Beating The Market Is Simple, But Not Easy

Includes: RSP, SPY
by: Boris Marjanovic


Investors spend a great deal of time and effort trying to find the best stocks, but most still fail to beat the market.

This is most likely due to information overload -- humans can’t process large volumes of information and make rational decisions.

Simple statistical (or “quant”) models do a much better job at picking stocks and outperform even the best human experts.

Therefore, using a quant investment approach is the best solution when it comes to beating the market.

Why is it that most investors -- amateurs and professionals alike -- consistently underperform the market? One common explanation is that investors tend to trade too much; and to a certain extent, this makes a lot of sense. Excessive trading results in higher fees and taxes, which obviously has a negative effect on net returns. The empirical evidence also confirms this. A famous study analyzed over 35,000 individual brokerage accounts and found that less active investors (i.e., those with lower turnover) had significantly better net returns than their more active counterparts. So it does appear that less really is more when it comes to investing. No wonder Warren Buffett, in a play on Sir Isaac Newton's laws of motion, once said, "For investors as a whole, returns decrease as motion increases."

But, as this article will show, excessive trading isn't the only culprit that's causing investors to underperform the market. There are psychological factors in play here as well. As we're about to see, investors -- and all people in general -- are terrible at processing information and making rational decisions based on that information. We're so bad at it, in fact, that simple statistical or quantitative models significantly outperform even the best so-called experts. The models outperform because we humans are overconfident, biased, and unable or unwilling to change.

Experts Aren't that "Expert"

The pricing of wine is a classic demonstration of how simple statistics can outperform even the best human experts. The Princeton economist and wine lover Orley Ashenfelter wanted to predict the future value of fine Bordeaux wines from information available in the year they were made. He developed a simple equation based on just four factors: the age of the vintage, the average temperature over the summer growing season, the amount of rain at harvest-time, and the total rainfall during the previous winter. This simple model provides accurate price forecasts years and even decades into the future. In fact, the correlation between the model's predictions and actual prices is above 0.90. World-renowned wine experts can't even come close to that.

The Bordeaux wine example isn't unique, however. Statistical models tend to outperform humans in almost every other domain as well. In an extensive meta-analysis, researchers examined 136 studies of models versus human experts. The range of studies covered areas as diverse as criminal re-offense to occupational choice, diagnosis of heart attacks to academic performance. In all 136 studies, the experts had at least as much information as was used in the models and in some cases they had more information; hence, it would seem that the experts had an advantage. Yet, surprisingly, in only eight of the 136 studies did the experts outperform the models. All of these eight shared one trait in common -- the experts had an informational advantage. If the models had the same information they would have outperformed.

The common response to these findings is to argue that surely people should be able to use models as an input, with the flexibility to override them when required. However, the evidence suggests that models represent a ceiling in performance (from which we detract) rather than a floor (to which we can add). Several studies have shown that even when models' predictions were made available to the experts, they still managed to underperform the models! As the psychologist Philip Tetlock discovered, experts tend to develop an enhanced illusion about their skill and become unrealistically overconfident. This causes them to overweight their own opinions and experiences relative to those of the models, which ironically leads to their underperformance. As we'll see later, overconfident investors make the same mistake.

When it comes to building a predictive formula, we should heed the advice of Albert Einstein and "keep everything as simple as possible, but not simpler." Simple back-of-the-envelope formulas based on existing statistics or common sense are often very good predictors of significant outcomes. As we saw earlier, wine prices can be accurately predicted using less than a handful of readily available variables. In another memorable example, it's been shown that marital stability is well predicted by a formula: "frequency of lovemaking minus frequency of quarrels." You don't want your results to be a negative number! (I'm sure a lot of readers will know this from experience.) And as we're about to see, simple formulas also work well when it comes to stock selection.

The Rise of the Quants

Investors spend countless hours digging through SEC filings, talking with company executives, and building elaborate earnings models. But even after all of this hard work, most investors still fail to match, let alone beat, a simple index fund or ETF that tracks the market. How can this be? Information overload is the most likely answer. We humans have limits on our ability to process information. We simply aren't supercomputers that can carry out massive amounts of complex calculations in a fraction of a second. We have limited processing capacity. And if all that wasn't enough, we're also -- by our very nature -- biased and irrational creatures. It's these inherent flaws that often cause us to make poor decisions.

Consider, for instance, a study in which participants were asked to choose between four different cars. They were either given four attributes per car or 12 attributes per car. In both cases, one of the cars was noticeably better than the others, with some 75% of its attributes being positive. Two cars had 50% positive attributes and one car had only 25%. With only a low level of information, nearly 60% of subjects chose the best car. However, when faced with information overload (12 attributes to think about), only around 20% of subjects chose the best car. Other similar studies have also found the same results -- as more information is added, accuracy tends to decrease. Another common finding is that people's confidence increase with the amount of information available. This tells us that high subjective confidence (e.g., high conviction stock picks) is not to be trusted as an indicator of accuracy; if anything, low confidence could be more informative.

