individual, maybe mutual fund buyer, will buy after something’s gone up because they think it’s a better investment. They’ll sell when it goes down because they’ll get scared, and they think it’s a worse investment. Whereas, a typical institutional investor or pension fund, will buy when it goes down because they have to rebalance their portfolio to keep an asset allocation mix at a certain level.     – Ray Dalio

Ray Dalio is one of the preeminent macro traders of the modern era with the track record to prove it. His hedge fund, Bridgewater Associates, is the most profitable hedge fund of all time — making billions upon billions of dollars for its investors.

Any media Dalio does is worth a listen. The man has so much wisdom and insight, he could go on for hours and hours. We only get two here, but it’s a solid two hours. Tim is an excellent interviewer who knows how to pull the maximum amount from his guests.

Key areas discussed include:

There’s no way to master global macro without deeply understanding the concepts Dalio discusses. Listen to this once, and if it doesn’t quite click, keep listening again and again. The wisdom here is timeless.

 

Best News/Commentary/Narrative Based Trading Podcasts

#1 Financial Sense Newshour      

Financial Sense Newshour

Financial Sense Newshour is a podcast available as a paid subscription through the FS Insider program. In it you’ll find market strategists with various backgrounds sharing their views. This show is a useful tool to stress test your own market theses. Everyone suffers from confirmation bias and actively seeking out opinions that differ from your own helps “red team” your analysis.

 

#2 Adventures In Finance       

Adventures In Finance

Adventures In Finance has the highest production value of any financial podcast on the net. The hosts always put on an entertaining show chock full of interesting nuggets. The content of each episode varies greatly, but for the most part it focuses on bringing you the most compelling global macro narratives. They also have a brilliant segment titled “Things I Got Wrong”, where a money manager explains mistakes they’ve made and what they’ve learned from them.

Recommended Episode

Adventures In Finance Episode: 22 – Murderer, Gambler, Aristocrat & Pauper. John Law, The Godfather of Central Banking

In this highly entertaining episode, the team explores the fascinating story of John Law, a man whose economic ideas have influenced and shaped modern central banking. Law’s financial shenanigans created the “Mississippi Bubble”, a spectacular boom and bust process that played out in early 18th-century France. Listening to history is a great way to educate yourself on the perils of modern monetary policy as well as the timeless fear and greed that dominates our markets.

 

#3 Macro Voices        

Macro Voices

Macro Voices is a weekly podcast reviewing the largest global macro themes driving equities, rates, currencies, and commodities. The host, Erik Townsend, is a tech entrepreneur turned global macro fanatic who loves discussing the big picture. Erik’s show has a different flavor than your typical market commentary because he actively trades through a small hedge fund. He has skin in the game and cares about getting things right.

If you want to hear more about Erik, Chat With Traders did a feature on him that you can listen to here.

 

#4 McAlvany Weekly Commentary       

McAlvany Weekly Commentary

David McAlvany hosts this valuable global macro show where he discusses all the latest political and economic news that matters to financial markets. There’s a strong bias against central banks and for gold, but if you can get past that, there’s plenty of interesting macro themes to chew on. This show is best used to help with trade idea generation, but there are also various intelligent guests that come by and talk broader macro theory.

 

That concludes the list of podcasts we listen to. If you have any recommendations that we missed, just leave them in the comments below…  

 

If you’re interested in a thorough global macro book list, be sure to check out our rundown of the best global macro books here.

And if you’d like to see how our team trades, check out the Macro Ops Handbook here.

 

 

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

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

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

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

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

My take:

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

This is no easy task.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thanks for reading,

Alex

 

 

 

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Samurai, Livermore, and Market Timeframes (Daily Speculations)

Daily Speculations are just a post 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 one of one of the weekly market Briefs I write each week for members of the Collective. I wrote this one last year some time. It’s about Samurais, Livermore and market timing, obviously. 

Enjoy.

There is timing in the whole life of the warrior, in his thriving and declining, in his harmony and discord. Similarly, there is timing in the Way of the merchant, in the rise and fall of capital. All things entail rising and falling timing. You must be able to discern this. In strategy, there are various timing considerations. From the outset you must know the applicable timing and the inapplicable timing, and from among the large and small things and the fast and slow timings find the relevant timing, first seeing the distance timing and the background timing. This is the main thing in strategy. It is especially important to know the background timing, otherwise your strategy will become uncertain. ~ Miyamoto Musashi

Miyamoto Musashi was a renowned Samurai and Ronin (meaning he served no master) in Japan during the early 17th century.

