For those who aren’t familiar, Vulcans are a fictional alien humanoid species in the Star Trek media franchise who strictly adhere to logic, reason and suppression of emotions. The story goes that, after centuries of continuous violent and highly destructive warfare, a Vulcan named Surak started the Time of Awakening. Surak taught that the root cause of all the problems on Vulcan lay in the uncontrolled outpouring of the people’s emotions. His followers swore to live by an ethical system devised by Surak and based purely on logical principles. Emotions were to be controlled and repressed.
The contrast of the Vulcan character Spock’s logic, ethics, and stoicism against a crew of emotional and intuitive humans was the winning formula in Roddenberry’s original low-budget ’60s teleplays. Star Trek became a cult classic in syndication and subsequently spawned a hit franchise of highly successful films and television shows that have spanned sixty years.
Like many nerdy kids growing up in the ’70s and ’80s, I was obsessed with Star Trek. I’ve always thought of the world imagined by Roddenberry as a north star or benchmark for human progress. Even though it’s utopian, I still often ask myself: how close are we to that future? How far will we get within my short life?
I particularly liked Spock.
Whenever I’ve read or heard people talk about “irrational exuberance” in stock markets, I’ve thought about Spock and how he would invest. Would he? I’m not alone. Barry Ritholz’s post is a fun read, and Bret Arends logically concludes that there would never be a stock market on planet Vulcan.
In Internet meme terms, #sponks?
I can still remember seeing the nightly news as a kid, where there were always reports about the travails of inflation, interest rates, and adults being deathly serious and worried about it. Later, I watched the first contemporary global financial crisis on October 19, 1987—a day known as “Black Monday”—when the Dow Jones Industrial Average dropped 22.6 percent. I watched the pandemonium on the trading room floor and even grown men crying. The severity of the crash sparked fears of extended economic instability or even a reprise of the Great Depression, which, like every American kid, I had learned about in history class as something that left many people unemployed, homeless, and starving. I wondered: if this stock market thing is so dangerous, why do people do it? It seemed irrational. I’ve probably held that underlying bias my entire life.
A Vulcan would likely never gamble frivolously, but they would engage in a well-reasoned, calculated risk, as all action and enterprise (wink) in the face of uncertainty involves some degree of risk. What’s the difference between investing and gambling? After all, both involve risk in hopes of financial gain.
The distinction may lie in an individual’s risk tolerance. Is an investor taking a calculated risk, or are they engaging in a game of statistical probability?
Shall we play a game of chance?
Would a Vulcan investor prefer to have a 95% chance of turning $100 into $110 over a year—breaking even or losing 5 bucks—versus a 50/50 chance to double the investment or lose most of it? Psychologists would say that opting for the $110 is consistent with investor behavior—a calculated risk. Going for the all-or-nothing option is gambling behavior. Like most things, it’s a distribution curve. When we consider that many volatile common stocks, and even a market index, could gain or lose 20–30% or more in a matter of days or even hours, the lines can be blurry indeed. In fact, professional competitive gamblers and modern quantitative investors both use probability analysis to win.
Based on past performance, if an investor expects reliable portfolio returns of 15%, 20%, 30%, or greater over a longer time horizon, their tolerance for (or ignorance of) risk may be veering into gambling territory—even if they are investing in a reputable, popular asset. Why? Well, the long-term compound annual growth rate (CAGR) for a reasonably diversified portfolio is around 7–8% for U.S. equities and around 4% for the safer spectrum of bond markets. That 20-year return has remained remarkably consistent for generations across 150 years of the U.S. market. While that rate of return may not seem like much based on recent history, when returns are reinvested and compounding, it becomes an attractive and reasonably low-risk long-term investment.
So back to our question, what would Spock do? What if, through a series of misadventures, he traveled back in time to 2020 and was marooned in the current timeline? With his alien features and lack of official identity, he would have limited ability to find work, similar to the classic 1967 episode, The City on the Edge of Forever where Kirk, Spock and McCoy travel back in time to depression-era New York City and have to live in a shelter and find odd jobs. A similar theme was revisited in the 1986 feature film, Star Trek IV: The Voyage Home.
