Passive Investing: Passive Aggressive

Published on 02/12/19 | Saurav Sen & Catherine McNey | 5,577 Words

The BuyGist:

  • After the blazingly fast crash of Q4 2018, we were curious whether markets are functioning “properly".
  • One phenomenon that’s been gaining traction (to put it mildly) in the equity markets is the Passive Investing (Indexing) boom. 
  • We suspected that this phenomenon is changing the way equity markets function. 
  • We broke down our hypothesis into 6 measurable statistics: 
    • Is there a tailwind effect?
    • Is there a Persistence effect?
    • Is Beta playing a bigger role?
    • Are Correlations increasing?
    • Is Dispersion decreasing?
    • Is there a Market-Cap effect?
  • We looked at the S&P 500 and its constituent stocks. We parsed through the data for the index and the stocks from 1999 to 2018. 
  • Most of the results are eyebrow-raising. Our conclusions are clear.

Why are we doing this?

Because it affects us directly, even if we’re not agents of Passive Investing. You’ll see how. But to start off, it’s best we give you some context.

Investing really tests the nervous system. Our guru, Warren Buffett, captured the inherent emotional torture of investing in one simple sentence: 

“Market price and intrinsic value often follow very different paths – sometimes for extended periods – but eventually they meet.”

We put in the hours, we do our homework, and we conservatively estimate an intrinsic value. That’s all well and good. But unless Mr. Market comes around to our view, we won’t make money. Mr. Buffett says that “eventually” it may happen. What does “eventually” mean? Nobody knows, not even Buffett. That’s why it’s hard for most investors to hold their nerves. That’s why it’s torture. It requires a certain level of faith.

The thing is – for us to believe that Buffett is right, is to believe that markets function properly. That means markets have buyers and sellers, who are reasonably smart and attach a certain buying or selling price to a certain security. That’s how markets work. If that’s not the case – if prices aren’t determined by the supply of sellers and demand of buyers – it’s not a well-functioning market. One of our other heroes – Michael Mauboussin – puts it best: 

“Sufficient investor diversity is the essential feature in efficient price formation. Provided the decision rules of investors are diverse—even if they are suboptimal—errors tend to cancel out and markets arrive at appropriate prices. Similarly, if these decision rules lose diversity, markets become fragile and susceptible to inefficiency. So the issue is not whether individuals are irrational (they are) but whether they are irrational in the same way at the same time.”

We suspect that the unprecedented growth in Passive Investing, which we think is generally a boon to humanity, has some nasty side-effects. As Mauboussin puts it, “[when]decision rules lose diversity, markets become fragile and susceptible to inefficiency”. 

As adherents of Intelligent Investing, do we need to get comfortable with a new reality where intrinsic value and market price don’t meet for a very long time? Does Buffett’s definition of “eventually” mean 3, 4 or 5 years nowadays, instead of 1 or 2 as it used to be? Or is the new reality just that Buffett’s definition of “eventually” is more unpredictable now – anything from 1 month to 5 years?

This article is about getting the facts straight through some common-sense data analysis. Is Passive Investing breaking the markets?

The Passive Investing Boom

First, let’s make sure we’re on the same page. Passive Investing is the act of investing in exchange-traded-funds (ETFs) that usually mimic an already-established index of stocks, bonds, commodities or other liquid asset classes. It’s “passive” because you don’t take an “active” bet on a company or security; you bet on the entire market and sit on it over the long term. The Godfather of Passive Investing was John Bogle, who, sadly, passed away very recently. Through his firm, Vanguard, he revolutionized investing for the layperson. His thinking was simple but powerful:

  1. Most people don’t care about outsized returns. They’re fine with average returns as long as they have enough money left over for retirement. 
  2. The Mutual Fund Industry charges fees that look low but actually eat up about 30% of the gains over the long term. 
  3. Worse, many of those managers do little more than mimic the index. 
  4. Fees are a controllable variable.
  5. Lower those fees by more than half, allow people to easily invest in indices, and most of them will be much better off than going with a bunch of ordinary mutual funds.  

