December 29, 2017
By Steve Blumenthal

“I don’t believe that I am the only person who cannot predict future prices.
No one consistently can predict anything, especially investors.  Prices, not investors, predict the future.
Despite this, investors hope or believe that they can predict the future, or someone else can.
A lot of them look to you to predict what the next macroeconomic cycle will be.
We rely on the fact that other investors are convinced that they can predict the future,
and I believe that’s where our profits come from.”

– John W. Henry

“Steve, it’s Mark.”  I struggled to recognize the voice.  “Can you come down to see me?”

“Of course,” I answered.  It had been several years since we last spoke.  My friend was on the winning side of a battle with a bad polyp found on his vocal cords.

When Mark Finn speaks, you listen.  Commanding, sharp, brilliant.  He’s a maverick in the investment business.  He’s also a mentor, incredibly humble and frankly one of the nicest human beings you could meet.  He continued, “I need your help on something.  I’ve got something big and I’m not sure how it should best be packaged.  What I can tell you is I think this ‘son of a gun’ might someday win a Nobel Prize.”  Mark had me at “I need your help.”  Mark is a former chairman of the Commonwealth of Virginia Retirement Plan’s Investment Advisory Committee and current chairman and CEO of Vantage Consulting.  He is the smartest investor I know, so I was really curious about what my friend found that might be Nobel Prize worthy.

A few weeks later, I joined Mark on his golf course in Virginia.  A college standout, he remains a low single-digit player and still ultra-competitive.  A normal four-hour round took us six.  Uncrowded and absent pressure, we talked, hit the ball and talked more.  Mark began, “What if you could own the exact same stocks that make up the S&P 500 Index and improve the return by perhaps 4% per year?”  To better understand where he was taking me, he started first by talking about the benefits of equal-weight over cap-weight.  Simply, think of it as owning the same 500 stocks but with, by rule, equal exposure to each.  Over time, equal-weight has beaten cap-weight by nearly 2% per year.  Better?  Yes.  Not all the time but better over a longer cycle.

Mark next went on to talk about fundamental weighting.  An idea that my friend Rob Arnott pioneered and later lead to a wave of S&P 500 Index-beating product known as “Smart Beta.”  The concept is to own the same exact stocks that make up S&P 500 Index, but in a weighting process that over-weights to the stocks with the strongest fundamentals.  Sounds logical.  This index process beats the cap-weighted S&P 500 by one to two percent per year yet like equal-weight not all the time.  (As a quick aside: If you’ve been exposed to this type of ETF the last number of years, you’ve under-performed.  The process has a “value” bias and value stocks have materially under-performed growth stocks the last five years.)

OK – to make some sense for where Mark was taking me.  It’s important to understand the following:

You and I and most investors have long considered the S&P 500 and the Dow Jones Industrial Average indices to be “the market.”  The Dow Jones Industrial Average (DJIA) is a stock market index created by Wall Street Journal editor Charles Dow on May 26, 1896.  The “Composite Index,” as the S&P 500 was first called when it introduced its first stock index in 1923, began tracking a small number of stocks. Three years later in 1926, the Composite Index expanded to 90 stocks and then, in 1957, it expanded to its current 500.

So over the years, it is what we all view as “the market.”  Over the last 10 years, we’ve witnessed a proliferation of products designed to beat those cap-weighted indices.  And some do.  There is an inherent problem built into the cap-weighted structure that causes an over-concentration of exposures to certain sectors and certain stocks at certain times based on the construction rules of an index put in place many years ago.  Cap-weighting means the stocks with the highest market capitalization (take the number of shares outstanding times the share price) get a higher weighting in the index.  Of course, that is something you well know.

One example of increased risk is the over-exposure to technology in 1999.  It became 39% of your index exposure.  Everyone chasing into tech stocks caused prices to rise causing their market capitalization to grow every larger.  So, in the S&P 500 Index, you got more and more exposure to tech stocks.  Over-weight the most expensive thing at the wrong time.  Tech went on to lose over 75% the next two years.  And it was financials in 2007 and a number of those stocks went on to lose over 80% in The Great Financial Crisis.  Some goose-egged and went out of business, like Lehman Brothers and Bear Stearns.  By structure, you are owing more of the most overvalued stocks.  If you think about it, the cap-weighted structure forces you into the most overvalued sectors at market tops.  I think many investors fail to get this even today.  OK, back to the story.

Mark went on to tell me about his friend Rory Riggs.  Rory co-founded a firm called Royalty Pharma and invented a market for investors to invest in promising drugs and earn a future percentage or royalty payments on drug sales.  His fund earns royalty payments on something like nine out of the top 20 best-selling drugs in the world.  A business leader with a strong scientific research mind.  Great combination.  Smart guy.