To survive in our current era, what if Spock used his intelligence to acquire seed capital and invested it in the stock market, perhaps in hopes of acquiring enough money over his long lifespan to build a time machine to return to his timeline? His knowledge of human history would be exceptional, but not likely detailed enough to know precisely when certain events would unfold, or which companies were going to be successful when.
Arriving in the spring of 2020, in the midst of the global Covid-19 pandemic, Spock would likely be fascinated by human investor behavior. According to Moira O’Neill of the Financial Times, the retail investor marketplace saw a flood of new players between the age of 25 to 34 during the pandemic. The lockdown ushered in a period of home isolation and direct government stimulus, leading to surplus spending income. Eating at home, a closed hospitality industry, and restrictions on overseas travel resulted in significant savings for many households.
O’Neill notes that Interactive Investor, a British trading platform for retail investors, saw a surge in new accounts in the second quarter of 2020, with growth rates of 275% and 258% year over year. The US saw Robinhood’s rise and continued interest in meme coins and cryptocurrency. Millennials were particularly attracted to major tech stocks and cryptocurrencies, with Apple, Amazon and Microsoft ranking among the top 10 holdings in April.
Spock would no doubt look at a few historic charts and be puzzled by the market returns humans were earning. Amazon shares, purchased at $1,901 in early April, climbed steadily to $3,531 by September 2, a staggering 85% gain. Apple shares saw an even more impressive rise, increasing from $60 to $134, a 123% surge. The thrill of doubling one’s money in a short period was undoubtedly a driving factor for these young investors.
Risks inherent to equity investing are well understood among seasoned and professional investors. What goes up must eventually also come down. For example, Eastman Kodak and Simplicity Pattern were in the “Nifty Fifty,” a popular basket of “can’t lose” blue chip stocks back in 1970. Unfortunately, many retail investors living through a long bull market with low inflation and rising asset prices have not experienced the devastating downside the market has seen in the past. These consistent outsized returns have seasoned financial analysts and economists shaking their heads, knowing that a painful lesson awaits.
Spock would want to understand irrational behavior and would turn to economics as the most logical approach to understanding the intersection of financial markets, politics, human psychology and allocation of resources. Economists have observed that market valuations have become disconnected from economic reality. A global pandemic with a stock market boom seems to defy common sense unless you consider that “printing” unprecedented amounts of money to ride out lock-downs, followed by subsequent rounds of quantitative easing and supply chain disruptions led to inflation and an asset valuation bubble. I’d think Vulcans, like many economists, would have predicted that outcome.
Bob Elliott, who led the quantitative research team at Bridgewater & Associates noted on an episode of the Excess Returns podcast that markets aren’t disconnected from macroeconomic reality, but prices can and do diverge from it for long periods. Ultimately, asset prices realign with fundamentals. Forward earnings have to come from growth, and that growth is interconnected with margins, wages, productivity and revenue.
This principle holds true for companies and stock prices as well. This is why investing in equities is not mere speculation. When you buy a stock, you’re buying a piece of a business where the primary goal is profit. We observe oscillating bubbles and busts, and prices can diverge, but in the end, companies generate cash flows and earnings. Those earnings are then bought at specific multiples, which are rooted in economic fundamentals like sales and market share within the economy.
Consider if S&P 500 earnings are expected to grow 17% year-over-year based on the prices we pay for forward earnings. How do we get to that figure? It comes down to a combination of margins and revenues. If you think of S&P 500 companies as a diverse reflection of the broader US economy, we would need nominal growth of 5% and an additional margin expansion to achieve the remaining 12% earnings growth. Margins are the difference between sales and costs, with labor being the biggest expense. Paying the average population of labor less means workers have less money to spend.
The only way to achieve margin expansion in that situation is by people spending more than they earn. Will households borrow more? Will businesses reinvest profits and increase capital expenditures? Will the government deficit widen? That money has to come from somewhere.
Reflecting back on the 2000 cycle, we saw similar patterns. Ultimately, market prices came back down to earth. The Shiller Cyclically Adjusted Price to Earnings ratio or CAPE is a valuation measure that uses real earnings per share (EPS) over a 10-year period to smooth out fluctuations in corporate profits that occur over different periods of a business cycle. It’s used as barometer for asset valuation trends — particularly periods of broad overvaluation.