He had a unique style of fighting with double blades which was apparently quite effective; he racked up an undefeated record of 60 duels. In his later years, he wrote The Book of Five Rings — a work similar to Sun Tzu’s Art of War that covers strategy, tactics, and philosophy through the scope of sword fighting, but that’s also applicable to life in general.

In Five Rings, Musashi talks about the importance of timing and maintaining one’s awareness of the ebb and flow inherent in life and war.

Timing, as we know, is as critical an input to a deadly Ronin Samurai as it is to a Master Trader. Musashi’s wisdom on the differences of time and the warrior’s manipulation of such is a subject that every trader needs to understand and practice at the deepest levels.

I’ve been increasingly thinking about Musashi and timing because of the macro environment we now find ourselves in. It’s in this type of environment where understanding the difference of applicable and inapplicable, as well as, background and distance timing, becomes ever more crucial. Let me explain.

As traders operating in markets, we’re fighting in multiple timeframes. In these different timeframes, there are varying levels of signal to noise. We’re left to discern what timing is applicable to our trade objective and what’s not. We need to learn from experience which timeframes we can have relevant conviction in and which we simply can’t.

For instance, one cannot simply be bullish on stock XYZ. That is a meaningless statement. Are you bullish on the stock over the short term (ie, next 4-8 weeks) because of factors a, b, and c? Or do you really think the company is undervalued and price should rise at some point over the next couple of years, but you have no conviction on where it will go in the near term?

Discerning your trade objective and reasoning is vital to understanding what your “relevant timing” is. Meaning, you understand the relevant time and the factors applicable to your thesis — every timeframe has its own unique drivers.

Musashi talks about the importance of “first seeing the distance timing and background timing.”

The trading equivalent to these is price action and macro. Price action (distance timing) signals where things stand now and where they’re possibly headed in the near term (1-3 months). Macro (background timing) tells us where the larger forces and imbalances are. It provides us with a gauge of where the risks and opportunities lie in the future. Both sections of time affect one another, so the ebb and flow of the battlefield (market) is constantly evolving.

But, as Musashi says, it is “especially important to know the background timing” because that is the more powerful force that will eventually bend and dominate the near-term. It’s when these different time frames lineup that you get the most powerful and profitable trends.

Livermore (the Musashi of markets) understood the importance of timing in speculation better than anybody and talked about this lesson repeatedly in Reminiscences of a Stock Operator:

“The way to make money is to make it. The way to make big money is to be right at exactly the right time. In this business a man has to think of both theory and practice. A speculator must not be merely a student, he must be both a student and a speculator.”

“There is a time for all things, but I didn’t know it. And that is precisely what beats so many men in Wall Street who are very far from being in the main sucker class. There is the plain fool, who does the wrong thing at all times everywhere, but there is the Wall Street fool, who thinks he must trade all the time. “

“Obviously the thing to do was to be bullish in a bull market and bearish in a bear market. Sounds silly, doesn’t it? But I had to grasp that general principle firmly before I saw that to put it into practice really meant to anticipate probabilities. It took me a long time to learn to trade on those lines. “

“I think it was a long step forward in my trading education when I realized at last that when old Mr. Partridge kept on telling the other customers, ‘Well, you know this is a bull market!’ he really meant to tell them that the big money was not in the individual fluctuations but in the main movements — that is, not in reading the tape but in sizing up the entire market and its trend.”

“…the point is not so much to buy as cheap as possible or go short at the top prices, but to buy or sell at the right time.”

“I had made a mistake. But where? I was bearish in a bear market. That was wise. I had sold stocks short. That was proper. I had sold them too soon. That was costly. My position was right but my play was wrong.”

“That is what happened. I didn’t wait to determine whether or not the time was right for plunging on the bear side. On the one occasion when I should have invoked the aid of my tape-reading I didn’t do it. That is how I came to learn that even when one is properly bearish at the very beginning of a bear market it is well not to begin selling in bulk until there is no danger of the engine back-firing.”

The key to dealing with the various temporal frames (that’s a fancy way of saying lengths of time) is just to remain aware of your analysis and expectations and how they align with the march of time.

Don’t get tunnel vision and try to fit a long-term macro view into a short-term trade expectation.

Be flexible and know which timeframe you’re operating in and in which your edge lies.

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

 

 

 

The Label Stupidity Loop
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The Label Stupidity Loop

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

Humans love adopting labels.

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

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

Labels fix this.

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

Labels also give us a tribe.

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

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

So adopting labels is great.

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

The only thing is… labels make us stupid.

Really, really stupid.

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

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

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

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

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

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

Psychologists call this, “Identity Protective Cognition”.

Vox described IPC as:

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

Why does this matter?

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

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

Take politics for example.

