The Science of Investing

Though clearly not perfect, the finance professors' approach to investing has a lot going for it, particularly the notion of factors.

Samuel Lee 29.08.2013
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Though it sometimes is hijacked by ideologues, the scientific method works. The most successful societies entrust scientifically trained workers with the most specialized tasks, such as performing brain surgery, designing airplanes, and setting marketwide interest rates. And yet, many individuals regularly entrust their fortunes to the investing equivalents of witch doctors and astrologers. Or they take matters into their own hands for no good reason other than a vague belief that they can do it if they put their minds to it. Unlike good scientists, they're not skeptical enough of themselves or others.

Brains and education are no panacea. My father is a tenured professor of electrical engineering at one of South Korea's top research universities. When he designs a microchip, he draws on his years of education, consults industry journals, and relies heavily on the work of other engineers. When he speculates in small-cap technology stocks, his efforts are far more casual and sometimes include asking me which ones I like--I always profess ignorance. Despite admittedly subpar performance over 20-something years of investing, he refuses to give up control and index his holdings. It's as if the logical, skeptical part of his brain shuts down, and a more animal, overconfident part of his brain takes over in all matters investing. Would I, armed with nothing more than the efforts of a few spare hours each week, go into the chip-design business to compete with the likes of Intel and ARM? Of course not.

There is a science to investing. Though you may not know them by their technical names, chances are you're familiar with the fruits of Modern Portfolio Theory, the connection between risk and return, the theory of interest, and the efficient market hypothesis. Parts of financial theory are so integral to the practice of investing that most investors have forgotten they originated in academia. That said, some domains are more amenable to scientific expertise than others. The sweetest fruits of biomedicine originate from trained scientists; the best investment results don't always originate from finance Ph.D.s. In fact, some of the greatest investors are derisive of "scientific" approaches to investing. Warren Buffett warned, "Beware of geeks bearing formulas."

Why aren't finance professors dominant in investing? I can think of several reasons. The foremost reason is certainly emotion, which can consume even the finest minds. Famed logician Kurt Gödel was terrified of being poisoned and ate only food prepared by his wife, Adele. When she was hospitalized for six months, he starved himself to death. Even very smart investors can kill their retirement plans when they're in thrall to their animal brains. Investing requires unusual discipline that, by definition, most people lack. Moreover, this quality, in my experience, seems unrelated to brainpower.

Second, finance and economic researchers often don't have powerful enough tools to divine as much meaning from the available data as they'd like. That doesn't stop them from trying, though, and they often end up making astrologers look good by comparison. Not helping matters is how their work can have profound economic, social, and political implications, tempting researchers into the morass of politics, where their impartiality often withers and dies. Find me an economist who assumes nuanced positions that can't be neatly described as Democrat or Republican, and I'll show you someone who's practically irrelevant in the nation's political discourse.

Third, most of the information in the markets is not quantifiable with the tools at our disposal. By restricting themselves to hard numbers, scientific investors sometimes miss what qualitative investors see clear as day. Franco Modigliani, of the Modigliani-Miller theorem, was puzzled that so many firms paid dividends. Investors have known since the days of Ben Graham that dividends impose discipline on corporate managers, keeping them from doing too many stupid things. Soft qualities such as culture and incentives matter, even if you can't easily assign numbers to them or model them in a closed-form solution.

There are good reasons to be skeptical of the things that come out of finance researchers' mouths, but it's a big mistake to completely dismiss them. I'd go as far as to say evidence-driven investing is the best approach for the majority of investors, because it's based on an efficient learning strategy. Many investors pick a terrible learning strategy: personal experience. Experience is unreliable; colored by emotions and the zeitgeist, it captures a period that's short by the standards of history. Investors traumatized by the Great Depression learned that stocks are dangerous and should be avoided; investors who rode the bull markets of the 1980s and 1990s learned that stocks are unstoppable engines of wealth. Both learned the hard way that personal experience is a flawed teacher.

A better strategy is to learn from history, so you don't repeat the mistakes past generations made. Scientific investing, at its best, is about engaging the data honestly, with the intention of learning something new, hopefully something discordant with previously held beliefs. Science as it's currently practiced has plenty of flaws, but it's still the most reliable method of acquiring the truth that I know of.

What are the fruits of science as it pertains to investing? There's a lot of nonsense, but also a great deal of sense: Factors are important, and most investors should focus on investing in them.