Current Shiller PE Ratio: 36.34
Mean: 17.21
Median: 16.03
Min: 4.78 (Dec 1920)
Max: 44.19 (Dec 1999)
Spock’s edge as an investor would be his intelligence, patience and long lifespan. His investment decisions would be based on pure logic and mathematics. Perhaps he would be the world’s greatest quantitative investor. With access to the world’s information on the Internet, understanding finance and markets would be a quick study for Spock. He would undoubtedly begin with academic finance.
Vulcans and the Federation of Planets, living in a post-scarcity age where energy and materials are practically infinite thanks to technological innovations like matter replicators, “evolve beyond” organizing their economies via market capitalism. Having operated as a Chief Science Officer on exploratory missions for the Federation, with a mandate to avoid intervening in the technological progress of planetary civilizations, Spock would find 21st-century human markets a primitive way of organizing labor and resources. He would learn that the roots of academic finance go back centuries, intertwined with various interdisciplinary works across economics and mathematics, and that, during the 1950s, applied academic finance experienced an analytical, data-driven renaissance that established the theoretical foundations for how most professionals invest in the 21st century. Spock would find a rich collection of academic research publicly available on the internet that would lead him on a fascinating journey to develop his approach.
Let’s imagine what Spock would discover as he learned how to develop a logical investment portfolio that would maximize his returns.
When most of us initially imagine what it means to invest in the stock market, we think about buying shares in companies that we think will make a good return. The collection of our investments, including stocks and other assets make up our portfolio.
A 20th century American human named Harry Markowitz introduced what is now known as Modern Portfolio Theory in his article Portfolio Selection in a 1952 issue of The Journal of Finance. He said that the performance of an individual stock is not as important as the performance and composition of an investor’s entire portfolio over time. He noted that diversification was critically important to consistently achieve returns.
For his theory of allocation of financial assets under uncertainty, also known as the Theory of Portfolio Choice, Markowitz shared the 1990 Nobel Memorial Prize in Economic Sciences with William F. Sharpe and Merton Miller. Specifically, the Nobel Committee cited the theory of portfolio choice developed by Markowitz as the “first pioneering contribution in the field of financial economics.” The Nobel Committee also acknowledged that Markowitz’s original portfolio theory was the basis for “a second significant contribution to the theory of financial economics”: the Capital Asset Pricing Model (CAPM), a theory of price formation for financial assets.
Bill Sharpe & Merton Miller’s CAPM model basically compares three things:
- an asset’s sensitivity to market risk, often represented by the quantity beta (β)
- the expected return of a market benchmark
- the expected return of a theoretical risk-free asset (such as a 100% guaranteed bond).
The formula is: (rf + beta) x equity risk premium
This helps determine the relationship between risk and expected return on an investment when designing a portfolio. Despite the evolution of better approaches to portfolio selection, it remains a foundational rule of thumb due to its simplicity and utility.
In relation to this idea of market beta (β), we often hear the term market alpha (α). Market alpha is a measure of an investment’s performance compared to a benchmark index, such as the S&P 500. Essentially, it represents the “excess returns” that an investment generates beyond what would be statistically predictable based on its risk level and the performance of the broader market.
Put simply, with so many sophisticated indices available today, alpha is the return from an investor’s skill or luck, while beta is the return from a calculated exposure to statistically predictable risk. As you can imagine, separating alpha from beta in a portfolio isn’t easy to do. If you’re working in index funds—all of which are based on benchmarks—your returns are mostly beta, influenced by your appetite for risk, for which you’re potentially rewarded with a risk premium.
Over time, academic finance has developed ever more sophisticated methods of explaining, or statistically modelling asset prices. French mathematician Louis Bachelier described in his 1900 PhD thesis “The Theory of Speculation” how the prices of commodities and stocks varied in markets, and introduced what’s called “The Random Walk Hypothesis.” This states that stock market prices are not significantly different from random noise and thus cannot be predicted.
