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Data Update 2 for 2020: Retrospective on a Disruptive Decade

My data updates usually look at the data for the most recent year and what I learn from them, but 2020 also marks the end of a decade. In this post, I look back at markets over the period, a testing period for many active investors, and particularly so for value investors, who found that even as financial assets posted solid returns, what they thought were tried and true approaches to "beating the market" seemed to lose their power. In addition, trust in mean reversion, i.e., that things would go back to historic norms was shaken as interest rates remained low for much of the period and PE ratios rose above historical averages and continued to rise, rather than fall back. 

1. It was a great year, and a very good decade, for equities, and a very good year for bonds!
While investing should always be forward-looking, there is a benefit to pausing and looking backwards. If you had US stocks in your portfolio, 2019 was a very good year. The S&P 500 started the year at 2506.85 and ended the year at 3230.78, an increase of 28.88%, and with dividends added, the return for the year was 31.22%. To get a sense of how this year measures up against other good years, I compared it to the annual returns from 1927 to 2019 in this graph:
Download spreadsheet with annual market data
Over the 92 years that are in this historical assessment, 2019 ranked as the sixteenth best year and second only to 2013 (annual return of 32.15%) in this century. While stocks have garnered the bulk of the attention for having a good year, bonds were not slackers in the returns game. In 2019, the ten-year US treasury bond returned 9.64% and ten-year Baa corporate bonds weighing in with a 15.33% return. That may surprise some, given how low interest rates have been, but the bulk of these returns came from price appreciation, as the US treasury bond rate declined from 2.69% to 1.92%, and the corporate bonds also benefited from a decline in default spreads (the price of risk in the bond market) during the year. The year also capped off a decade of gains for stocks, with the S&P almost tripling from 1115.10 on January 1, 2010 to 3230.78 on January 1, 2020, and with dividends included and reinvested, the cumulated return for the decade is 252.96%. To put these returns in perspective, I have compared this cumulated return to the eight full decades that I have data for in the table below, in conjunction with the cumulated returns for treasury and corporate bonds over each decade:
Download spreadsheet with annual market data
While 2010-19 represented a bounce back for stocks from a dismal 2000-09 time period, with the 2008 crisis ravaging returns, it falls behind three other decades of even higher returns (1950-59, 1980-89 and 1990-1999). It was a middling decade for both treasury and corporate bonds, with cumulated returns running ahead of the three decades spanning 1940 to 1969 but falling behind the other decades, in terms of returns delivered. Treasury bills delivered their worst decade of returns, since the 1940s, with the cumulated return amounting to 5.25%. I don’t want to overanalyze historical data, but there are interesting nuggets of information in the data:
a. Historical Risk Premium: The US historical data has been used by many analysts in corporate finance and valuation as the basis for computing historical risk premiums and in the table below, I compute the risk premiums that investors would have earned in this market, investing in stocks as opposed to treasury bills and bonds, over different time periods, and with different averaging approaches:
Download spreadsheet with annual market data
If you go with the geometric average premium from 1927-2019 as your predictor for the equity risk premium in 2020, US stocks should earn about 4.83% more than US treasury bonds for the year:
Expected return on stocks in 2020 = T.Bond Rate + Historical ERP 
= 1.92% + 4.83% = 6.75%
Since a portion of this return will come from dividends, the expected price appreciation in stocks is the difference:
Expected price appreciation on stocks = Expected Return - Dividend yield 
= 6.75%- 1.82% = 4.93%
I am not a fan of historical premiums, not only because they represent almost an almost slavish faith in mean reversion but also because they are noisy; the standard errors in the historical premiums are highlighted in red and you can see that even with 92 years of data, the standard error in the risk premium is 2.20% and that with 10 or 20 years of data, the risk premium estimate is drowned out by estimation error.
b. Asset Allocation: The fact that stocks have beaten treasury and corporate bonds by wide margins over the entire history is often the sales pitch used to push investors to allocate more of their savings to stocks, with the argument being that stocks always win in the long term. The data should yield cautionary notes:
  • First, in three decades out of the nine in the table, stocks under-performed treasury bonds and treasury bills, and if your response is that ten years is not a long enough time period, you may want to check the actuarial tables. 
  • Second, there is a selection bias in our use of the US markets for computing the historical premium. Looking across the globe, the US was one of the most successful equity markets of the last century and using it may be skewing our results upwards. Put bluntly, if you had invested in the Nikkei at the height of its climb in the 1980s, you would still be struggling to get back the money you lost, when the Japanese markets collapsed.
c. Market Timing: It is human nature to try to time markets, and some investors make it the central focus of their investment philosophies. I will not try to litigate the good sense of doing so in this post, but the historical return data gives us a sense of both the upside and the downside of doing so. In terms of pluses, an investor who was able to avoid the doomed decades (when stocks earned less than T.Bills and T.Bonds) would be comfortably ahead of an investor who did not, if he or she stayed fully invested in the remaining decades. In terms of minuses, if the market timing investor failed to stay invested in stocks in the good decades, the opportunity costs would quickly overwhelm the benefits. Between 2010 and 2019, there were many investors who believed that a correction was around the corner, driven by their perception that interest rates were being kept artificially low by central banks and that they would revert to historic norms quickly. When that reversion did not occur, these investors paid a hefty price in returns foregone. All of the historical returns that I have reported in this section are nominal, and to the extent that you are interested in real returns, you may want to download the historical data from my website and check out the results. (Hint: Not much changes)