Warren Buffett had said that in the realm of investing John Bogle did more for a vast majority of people than anyone else in history. Today, Bogle’s ideas are mainstream. And over the last 10 years, since the Great Recession, Passive Investing has garnered trillions of dollars in assets. Today, the total assets-under-management (AUM) in Passive Investment vehicles stands at more than $10 trillion. As you’ve probably guessed, Mutual Funds (the traditional “Active” Managers) have lost assets over the last decade. Bogle created an unstoppable force. 

In 1990, Active Managers accounted for nearly 90% of the AUM in the US. By 2016, that number was 61%. Passive investing accounted for nearly 40% of AUM. It’s even higher now. Here’s the data from Morningstar

The numbers are staggering. Here’s another doozy – when the fast and lethal crash of late 2018 happened, the Wall Street Journal put out a piece saying that about 85% of all trading volume in the equity markets are on autopilot, meaning that they are  controlled by machines, models, or passive investing formulas, creating an unprecedented trading herd that moves in unison and is blazingly fast.”

That doesn’t sound like an “efficient” market to us. In fact, it was this very blazingly fast move that got us thinking about this worldview.

The Backstory

The main reason for that blazingly fast move was in unison was this: those trading models aren’t all that diverse. If you’ve ever worked with a Financial Advisor, you may have heard the term “Asset Allocation Model”. They are optimization models that tell us how to allocate our money across various asset classes like stocks, bonds, commodities etc. The models choose that allocation based on maximizing return for a given level of target risk or minimizing risk for a given level of target return. Many of the new Robo-advisors like Betterment tout that they use a “Mean-Variance Optimization” model. Many other firms use similar models. The common thread in those models is, of course, the data. The other common thread is that they tend to be trend following. The industry word for this is called “the Momentum factor”. By design, these models tend to allocate more to asset classes that have been doing well recently.

The genesis of Mean-Variance Optimization was in something called the Modern Portfolio Theory. This was an amazing intellectual breakthrough in the field of finance in the 1950s and 60s – it laid out a framework for thinking about risk and returns. But empirical evidence since that time has proven that it’s just that – a framework for thinking about the world, a perfect world. In the real world, risk is more than simply standard deviation of returns. 

While, on balance, the Modern Portfolio Theory was an important milestone in the field of finance, it gave rise to some dubious side-effects. Also born at The University of Chicago was the notion of Market Efficiency. The extreme version of this theory suggests that markets – especially equity markets – are so “efficient” that any news is swiftly incorporated into security prices, which makes it impossible to “beat the market”. Proponents of this extreme version point to the dismal performance of Mutual Funds – most of them apparently don’t beat their benchmark over the long term. This paper is not about debating Market Efficiency. But our thoughts on the topic are molded by the giants of investing

The acceptance of Market Efficiency, either in its extreme version or in a milder version, had become mainstream since the 60s. And this movement fueled the acceptance of Passive Investing. The thinking went something like this: If I can’t beat the market, I may as well own the market. Bogle’s idea was more about cost. He wanted to cut the obnoxious Mutual Fund industry fees out of the equation. The Efficient Market Fundamentalists went off on a different tangent – that it was impossible for Fund Managers to beat the index over the long run, because markets are super-efficient. Bogle’s approach is more grounded in common sense. But both arguments have an inherent irony they can’t escape. 

The Irony

The subtext of both those arguments is this: In order to make “market-like-returns” over the long term, the underlying stocks need to generate positive returns (on average) that mimic the underlying returns on equity (ROE) of the companies. Buying into the S&P 500 over the long-term means buying into the idea that corporate America will generate X, Y, Z% on its shareholder capital over the long term. That number usually hovers around 6-8% based on historical data. But that happened in the past only because there was a fully functioning market, in which there were buyers and sellers continuously negotiating stock prices based on their analysis of the underlying companies. Passive Investing has nothing to do with a view on the underlying companies. And yet it expects that the future will rhyme with the past – that stocks will return something positive because they will reflect the underlying performance of the companies. In other words, for Passive Investing (Indexing) to work out over the long-term, they need “Active” investors to do their job. 