Eugene Fama and Paul Samuelson built upon Bachelier’s work and evolved those ideas into the Efficient Market Hypothesis. In the mid 1960s Fama and Samuelson both published papers on the Random Walk Hypothesis showing that if the market is efficient, prices would exhibit “random-walk” behavior and that asset prices reflect the latest available information. A critical and direct implication is that it is impossible to “beat the market” consistently on a risk-adjusted basis. In 1970, Fama published a review of both the theory and the evidence for the hypothesis. Because the EMH is formulated in terms of risk adjustment, it only makes testable predictions when coupled with a particular model of risk.
Today, most academic investors, including Fama, recognize that while markets are obviously not perfectly efficient, they are practically efficient enough to serve as a foundation for understanding prices. Other factors related to behavioral economics are either improbable to measure or attributable to other factors over time.
In other words, Fama’s hypothesis is merely a starting point that observes the “wisdom of the crowd.” When a massive population of investors is in play in a given market, and all—or many—are operating with the most up-to-date information, the market has the potential for efficient and rapid price discovery. This idea is often summarized in the adage: “If you think you know something that the market hasn’t already priced in, statistically speaking, you’re probably mistaken.” That is to say, if you think you have research or insight indicating that the price of a specific asset is going to go up, many others likely have the same information, which is reflected in the current price—or even superior data to the contrary. There are two sides to every trade that balance to zero, which is why owning a broad sample of the entire market captures the total net market gains. Jack Bogle described this in his humorous quote, “instead of trying to find a needle in a haystack, just buy the haystack.”
What is this haystack? Well, Bogle was talking about an index fund that simply tracks a market benchmark index, such as the S&P 500 or the Nasdaq 100. The first stock-market indices were developed and published in the 1880s by financial newspapers as day-to-day summaries of stock price fluctuations. Later, more sophisticated indices like the S&P 500 (1926) were developed Standard & Poor Publishing House’s Bureau of Statistics for professional financial analysts and stock traders.
So how did we arrive at this point where an ever-growing population of investors are opting for passive strategies?
They are statistically more predictable.
Harry Markowitz’s work on the importance of diversification had been published 12 years prior to Eugene Fama’s work on the Efficient Market Hypothesis, which suggested that outperforming the market would be difficult. Bill Sharpe’s work on CAPM, observing that portfolio performance is largely explained by exposure to market risk, was published the same year. Theory and evidence were starting to point to a startling reality: the typical investment approach of picking stocks and timing the market wasn’t as useful as previously imagined. It was the beginning of a long, multi-decade reveal, where the Wizard of Oz began shouting, “Ignore the man behind the curtain!”
People started asking what active managers were doing to justify their high fees of 2-5%? This growing body of research indicated that many were getting paid to simply take risk with other people’s money that didn’t outperform the market after taxes and fees. In other words, in the long run, investing in active management was riskier and less consistent than just buying a broad sample of the stocks in the market yourself.
In 1964, Mac McQuown, an Illinois farm boy who took a life-changing class in finance at Northwestern and became a life-long self-described “data dog” joined a quantitative investment strategy think tank inside Wells Fargo and assembled a team of academics to work on quantitative investing strategies. Six of the people in this group eventually went on to win the Nobel Memorial Prize in Economic Sciences for their respective works.
The researchers at Wells Fargo asked a simple question: if all this evidence is true—that active managers don’t actually beat the market long term and any temporary excess returns are due to luck or additional known risk—then what’s the most logical and reliable approach to investing? They concluded it would be an approach that minimizes costs and captures market beta returns. Based on this sensible premise, they came up with the idea of the index fund, which simply replicates an index that tracks a broad market sample and its returns based on cap-weight, buying the number of shares of a company based on its market capitalization as a reasonable proxy for its relative contribution to the overall returns.
The first large cap Index Fund was deployed by Wells Fargo investment advisers for the pension plan for Samsonite luggage company in 1971. However, due to the Glass Steagall Act of 1933 that separated commercial and investment banking, Wells Fargo wasn’t allowed to sell index funds to retail investors.