Rory thought the cap-weighted structure was too basic and logically made little sense.  Had Wall Street tested for various risk exposures the way drug companies are required to rigorously test new drugs?  As a business person, he believed that stocks should be a steady return premium over both the risk-free rate and the rate that bonds pay investors.  But that wasn’t the case for cap-weighted indices.  Certainly not on a consistent basis.  Logically, if management is driven to produce a certain growth and profit objective.  Shouldn’t that show up in the consistency of equity returns over bonds over time?  So sometime in 2000 or 2001 he began his research.

Since then, and 100% at his own expense, he hired and built of talented team of super-smart kids from some of the country’s best universities.  They began programming the data of every publically traded company into their database.  They’d pull out corporate annual filings and developed a programming language that fingerprinted each company.  Coding one company at a time and reviewing and updating annually.  At the very core, they were looking to code the type of business, who they sell to and exactly how a company makes its money.  Are the corporation’s clients retail individuals, other businesses, governments, conglomerates, what sector do they service, and more and more.  Think of the coding at a super-granular level.  A GPS of global businesses.

Here’s the skinny:

This is a new category of index.  First, the results, if you take the same S&P 500 stocks and you weight them the way Rory’s firm is proposing you weight them, he has found that over long periods of time, decades including all the sub samples, you can get 200 to 400 basis points (2% to 4%) increase in average annualized return.  That’s quite extraordinary.  Rory’s firm, Syntax, finds similar results across industries, across countries and across company sizes (small-cap, mid-cap, large-cap companies).  That’s robust.

When you analyze any index or strategy methodology, one has to always question the source of the data.  Is there data snooping biases?  Meaning, did you fit the data to produce a better result?  Rory spent a decade building a data set and he’s run a live portfolio with a similar pattern of results.  And he didn’t just build it on the S&P 500 stocks, he coded the business models of pretty much every stock globally.  He tested the weighting process not only on the S&P 500 but on international indices, sector indices, and all sorts of indices (names you are likely well familiar with).  The consistent and significant finding is the robustness across all these tests.

Fundamentally, Rory is using information in the real economy.  It looks at how a business makes its money.  The type of business and types of customers.  I believe, the Syntax technology solves a diversification problem by helping investors avoid an overconcentration to related business risks.  Cap-weighting does the opposite.  That’s where the improvement in return comes from over traditional cap-weighted indices.  But I’ve found that Syntax’s weighting methodology also beats equal-weight and fundamental weight.

It won’t protect you against the declines that come in recession.  Like 2000-2002 and 2008-2009, but 200 to 400 bps per year improvement is a great starting place.

In short, Rory created a new business risk classification technology.  It is a massive database of information that took hundreds of thousands of hours and many years to build.  And it took an individual with a curious scientific mindset, business brilliance, deep pockets and gutty determination to turn gut feeling into what my friend Mark believes is Nobel Prize worthy.

The reason I’m sharing this story with you is that I think it is awesome the types of things that are going on today.  This is just one story that may benefit you and me.  A new great tool for us to use.  There remains always reason for great optimism.

There are other tools at our disposal.  Technology provides us access to so many diverse and targeted domestic and global risk exposures.  I believe we all need broad diversification.  And more than ever, I believe we need to have tools that are liquid that provide us with flexibility.

My conversation with Mark was seven years ago.  I’ve been under the hood and have watched Rory’s business with excitement since then.  So much so that when my daughter was about to take a consulting job with EY, I told her to talk with Rory first.  In full disclosure and transparency, you can see that I’m biased, but I do think Mark is right and I do believe that you and I can benefit from Rory’s team’s and Mark’s great work.

What about index performance?  It looks like this:

The index results look like this based in 25 years of calculations done by S&P Dow Jones Indices.  S&P launched the index a few years ago.  Summary: stratified-weight (Syntax’s process) beat cap-weight 75% of the time with an average spread of 400 basis points and equal-weight 85% of the time with an average spread of 200 basis points.

That’s promising.  I’m hoping for an ETF we can trade in 2018.  I’ll keep you posted.

Next week we’ll look at the final year-end valuations.  The market remains extremely overvalued and GMO and others are predicting low-to-negative annualized returns over the coming 7-10 years.  But, as the great John Henry said, “Let prices, not investors, predict the future.”  Keep John Henry front of mind.

In that direction, I am co-hosting a complimentary webinar with Robert Schuster of Ned Davis Research called “How to Invest in a Runaway Stock Market” on Wednesday, January 3 at 2:00 pm (ET).  During this webinar, you will learn:

• How to identify key turning points in bull and bear markets.
• How to avoid emotional decision-making.
• How CMG and NDR use a rules-based strategy designed to participate in bull markets and avoid bear markets.

Click HERE to register.

Grab a coffee and read on.  The balance of this week’s OMR is a quick read.  You’ll find the link to Rory, Jon and Mark’s paper published this month in the Journal of Index Investing along with latest Trade Signals charts.

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