2. A Low Interest Rate Decade
If there was a defining characteristic for the decade, it was that interest rates, both in the US and globally, dropped to levels not seen in decades. You can see this in the path of the US 10-year treasury bond rate in the graph below:
Download historical treasury rates, by year
Since the drop in rates occurred after the 2008 crisis, and in the aftermath of concerted actions by central banks to bolster weak economies, it has become conventional wisdom that it is central banks that have kept rates artificially low, and that the ending of quantitative easing would cause rates to revert back to historical averages. As many of you who have been reading my posts know, I don't believe that central banks have the power to keep long term market-set rates low, if the fundamentals don't support low rates. In fact, one of my favorite graphs is one where I compare the 10-year treasury bond rate each year to the sum of the inflation rate and real GDP growth rate that year (intrinsic riskfree rate):
Download historical treasury rates, by year
As you can see, the main reason why rates have dropped in the US and Europe has been fundamental. As inflation has declined (and become deflation in some parts of the world) and real GDP growth has been anemic, intrinsic and actual risk free rates have dropped. To the extent that the difference between the two is a measure of central banking actions, it is true that the Fed’s actions kept actual rates lower than intrinsic rates more in the last decade than in prior years, but it is also true that even in the absence of central banking intervention, rates would not have reverted back to historical norms. 

3. It was a tech decade, and FAANG stocks stole the show!
While it was a good decade for stocks,  the gains varied across sectors. Using the S&P 500 again as the indicator, you can see the shift in value over the decade by looking at how the different sectors evolved over the decade, as a percent of the S&P 500:
The most striking shift is in the energy sector, which dropped from 11.51% of the index to 4.60%, in market capitalization terms. Some of this drop is clearly due to the decline in oil prices during the decade, but some of it can be attributed to a general loss of faith in the future of fossil fuel and conventional energy companies. The biggest sector through the entire decade was technology but its increase in percentage terms seems modest at first sight, rising from 19.76% in 2009 to 21.97% in 2019, but that is because two of the biggest names in the sector, Google and Facebook, were moved to the communication services sector; if they had been left in technology, its share of the index would have risen to more than 30%. In fact, five companies (Facebook, Alphabet, Apple, Netflix and Google), representing the FAANG stocks, had a very good decade, with their collective market capitalization increasing by $3.4 trillion over the ten years:

Put in perspective, the FAANG stocks accounted for 22% of the increase in market capitalization of the S&P 500, and any portfolio that did not include any of these stocks for the entire decade would have had a tough time keeping up with the market, let alone beating it. (This is an approximation, since not all five FAANG stocks were part of the S&P 500 for the entire decade, with Facebook entering after its IPO in 2012 and Netflix being added to the index in 2014).

4. Mean Reversion or Structural Shift
One of the perils of being in a market like the US, where rich historical data is available and easily accessible is that analysts and academics have pored over the data and not surprisingly found patterns that have very quickly become part of investment lore. Thus, we have been told that value beats growth, at least over long periods, and that small cap stocks earn a premium, and have converted these findings into investing strategies and valuation practices. While it is dangerous to use a decade’s results to abandon a long history, the last decade offered sobering counters to old investing nostrums.

a. Value versus Growth
The basis for the belief that value beats growth is both intuitive and empirical. The intuitive argument is that value stocks are priced cheaper and hence need to do less to beat expectations and the empirical argument is that stocks that are classified as value stocks, defined as low price to book and low price to book stocks, have historically done better than growth stocks, defined as those trading at high price to book and high price earnings ratios. Looking at the annual returns on the lowest and highest PBV stocks in the United States, going back to 1927:
Raw Data from Ken French
The lowest price to book stocks have historically earned 5.22% more than the highest price to book stocks, if you look at 1927-2019. Broken down by decades, though, you can see that the assumption that value beats growth is not as easily justified:
Raw Data from Ken French
While there are some, especially in the old-time value crowd, that view the last decade as an aberration, the slide in the value premium has been occurring over a much longer period, suggesting that there are fundamental factors at play that are eating away at the premium. If you are a believer in value, as I am, there is a consolation prize here. Assuming that low PE stocks and low PBV stocks are good value is the laziest form of value investing, and it is perhaps not surprising that in a world where ETFs and index funds can be created to take advantage of these screens, there is no payoff to lazy value investing. I believe that good value investing requires creativity and out-of-the-box thinking, as well as a willingness to live with uncertainty, and even then, the payoff 