Realizing the potential of their innovation for the average person, they contacted a well-known investor named Jack Bogle, the recent founder of a new type of mutual fund operation he named Vanguard, after Horatio Nelson’s flagship at the Battle of the Nile, HMS Vanguard. Jack was also interested in implementing these ideas because he was looking for strategies to restructure active management out of the mutual fund business and start a revolutionary fund owned by the fund shareholders—not the “mutual” management firm that lorded over fund managers, demanding higher profits through greater risk and higher fees. The academics at Wells Fargo shared this discovery with Bogle for the very reasonable price of $0.00—an act of tremendous generosity that would transform investing over the next fifty years.
Jack launched the first large cap equity index mutual fund available to retail investors in 1976 called the First Index Investment Trust (now called the Vanguard 500 Index Fund). Initially, the ship didn’t see any wind in her sails. Investors and the active advisor community balked at the idea of mere “average returns.” The press dubbed it “Bogle’s Folly“. But, over time, the proof of the pudding was in the eating. The public and the press took notice that these simple algorithmic passive funds were outperforming active funds over time, especially after fees. Vanguard has since become one of the largest asset managers in the world with more than 1 trillion dollars under management.
Dimensional Fund Advisors, led by David Booth—who had previously been part of the Wells Fargo group that created the first index fund—launched the first Small Cap Fund in 1981. Booth’s idea was that institutions, like the Samsonite Pension Plan, could use a small cap fund to diversify their portfolios beyond large cap stocks. Most institutions were invested in large cap stocks because small cap stocks are more expensive to trade. There was no small cap index available at that time, so Dimensional did not implement a strictly passive index fund, since that would require rigid trading around index reconstitution dates to match the index. Instead, they built a more flexible product that resembled an index fund more than a traditional actively managed fund.
Following Dimensional’s launch of the first Small Cap Fund, Rolf Banz, one of Eugene Fama’s students at the University of Chicago, published a paper observing that small cap stocks, such as those held in Dimensional’s fund, demonstrated systematically higher returns that could only be explained by their exposure to market risk. It turned out that not only were small cap stocks good for diversifying large cap portfolios, as David Booth had originally intended, but they might also offer exposure to another source of higher-than-expected returns. This came to be known as the “small cap premium.”
These factors were made famous by a dynamic duo of academics you’ll hear referenced among investors: Fama & French. Gene Fama, the same guy who came up with the Efficient Market Hypothesis, was challenged by his student’s thesis. The data supported it. These findings appeared to violate his Market Efficiency Hypothesis. If market risk is the only thing that explains differences in returns and some types of stocks have systematically higher returns than that theory predicts, Fama & French concluded it could mean one of two things: either the markets are not efficient or that we needed a better model for market efficiency.
Fama and Ken French set forth to defend the Market Efficiency Hypothesis with a new asset pricing model. Bill Sharpe’s CAPM had predicted that exposure to market risk was the only driver of expected returns, but the model had struggled empirically. Based on the observation that Sharpe’s theory was being systematically violated by certain types of stocks, Fama & French posited in a seminal 1992 paper that, in addition to market risk, the unique risks of small cap stocks and value stocks may be needed to explain differences in expected returns. This resulted in the Fama-French Three Factor Asset Pricing Model, which launched a generation of quantitative factor-style investors and has become a cornerstone of empirically-driven financial investment strategy.
This paper was followed up in 2015 with another highly cited paper from the same authors suggesting that profitability and investment were additional factors needed to explain differences in expected returns. This resulted in what’s widely known today as the Fama-French Five Factor Asset Pricing Model.
A subsequent 2017 paper extending the model to a large data set of international markets further demonstrated that these five factors are able to explain 95% of differences in returns between diversified portfolios. This means that if two funds have different returns, the difference is most likely explained by exposure to the factors in the model.
Today Fama and French’s Five Factor asset pricing model is the core model in academic Finance.
Fama & French’s five factors:
- Market risk: The excess return of a broad market portfolio over a risk-free rate
- Size: The effect of small-cap stocks potentially outperforming large-cap stocks
- Value: The outperformance of high book-to-market value companies versus low book-to-market value companies
- Profitability: The difference between the returns of firms with high and low operating profitability
- Investment: The difference between the returns of firms that invest aggressively versus those that invest conservatively
Passive investors asked a simple question. If an active manager has been beating the market by tilting towards one of the factor premiums with high profitability and charging a high fee for the service, couldn’t investors alternatively capture better net returns systematically using lower cost funds with factor tilts?