Factor Investing
Unless you like to open the occasional dusty academic tome, chances are you're not intimately familiar with factor investing. It's really not as esoteric as it sounds. You've heard of style investing--small cap versus large cap, or value versus growth. If you've ever tilted to a particular style, you've engaged in factor investing. Style investing is a kind of factor investing, dealing with only two factors: size (large-small) and value (value-growth).

A working definition of a factor is an attribute of an asset that both explains and produces excess returns. Factor investing can be thought of as buying these return-generating attributes rather than buying asset classes or picking stocks.

None of this is new. The original factor theory, dating back to the 1960s, is the capital asset pricing model, or CAPM, which predicts that the only determinant of an asset's expected return is how strongly its returns move (or, in technical terms, covary) with the market's. The strength of the relationship is summarized in a variable called beta. A beta of 1 indicates that for each percentage point the market moves, an asset's price moves in the same direction by a percentage point. CAPM predicts asset returns are linearly related to market beta. However, since the 1970s, academics have known that stock returns don't seem to be related to beta. This finding spurred many fruitless or convoluted attempts to explain how market efficiency could be squared with a world in which CAPM didn't work.

Eugene Fama and Kenneth French "fixed" the CAPM, at least for stocks, by adding two factors: size and value. They observed that smaller stocks outperformed larger stocks and stocks with high book/market outperformed stocks with low book/market. More importantly, the relationships were smooth; the smaller or more value-laden the stock, the higher its return. Fama and French interpret the smoothness of the relationship as indicating the market is rationally "pricing" these attributes, which implies that size and value strategies enjoy higher expected returns for being riskier.

Further research has uncovered more stock factors, including momentum, quality, and low volatility, in nearly every equity market studied. They also display the same smooth relationship: The stronger the factor attribute, the higher the excess returns. The interpretation of these factors depends on whether you believe the market is efficient. In an efficient market, they must be connected to risk. However, if the market is not perfectly rational, some may represent quantitative strategies that exploit mispricings to produce excess returns.

I don't believe value, quality, momentum, and low-volatility strategies work because they are riskier. The strategies were exploited by investors before academics triumphantly published them in journals as "discoveries." It's also hard to reconcile them all as representing risk because if you lump them all together, you get an eerily smooth return stream.

This does not mean that all factors earn profits by identifying mispricings. Some attributes, such as illiquidity, are associated with higher returns because they obviously represent risk. So factor investing encompasses two different approaches:

Rational factor theory, which deals with the rewards that accrue to different types of risk and how the market prices them. Factor investing in this context is about finding the optimal portfolio of factor risks.

Factor investing as commonly understood by practitioners, which is the identification of simple quantitative strategies associated with excess returns.

Though it's been around for decades, factor investing has only in the past decade gained adherents. Recent converts include the Government Pension Fund of Norway, the biggest pension fund in Europe, and CalPERS, the biggest public pension fund in the United States. They've seen the light after realizing that the active managers they were richly compensating were simply offering factor risks and factor-based strategies under the guise of skill.

Redefining Alpha
An implication of factor-based investing is that what was once legitimately deemed "alpha"--excess returns attributable to skill--has morphed into "beta" (or a factor) once researchers identify a simple strategy that replicates the alpha. For instance, certain hedge fund managers in the 1980s and 1990s pursued then-exotic strategies such as merger arbitrage that produced excellent returns uncorrelated to the market. However, once researchers identified how the arbitrage strategies worked and created mechanical replications, the managers' alpha became beta.

A consequence of this process is that the hurdle for being declared a truly skilled manager has risen over time. In the 1980s, it was good enough to beat your benchmarks. These days, studies looking for evidence of skill in equity mutual funds control for exposure to size, value, and momentum factors. In other words, if your excess returns come during times that value, smaller-cap, or momentum stocks outperform, the procedure will adjust your "excess" return to zero and declare you unskilled.

If you believe the excess returns of value and momentum strategies reflect risk, then it's a reasonable adjustment. If you believe value and momentum produce excess returns because of market inefficiency, then it's not--what you've done is redefine outperformance. I'm of the latter view. From my perspective, the mountains of studies purporting to show that active equity managers can't beat the market are really showing that much of their excess returns can be replicated by a handful of factor strategies.


Samuel Lee is an ETF strategist with Morningstar and editor of Morningstar ETFInvestor.

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Samuel Lee  Samuel Lee is an ETF strategist with Morningstar and editor of Morningstar ETFInvestor

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