That’s the irony: For Passive Investing to function properly it needs Active Investing to function properly. But Passive Investing is – ironically – driving a wedge through the proper functioning of markets, i.e. they’re making markets less efficient. That’s our hypothesis anyway, which we’re about to test.

The issue is price determination. If market prices of underlying stocks in an index are determined by forces other than buyers and sellers negotiating, we have a problem. Let’s take the argument to the extreme – if 100% of assets are in Passive Investment vehicles, what would determine prices? Index Membership? The predominant determinant of prices won’t be “is the company doing well?”. Today, Passive Markets make up more than 40% of Assets Under Management in Equities. We suspect that has already caused some major side-effects in the equity markets.

Just before we get into the weeds, we’ll tie a bow around the irony of Passive Investing. To do that we’ll rely on another giant of investing to clear the air. His theory succinctly ties together the cause and effect of Passive Investing that we’ve been trying to drive home so far: 

“If participants labor under the misapprehension that the market is always right, the feedback they get is misleading. Indeed, the belief in efficient markets renders markets more unstable by short-circuiting the corrective process that would occur if participants recognized that markets are always biased. The more the theory of efficient markets is believed, the less efficient the markets become.”– George Soros

Is this true? Let’s dig into the numbers.

The Hypothesis

Visualize this: A lot of money is pouring into the S&P 500 because most investors...

  1. Believe that markets are super-efficient, so they may as well invest in the broader market, or…
  2. Don’t want to pay Mutual Fund Managers all those high fees, or…
  3. Don’t have the time to look for investment ideas or don’t want to, or…
  4. All of the above. 

We’re not arguing with any of those reasons. For now, we’re just interested in the effects. As the proportion of assets in Passive Investing vehicles goes beyond 40-45% to more than 50%, will markets still function properly? We suspect that cracks are already starting to appear.

Our hypothesis is that the flood of money into Index Funds is changing the markets in noticeable ways. We’ve broken down our hypothesis into a few measurable effects: 

  1. There’s a “tailwind effect”:Since money is mostly flowing into indexes like the S&P 500, it must have a positive effect on its returns. 
  2. Persistence is increasing: We suspect that those tailwind effects will lead to more tailwind effects, primarily because asset allocation models that drive these Passive Investing decisions tend to be trend-following.
  3. Beta plays a bigger role:Now we get into individual stocks territory. The “effect” of the overall market on individual stocks – using the S&P 500 as a proxy – is more pronounced than it used to be. 
  4. Correlation is increasing:Returns of stocks in the S&P 500 are moving together in unison more than before.
  5. Dispersion is decreasing:Daily Returns of stocks in the S&P 500 are less diverse. Said another way, the distribution of returns is narrower. 
  6. There’s a Market-Cap Effect: Because of industry concentration in the Passive Fund Management industry, the sheer size of the firms and the inflow of funds would make it operationally very hard to channel that money into anything but large-capitalization companies. This would create a positive tailwind for large-cap stocks.

Before we get into the results of our sleuth work, we should make you aware of a few details about our data and analysis. None of these, we believe, dilute our findings:

  1. We collected daily returns for all current S&P 500 constituents going back to the beginning of 1999.
  2. We went back to 1999 because we wanted a similar number of data points on either side of the financial crisis of 2008-09.
  3. When we parsed through the found that 373 of the current S&P 500 constituents had data going back all the way. So, we cut out the rest. 
  4. About 1.3% of our daily returns data had 0%s. We suspected this was an error, but this was corroborated by various separate data sources: Yahoo Finance, Wall Street Journal, MarketWatch, NYSE and NASDAQ. Since it affected a small portion of our data, we left the data as is.
  5. In most cases we broke down the data into 2 groups: 1) 1999 to (and including) 2009, and 2) 2008 (including) to (and including) 2018. We know that there is an overlap: 2008 and 2009. We did this because those years were extraordinary events and we didn’t want those 2 years to bias one data set over the other. 
  6. To be absolutely forthcoming, our data set actually ends on January 10th, 2019 because that’s when we started our number crunching. We don’t think a few extra trading days skew our results.
  7. In essence, related to #5 above, 1999-2009 is our “control” data set. We use that time-period as our representation of a “Pre-Passive-Investing-Boom” period.