In Berkshire Hathaway’s 2016 annual shareholder letter, Warren Buffett claims that active investors had thrown away over $100 billion trying to beat the market in the preceding decade. Buffett isn’t alone in this belief. Larry Swedroe and Andrew Berkin, authors of The Incredible Shrinking Alpha, share his perspective. What used to look like excess risk-adjusted return, or alpha is increasingly explained by exposure to additional systematic risks, or beta. As the academic research entered the investor marketplace, the curtain was open and both advisors and savvy individual investors started to take notice. Anyone could replicate the research of five factor premiums using free online tools such as Portfolio Visualizer to back-test portfolios and see that even simple index funds proved that the factors worked.
Building portfolios around multiple risk premiums both increase a portfolio’s expected returns and increase the reliability of its long-term returns because different risk premiums will show up at different times, offering a combination of diversification and lower risk adjusted returns.
Swedroe and Berkin argue that active management is a losing game that will only get tougher. Why? Because the pool of alpha (returns requiring special skill) is shrinking, and competition among skilled investors is heating up. This matters because active managers need to prove they can generate alpha to justify their higher fees compared to index funds, otherwise they lose their jobs.
So, to figure out an investor’s alpha accurately, it’s all about assessing the amount and type of predictable factor risk in their portfolio. For instance, imagine a US equity manager who outperforms the Russell 1000 index by adding highly volatile micro-cap stocks. Over time, this manager should see higher returns than a benchmark made up of large-cap equities because they’re investing in smaller, riskier companies. However, in this case, the manager didn’t actually generate any alpha, because the excess return is explained by the elevated predictable risk, or beta, in their portfolio.
In short, understanding alpha means looking beyond the numbers and considering the risks taken to achieve those returns.
It’s not that active fund managers are getting worse, it’s simply that as we get better at understanding statistical risk in markets, the amount of unexplained return, or true alpha, simply decreases. Swedroe and Berkin call this the “shrinking pool of alpha.” Interestingly, their book applies this concept to Warren Buffett’s impressive track record, suggesting that Berkshire Hathaway has actually generated a “statistically insignificant” amount of alpha. They back this up with academic research showing that Buffett’s outperformance can be chalked up to his exposure to risk factors like size, value, and leverage, rather than his stock-picking skills. However, the authors do give Buffett credit for having the foresight to identify those risk factors and invest in them before they became popular.
Swedroe and Berkin point out that investing is a zero-sum game. For every buyer and seller, somebody has to be on the short end of the deal. It’s often the non-professional, individual investors who lose. In other words, professional investors increasingly look for alpha by outsmarting “suckers” in the retail market.
So, who are the suckers? Well, it can be any of us regular folks picking stocks in the retail market. We might watch CNBC, doom-scroll through daily financial news, find stock picks in major publications, copy the investment patterns of famous investors, recommend hot market picks to our friends (that are already trending up on a speculative wave), and even brag about picking a few fairly obvious large-cap winners.
Unfortunately, this common pattern of retail investor behavior is risky and rarely beats the CAGR of the S&P 500 over the long haul. Timing the market is practically impossible. How do you know when to sell? Will you be tempted to sell in a panic? Can you resist the urge to bet on the next winner?
The problem with this style of retail investing is that it often leads to a lack of portfolio diversification. When Warren Buffett says in Berkshire press conferences things like, “You only need to pick a few good businesses a year,” he’s not suggesting that retail investors have the time or acumen to invest like Berkshire Hathaway. Berkshire boasts a massive and highly diversified portfolio and makes large, highly publicized moves into concentrated bets on businesses. Buffett doesn’t recommend stock picking for the average retail investor.
Published research by Antii Petajisto demonstrates that such concentrated stock positions usually contribute negatively to portfolio returns. Since 1926, the median ten-year return on individual U.S. stocks relative to the broad equity market is –7.9%, underperforming by 0.82% per year. For stocks that have been among the top 20% performers over the previous five years, the median ten-year market-adjusted return falls to –17.8%, underperforming by 1.94% per year.