b. The Elusive Small Cap Premium
Another accepted part of empirical wisdom about stocks not only in the US, but also globally, is that small cap stocks deliver higher returns, after adjusting for risk using conventional risk and return models, than large cap stocks. 
Raw Data from Ken French
Looking at the data from 1927 to 2019, it looks conclusively like small market cap stocks have earned substantially higher returns than larger cap stocks; relative to the overall market, small cap stocks have delivered about 4-4.5% higher returns, and conventional adjustments for risk don't dent this number significantly. Not only has this led some to put their faith in small cap investing but it has also led analysts to add a small cap premium to costs of equity, when valuing small companies. I have not only never used a small cap premium, when valuing companies, but I am skeptical about its existence, and wrote a post on why a few years ago. Again, updating the data by decades, here is what I see:
Raw Data from Ken French
As with the value premium, the size premium had a rough decade between 2010 and 2019, dropping close to zero, on a value weighted basis, and turning significantly negative, when returns are computed on a equally weighted basis. Again, the trend is longer term, as there has been little or no evidence of a small cap premium since 1980, in contrast to the dramatic premiums in prior decades. If you are investing in small cap stocks, expecting a premium, you will be disappointed, and if you are still adding small cap premiums to your discount rates, when valuing companies, you are about four decades behind the times.

5. New buzzwords were born
Every decade has its buzzwords, words that not only become the focus for companies but are also money makers for consultants, and the last decade was no exception. At the risk of being accused of missing a few, there were two that stood out to me. The first was big data, driven partly by more extensive collection of information, especially online, and partly by tools that allowed this data to be accessed and analyzed. The other was crowd wisdom, where expert opinions were replaced by crowd judgments on a wide range of applications, from restaurant reviews to new (crypto) currencies.

a. Big Data
Earlier in this post, I looked at the surge in value of the FAANG stocks, and how they contributed to shaping the market over the last decade. One common element that all five companies shared was that they were not only reaching tens of millions of users, but that they were also collecting information on these users, and then using that information to improve existing products/services and add new ones. Other companies, seeking to emulate their success, tried their hand at “big data”, and it became a calling card for start-ups and young firms during the decade. While I agree that Netflix and Amazon, in particular, have turned big data into a weapon against competition, and Facebook’s entire advertising business is built on using personal data to focus advertising, I personally believe that like all buzz words, big data has been over sold. In particular, I noted, in a post from 2018 ,that for big data to create value,
  1. The data has to be exclusive: For data to be valuable, there has to be some exclusivity. Put simply, if everyone has it, no one has an advantage. Thus, the fact that you, as a business, can trace my location has little value when two dozen other applications and services on my iPhone are doing exactly the same thing. 
  2. The data has to be actionable: For value conversion to occur, the data that has been collected has to be usable in modifying and adapting the products and services you offer as a business. 
Using these two-part test, you can see why Amazon and Netflix are standouts when it comes to big data, since the data they collect is exclusive (Netflix on your viewing habits/tastes and Amazon on your retail behavior) and is then used to tailor their offerings (Netflix with its original content investments and offerings and Amazon with its product nudging). Using the same two-part test, you can also see why the claims of big data payoffs at MoviePass and Bird Scooters makers never made sense.

b. Crowd Wisdom
One consequence of the 2008 crisis was a loss in faith in both institutional authorities (central banks, governments, regulators) but also in experts, most of whom had been hopelessly wrong in the lead up to the crisis. It is therefore not surprising that you saw a move towards trusting crowds on answers to big questions right after the crisis. It is no coincidence that Satoshi Nakamoto (whoever he might be) posted the paper laying out the architecture of Bitcoin in November 2008, a proposal for a digital currency without a central bank or regulatory overlay, where transactions would be crowd-checked (by miners). While Bitcoin has been more successful as a speculative game than as a currency during the last decade, the block chains that it introduced have now found their way into a much wider range of businesses, threatening to replace institutional oversight (from banks, stock exchanges and other established entities) with cheaper alternatives. The crowd concept has expanded into almost every aspect of our lives, with Yelp ratings replacing restaurant reviewers in our choices of where to eat, Rotten Tomatoes supplanting movie critics in deciding what to watch and betting markets replacing polls in predicting election outcomes. I share the distrust of experts that many others have, but I also wary of crowd wisdom. After all, financial markets have been laboratories for observing how crowds behave for centuries, and we have found that while crowds are often much better at gauging the right answers than market gurus and experts, they are also prone to herding and collective bad choices. For those who have become too trusting of crowds, my recommendation is that they read “The Madness of Crowds”, an old manuscript that is still timely.

The decade to come
It has been said that those who forget the past are destined to relive it, and that is one reason why we pore over historical track records, hoping to get insight for the future. But it has also been said that army generals who prepare too intensely to fight the last war will lose the next one, suggesting that reading too much into history can be dangerous. To me the biggest lesson of the last decade is to keep an open mind and to not take conventional wisdom as a given. I don’t know what the next decade will bring us, but I can guarantee you that it will not look like the last one or any of the prior ones, So, strap on your seat belts and get ready! It’s going to be a wild ride!

YouTube Video


Data Links

  1. Stocks, Bonds and Bills: 1928-2019
  2. Intrinsic and Actual Risk free Rates: 1954-2019
  3. Ken French Data on Value and Size Effects

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