OK, with all that said, let’s get this party started. 

Is there a Tailwind Effect?


We decided to start from the top. Overall, we asked, did the S&P 500 get a nice tailwind from the inflow of assets into Passive Index Funds? This is the chain of events that cause that tailwinf effect:

  1. Billions of dollars flow into S&P 500 index funds. 
  2. The index fund managers would need to go out there and buy S&P 500 constituents. 
  3. All other things remaining constant, this would drive up prices of the constituent stocks. 
  4. The price of the overall index then, in a circular way, would increase. 

We split up the data in 2 buckets: 

  1. 1999 to 2009.
  2. 2008 to 2018 (edit on 2/20/2019, it said 2008-2009 earlier, which was incorrect)

Because daily data is noisy, we calculated 120-day returns. This roughly translates to 6 months (there are about 240-250 trading days in a year). We did this on a rolling-120-day basis. This makes the data more readable. No gimmicks here. No cherry-picking.

Then we charted out the distribution of those rolling 120-day returns for the two time windows. This is what we found: 

The X-axis is the series of return buckets. The Y-Axis is the number of occurrences. 

We can make a case that there has indeed been a tailwind effect. You can see that the distribution has shifted. But because this is an “overall” market number, it’s almost impossible to isolate the pure effect of Passive Investing. No amount of multivariate regression models will paint a clear picture. 

Next, we asked, are these rolling 120-day returns persistent?

Is Persistence increasing?

We’re not convinced.

For Persistence, we did a simple calculation. We calculated the correlation of each 120-day period of the S&P 500 against the previous 120-day period, starting in 1999. Again, we spit up our data into our two buckets. Here’s the distribution of the correlations we calculated: 

The X-axis is the series of Correlation buckets. The Y-Axis is the number of occurrences – statisticians call this Frequency. 

There is a slight rightward shift, no doubt. But not all the time. One could argue that in the zone where Correlations are positive, there is a pronounced rightward shift. That would support our theory. But that would be a stretch. We’re not here to make the data fit our theory. In fact, as Intelligent Investors, we’ll breathe a sigh of relief if we determine that Passive Investing has no side effects on the markets. 

Is Beta playing a bigger role?


Now we’re zooming into stocks from “overall market” territory. The logical question is this: Are swings in the overall market causing more pronounced swings in individual stocks? 

We took our series of 120-day S&P 500 returns and our 373 series of rolling 120-day returns of our constituent stocks. Then we calculated Betas for each stock over every possible 120-day period from 1999 to 2018. And then, as we’ve been doing, we split up our data into our 2 buckets. 

Here’s what we found: 

The X-Axis is the array of Beta buckets. The Y-Axis is the number of data points or occurrences in each bucket. 

This is compelling. There is a clear rightward shift. And a clear increase in the levels of Beta in the market. Clearly, the “effect” of the S&P 500 on its constituents is more pronounced since the Passive Investing boom accelerated. Now we’re on to something. 

By the way, Beta is the slope of the regression line that models the returns of S&P 500 as the independent variable and returns of each constituent stock as the dependent variable. If you’ve never studied Regressions, please brush up on it immediately. We’re kidding. 

It’s a bit of a backwards way of looking at things. An Index like the S&P 500 is made up of constituent stocks. The weighted average return of the stocks is going to the index return on any given day.  But then the Modern Portfolio Theory (mentioned earlier in the paper) spit out this concept of “systematic risk”, which is a fancy way of saying “overall market sentiment”. The mathematical expression of that concept is Beta. 