Choosing the right stocks is, of course, many orders of magnitude more difficult than choosing the right car. Thus, the optimal solution is to employ a statistical investment model to make the process easier (and more profitable). This is usually called "quant" investing. Benjamin Graham the "father of value investing" was the original quant. One of his most famous strategies was to systematically buy well diversified portfolios of "net-nets," or stocks trading for less than their net current asset value (current assets less all liabilities including preferred stock). In the decades following the Great Depression, this simple strategy worked well because bargains were easy to find. Graham reported that the average return, over a 30-year period, on diversified portfolios of net-net stocks was about 20% per year. Unfortunately, today's stock market is more efficient than it was in Graham's day, which explains why net-net stocks are now very rare (and the few that do exist are illiquid micro-caps that are difficult to trade).

Another quant investment strategy that's received considerable attention in recent years is Joel Greenblatt's "magic formula." It combines value (high earnings yield) and quality (high ROIC) to create a diversified portfolio of stocks that are the best of both worlds -- i.e., good companies at bargain prices. Researchers have conducted a number of studies on the strategy and found it to be a market beater, both domestically and abroad. The magic formula can even be slightly improved by ignoring the quality measure altogether and only focusing on valuation. This makes a lot of sense considering that valuation is one of the best predictors of long-term returns. A portfolio of stock sorted only on the cheapness metric achieves an astounding return of 16.3% per year and outperforms the two-metric (or original) magic formula by nearly 1% per year (and the S&P 500 by over 12%). As we can see below, each little bit of extra return -- when compounded year after year -- can add up to enormous differences.

Growth of $100 from January 1, 2000 to December 31, 2014

Note: (1) The magic formula rules are described in detail in Joel Greenblatt's book The Little Book That Still Beats the Market; (2) the only difference between the "adjusted" and "original" magic formulas is that the adjusted magic formula only uses earnings yield to rank stock, whereas the original uses both earnings yield and ROIC; (3) portfolios were equally weighted across 30 stocks and rebalanced annually; (4) annualized returns also include dividends.

Source: A North Investments ("ANI")

These are just a few simple examples of the types of quant investment strategies that any investor can implement, and there are many more. But in most cases, valuation-driven quant strategies work just as well, and often better, than fancier ones based on elaborate statistical models. Still, it's important to remember that no strategy works all the time. Even the best strategies experience periods of underperformance -- like, for instance, value investing during the 1990's dot-com bubble. (As a side note, the adjusted magic formula strategy failed to beat the market once every three years.) To put it simply, beating the market over the long run requires a lot of patience and persistence -- two characteristics that are in very short supply amongst today's investors.

Don't Try to Outsmart the Quant

To get the most out of quant models, the trading process must be automated to ensure a systematic and disciplined approach to investing that is unaffected by human emotion and judgmental biases. This will force investors to buy out-of-favor stocks they would normally tend to avoid -- and it's these stocks that usually turn out to be the biggest winners. Of course, this is easier said than done. Even with the simple magic formula, as Greenblatt found, many investors struggled to implement the strategy in practice. His investment firm offered two choices for retail clients to invest in stocks, either a "self-managed" account or a "professionally managed" account. The self-managed accounts had discretion over trading decisions, while professionally managed accounts were automated. Both choose from the same list of stocks recommended by the magic formula. So what happened?

Well, during the two year period under study, the self-managed accounts underperformed the professionally managed accounts by nearly 25% (and no, that's not a typo). Now again, remember, both account types chose investments from the same list of stocks and supposedly followed the same rules. This means that the people who "self-managed" their accounts took a winning system and used their overconfident judgment to unintentionally eliminate all the outperformance. But this isn't all that surprising. As you might recall, experts cross various domains also underperform simple statistical models, even when the models' predictions are made available to them. The moral: don't attempt to outsmart the quant model, you almost certainly won't succeed!

Quant ETFs

Non-market-cap-weighted (or "smart beta") ETFs are perfect for those looking for a simpler alternative to the quant investment strategies I outlined above. Most smart beta ETFs are weighted by fundamental factors such as dividends or earnings; others are equally weighted, meaning that each stock is held in equal dollar amounts, and, of course, each stock will have an equal influence on the daily change of the ETF.

One of my favorite smart beta ETFs is the Guggenheim S&P 500 Equal Weight ETF (NYSEARCA:RSP). As the name suggests, this is an equally-weighted ETF. There are two major benefits to equal weighting: (1) it provides better diversification because it avoids the top-heavy concentration that is a common problem with many cap-weighted ETFs; and (2) equally weighted ETFs systematically buy stocks that have underperformed and sells stocks that have outperformed -- a contrarian rebalancing approach that helps boost returns over time. The benefit of equal weighting is evidenced by the performance differential between the Guggenheim S&P 500 and the cap-weighted SPDR S&P 500 (NYSEARCA:SPY). Since its launch in April 2003, the Guggenheim S&P 500 has outperformed the SPDR S&P 500 by 3% per year -- not bad!


As the title of this article says, beating the market is simple, but not easy. It's simple because the entire quant investment process can be automated. But at the same time, it's difficult because it requires patience. All investment strategies, regardless of how good they are, go through periods of underperformance. Imagine diligently watching your portfolio each day as it does worse than the marker averages over the course of many weeks, months, sometimes even years. It's this periodic underperformance that causes emotional and impatient investors to lose faith and abandon such strategies. Don't be one of those investors. Beating the market can only be achieved if we go against our natural human instincts -- and while it's not easy, it definitely can be done!

Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it (other than from Seeking Alpha). The author has no business relationship with any company whose stock is mentioned in this article.