In Petajisto’s graphs below, we can understand why investors fall prey to concentrated positions based on the diminishing distributions over time.
In factor investing terms, this might be called concentrated momentum. As far as factors go, it’s easy to spot when it’s big, but it’s a challenging factor premium to harness. It doesn’t scale well without the sophisticated tools that professional traders use, such as call and stop-loss options. Even professional day trading looks like gambling with leverage—usually the activity of young, amped-up, self-described “degenerates” skimming cream off the top of the proverbial butter churn, generating a lot of noisy trade volume with disregard for what is being bought or sold. Forty percent of day traders quit within a month, and only 13% remain after three years. Only 13% of day traders maintain consistent profitability over six months, and a mere 1% succeed over five years. It may be a tactic that makes money, but it’s not a smart investment strategy. It contributes nothing to price discovery and arguably adds no value to modern civilization.
Busybody retirees, the new population of Robin Hood retail HODLers, and particularly men are the vast majority of the population active fund managers exploit to gain alpha. Unfortunately, this “pool of victims” is shrinking. According to the Investment Company Institute, over the past ten to fifteen years US households have moved trillions of dollars from direct equity ownership (i.e., stock picking) into passively managed ETFs and mutual funds. As the amateurs leave the game, opting for the market average offered by passive funds and ETFs, professionals are left to make a living off the mistakes of the dwindling pool of suckers and other institutional investors. The problem is, institutional investors aren’t as prone to making mistakes.
In the end, this means active investors have to compete for a smaller pool of alpha with increasingly sophisticated competitors. The future of active management doesn’t look too bright.
Swedroe and Berkin end on an upbeat note. They suggest that the same changes making it harder for active managers to score alpha – better understanding of risk factors, huge amounts of money flowing into passive investments, and increased competition among managers – have actually been a big win for passive investors.
Nowadays, investors have an incredible range of options to achieve targeted risk exposures across various asset classes, and they can do so at a much lower cost than ever before. This is a game-changer for anyone looking to invest wisely.
Dimensional Fund Advisors and Vanguard have also led the way toward an empirically-driven approach of using a combination of the core market beta from indexes and filtering those stocks based on various factors to develop hybrid passive/active factor ETF funds that have a higher statistical chance of generating more reliable returns over the long term.
Furthermore, understanding the academic underpinnings of the Fama-French Factor Asset Price Model allows financial advisors and individual investors to build more reliable investment portfolios out of multiple funds to suit a specific customer’s lifestyle and emerging market conditions.
For example, backtesting shows us that during periods of high valuation, when the Shiller Cyclically Adjusted Profits to Earnings ratio, or CAPE ratio, is high, the large-cap market is broadly overpriced. A correction and reversion to the mean is inevitable. During such times, two factors reliably outperform the market: small-cap (size premium) and value (value premium). Thus, strategically rebalancing a portfolio with a “tilt” toward small-cap value index funds or more sophisticated factor funds has a better probability of beating the broad market benchmark, as evidenced by the majority of backtests of major market corrections in all available data.
But passive investing has potential downsides. Recent work by Mike Green suggests that, as passive investing in broad index funds increasingly accounts for over 40% of capital inflows, an unfortunate side effect is structural inelasticity. This amplifies value concentration and reduces market efficiency because fewer active traders are engaging in discretionary allocation and price discovery. For example, active fund managers may choose to hold their investors’ inflow in a cash or equivalent safe liquid position if they think the market is too expensive or there are no smart buys, whereas a passive management team must immediately buy the passive index at the current price, which arguably drives up prices in proportion to cap weight. This “passive bid” is a market distortion that has contributed to the bloated valuations of large cap stocks.
So what would our hypothetical Spock investor do? Who knows. He might refuse to invest altogether. Or perhaps he’d be a brilliant stock analyst and pick winners better than his human counterparts, or possibly even develop an advanced computer algorithm to predict the market. But assuming that Spock would shy away from high risk investment strategies, he’d probably perform a tremendous amount of statistical analysis and build a highly diversified portfolio of low-cost index funds, securities, real estate, commodities and perform deep research on businesses that are under-priced and poised to outperform.