We don’t intend to get too “mathy” here, but here’s a cool piece of algebra if you’re in a nerdy mood. Beta, if you rearrange the equation, is the product of the correlation between the market and the stock TIMES the relative volatility of the market and the stock (the ratio of their volatility). So, if Betas increase, they could be because of correlation or volatility. 

In this case, we suspect it’s because of correlation. 

Is Correlation increasing?


Same drill here. We took our series of 120-day returns of the S&P 500 and calculated the correlation between that and the 120-day return of each constituent stock in the S&P 500. Just as a reminder, we find that this method is repetitive but thorough because it captures ALL 120-day periods between the S&P 500 and each of the 373 constituent stocks (that have all the requisite data) in our 20-year sample from 1999 to 2018.

Here’s what we found:

The X-Axis is the array of correlation buckets in our data set. The Y-Axis is the number of occurrences of each correlation bucket. This is the distribution of correlations. 

It’s not a stretch to say that correlations have increased dramatically. Stocks are moving more in unison with the overall market. While we can’t we absolutely deterministic in claiming that the boom in Passive Investing is exclusively causing this, we strongly believe that it has a major effect. In this case, we believe Correlation means Causation, even if we’re aware that it can’t be the only cause. 

Again, as a reminder, 2008 and 2009 data are part of both time periods. OK – our next question was: If stocks are moving more in unison with the index, are they also moving more in unison with each other?

Is Dispersion decreasing?


One way to measure correlation amongst the index’s various constituents is to do a good old-fashioned correlation matrix. We had 373 stocks. We nearly broke our computers while calculating correlation matrices of that size. So, we went with Dispersion, which we think is actually a more effective way of measuring unison amongst constituents. Correlation Matrices offer up a jungle of “pair-wise” correlations between any combination of 2 stocks among the 373 we have. When we ran our numbers, we took an average of those pair-wise correlations. But then we got the feeling that we weren’t being statistically pure. It’s not that this is a PhD paper. But we don’t want to do dubious calculations.

We measured dispersion by calculating the standard deviation of daily returns across the array of 373 stocks, going back to 1999. Statisticians would call this Cross-Sectional Volatility. We call it dispersion – we prefer English to “Statistish”. This is different from volatility, which refers to the standard deviation of a time-series of data for a singlestock or index or portfolio. Dispersion zooms into a single day and quantifies the variation in daily (could be monthly) returns amongst various stocks. They’re different concepts. 

As is customary by now, we split up our dataset into our control sample (1999 to 2009) and our test sample (2008 to 2018). Here’s what we found:

The contrast is stark. Dispersion has decreased dramatically. Now, there could be other factors that went into this. Remember, our assumption is that 1999 to 2009 was a fairly good proxy of a “Pre-Passive-Investing-Boom” period. Since the financial crisis of 2008-09, the bull market has been strong with fewer periods of corrections. For example, 1999-2009 would include the dot-com bust. 2008-2018 wouldn’t, but it does include 2011, 2016 and the latest crash in the fourth quarter of 2018. In our defense, no matter what data samples we choose, one can always pick holes, and rationalize as to why one sample may be biased. We get it. But we think the biases in our two datasets, while undoubtedly present, don’t overshadow the broader conclusions. 

In our view, dispersion has decreased around the same time that Passive Investing reached new heights. Is it a case of Correlation and not Causation? Possibly. But the answer to that question is always subjective, isn’t it? In the fields of Finance and Economics, statistics can only be used to confirm a hypothesis. No conclusions should be drawn merely from looking at data. This is not Physics.

“It is a mistake to use, as journalists and some economists do, statistics without logic but the reverse does not hold: It is not a mistake to use logic without statistics. (Roundtrip Fallacy).”– Nassim Nicolas Taleb

Preferably, we’d use both, which is what we’re doing.

Is there a Market-Cap Effect?


Remember, Passive Index funds need to be managed by someone. When we buy into an S&P 500 index fund, the companies managing that fund needs to go out there and buy shares of the underlying companies.