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

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

Most of us passively adopted these labels.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Do you identify with your market approach?

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

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

So why would you care what label it falls under?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What labels do you wear?

What type of belief cage have you built for yourself?

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

 

 

Liquidity, The NFCI, And Leverage
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Measuring Market Liquidity: The NFCI

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

If you want to trade macro, you need to understand liquidity.

PTJ, Druck, Soros, Dalio — all these legends have expressed this fact multiple times.

Liquidity is what moves markets.

This is even more true now than in the macro heydays of the 70s and 80s.

With the rise of “blind investing” in the form of passively buying and holding ETFs, the majority of investors don’t care about valuation or merit. They just auto-shuttle their excess funds to the nearest robo advisor without a second thought.

This amount of “excess funds” is largely dependent on liquidity conditions.

When liquidity is loose, it’s cheap to get levered. People have extra cash and plow it into risk assets. Prices rise.

When liquidity is tight, people have less cash to spend. They may even sell stuff to service their existing debt. Prices fall.

There are a myriad of ways to measure and monitor liquidity conditions.

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

No single method is best, but one of our favorites is using the Chicago Fed’s National Financial Conditions Index (NFCI).

This index combines over 105 different indicators of financial activity to form one easy-to-read liquidity measurement. Money markets, debt markets, equity markets, traditional banking systems, “shadow” banking systems — they’re all included.

The zero line represents average liquidity conditions. Positive values indicate tighter-than-average conditions and negative values indicate looser-than-average conditions.

The Chicago Fed also publishes the Adjusted National Financial Conditions Index (ANFCI).

Since financial liquidity conditions are highly correlated to economic conditions, this index isolates the uncorrelated component. It tells us what liquidity conditions are like relative to economic conditions.

Positive values indicate liquidity conditions are tighter than would be suggested by current economic conditions, while negative values indicate the opposite.

You can see the difference between the standard and adjusted index in the graph below.

We prefer the ANFCI because it isolates liquidity conditions better than the NFCI.

The NFCI doesn’t always tell you when liquidity is deteriorating. In the late 90’s and 2014/2015, liquidity conditions were worsening but the strong stock market and strong economy kept the NFCI below 0, signaling liquidity was loose.

In contrast, the ANFCI was above 0 during the same period, signaling conditions were actually tightening.

The ANFCI is a little noisy to look at, but if you smooth the data with a 12-month MA, you get a nice picture of liquidity conditions in the U.S.

The cyclical nature of our economy becomes clear and it’s easy to see how liquidity predicts business cycles. You can use this tool to help you trade on the right side of the market.

When liquidity is tightening, take bearish trades. When liquidity is loosening, take bullish trades.

This index is also broken down further into 3 sub indices — risk, credit, and leverage.

Risk is a coincident indicator, credit is a lagging indicator, and leverage is a leading indicator of financial stress.

For trading purposes, the leverage part of the equation matters the most to see where the stock market is headed.

Above average leverage sows the seeds for a recession and a falling stock market. Below average leverage precedes economic booms and stock market rallies.

Ray Dalio discovered this logic long before the Chicago Fed and has made billions trading off it.

The leverage index can be broken down yet again to only include nonfinancial leverage.

Nonfinancial leverage is one of the most powerful leading indicators of stock market performance.

Liquidity, The NFCI, And Leverage

This graph might look familiar to you because it’s basically the short-term debt cycle, which can help you time markets.

For example, debt was at obscene levels before 2008 and signaled a shorting opportunity. And by 2010 debt was back below average and signaled a buying opportunity.

People are always the most levered at a market top and the least levered at a bottom.

A skilled macro trader wants to do the opposite. Paying attention to nonfinancial leverage will help you do that.  

Lever up when others are unlevered and delever when others are highly levered.

Despite all the financial doom and gloom we’re drowned with nowadays, nonfinancial leverage readings tell a different story.

Current levels are only average.

Before making your next trade, take a look at these indicators.

How’s liquidity? Where are we at in the debt cycle?

Knowing these answers will make you a lot more confident in your trading. It’s hard to get blind sided by a big crash or miss out on a huge rally when you have a handle on liquidity.

Summary

  • Liquidity is a key variable in determining the macro landscape
  • We can monitor liquidity using the ANFCI
    • If the ANFCI is trending higher, liquidity is tightening and we want to lean bearish
    • If the ANFCI is trending lower, liquidity is loosening and we want to lean bullish
  • The nonfinancial leverage component of the NFCI tells us where we are in the debt cycle
  • We want to buy risk assets at the bottom of the debt cycle (below average leverage) and sell risk assets at the top of the debt cycle (above average leverage)

 

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