There are really 2 big index fund managers – Blackrock and Vanguard. The lion’s share of the Passive Investing boom is executed by them. When trillions of dollars of assets flow into their fund, it’s operationally very difficult for them to buy into small-cap indices with small-cap companies. Blackrock and Vanguard create index funds to stay passive, not active. They’d rather not run an index fund with small cap companies. That’s because of the sheer size of fund inflow – they’ll end up owning more than 20% of many of these companies. The sheer size and concentration of funds compels them to keep managing index funds of mostly large cap companies. This would create a significant tailwind in large-cap companies.

To test this hypothesis, we wanted to keep things simple. So, we asked ourselves a simple question: Have Large Capitalization stocks been doing well recently (since the financial crisis). To find out, we stepped outside the realm of the S&P 500 and looked at the Russell group of indices. They are widely used on Wall Street to compare how different “styles” of stocks have been performing. The Russell 1000 is their large-cap index. The Russell 2000 is their small-cap index. And Russell 3000 is their all-cap index, which is a combination of the first two. 

Here’s what we found: 

Large Cap stocks have outperformed. No doubt. But the outperformance isn’t dramatic. This is why we said that “maybe” there is a market-cap effect. The evidence is not as convincing as Beta, Correlation and Dispersion.

Over the long term, we would expect small-cap stocks to outperform large-cap stocks. That’s one of the lessons of the Modern Portfolio Theory – higher risk stocks should yield higher returns. That’s not been the case since the financial crisis. Now, it could be that since the advent of Cloud Computing and AI, large-cap companies have been able to widen their moats – many of them have become unstoppable Global Dominators. But it is odd that there is virtually no benefit in owning small-cap stocks as per this data. We suspect that many of those asset allocation models have noticed the same phenomenon. 


Saying that Passive Investing is breaking the markets is probably too aggressive. But, in our view, it is significantly affecting the markets. 

We looked at 6 signs to confirm our hypothesis. 3 were convincing. 2 were a “maybe”. 1 was not convincing. None refuted our hypothesis.

  1. Is there a tailwind effect? Maybe. 
  2. Has Persistence increased? Not convinced. 
  3. Is Beta playing a bigger role? Yes.
  4. Is Correlation increasing? Yes. 
  5. Is Dispersion decreasing? Yes.
  6. Is there a Market-Cap Effect? Maybe. 

Overall, the signs are clear to us. As Passive Investing vehicles go on to house more than 50% of invested assets in the US and in other countries, we suspect these signs will exacerbate. 

So, what should we do?

As we see it, there are 3 ways to adapt: 

  1. Invest in non-index stocks.
  2. Invest in larger cap stocks.
  3. Invest in companies that are in the Index Funds business. 
    • Blackrock
    • Vanguard
    • S&P Global Inc.

Investing in non-index stocks is a “let’s not pay their sport” strategy. If we’re old-school investors, we like to invest in comfortable companies at comfortable prices and get rewarded by the market’s validation of our views over the long term. We’re more likely to succeed in a part of the arena that’s less susceptible to Passive Investing fund inflows. This could include smaller-cap stocks, but the data shown in the previous section isn’t encouraging from a long-term returns standpoint.

Investing in mostly larger-cap stocks is a “if you can’t beat ‘em, join ‘em” strategy. But in this strategy, we’d have to be prepared to accept that broad market movements will drive stock prices most of the time. This means that market prices may take longer to converge to estimates of intrinsic value. As if investing wasn’t already a harsh test of our nerves, we’ll need to go on frequent mindfulness and meditation retreats.

Option #3 is the most actionable one. There are 3 major companies (in the US at least) that are driving this Passive Investing boom. Two are investable – Blackrock and S&P Global Inc. 

Our sport is changing. We did this work to understand the sport better. We’re long-term investors who believe that in the long run, good companies that make good products and are run by competent management teams will generate good returns. But this new phenomenon called Passive Investing doesn’t work like that. It’s changing the way markets function. That’s what the data suggests. It’s not our job to fight against it. But we won’t throw out our principles either. We’ll use them to adapt to new realities. 

Digging through S&P Global Inc. and Blackrock is a logical step in that direction.

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