Why Evidence-Based Investing Can Improve Your Practice

The mutual fund industry is built on a web of deeply satisfying myths of superstar fund managers, benchmarks, quality fund families, quests for value and high-flying recent returns. An iconic television ad right that aired before the internet bubble burst showed Janus fund analysts inspecting underground fiber optic cables to literally dig up value for investors. Fund investors like a good story.

In academic finance, the story isn’t nearly as interesting. Common mutual fund narratives are relegated to the fiction section of the bookshelf. Why do financial scientists have such a different view of investing than many in the financial services industry?

Two innovations changed the way financial economists view the value of stock picking. The first was the advent of electronic computing, which allows scientists to study mountains of returns data. The second innovation was the Sharpe/Lintner capital asset pricing model (CAPM). Computers allow scientists to test whether markets efficiently priced securities based on the amount of risk that an investor couldn’t get rid of through diversification.

But, arguably, the most important innovation in the science of mutual fund analysis was the movement of a young scholar named Eugene Fama from romance languages into economics. Fama’s Ph.D. thesis in 1965 used the power of advanced computing to show that stock prices tended to follow a random walk. Fama also contributed to developing a new method of analyzing how stocks respond to new information that provided a window into the remarkable efficiency of financial markets.

Then in 1967 Fama’s graduate student, Michael Jensen, decided to see whether returns on mutual funds could be explained by the exciting new CAPM equation. Jensen simply tested if systematic risk explained the variation in fund performance.

Could fund managers provide a higher return than would be predicted based on their risk (or beta)? Could the best funds provide a consistently higher return than an investor could achieve by just holding a market portfolio with the same amount of risk?

Jensen found that mutual funds didn’t outperform the market as a whole. This wasn’t entirely surprising and doesn’t, on its own, endanger the mutual fund mythology. On average, fund managers don’t outperform the market.

But a good advisor can still identify the talented managers who will outperform in the future, right? Unfortunately, Jensen also found no evidence of fund persistence. Yesterday’s winners didn’t necessarily outperform in the future.

One would think that Jensen’s research debunking the myth of consistently outperforming fund managers (measured by alpha, also known as Jensen’s alpha) would strike a blow in the remarkably efficient marketplace for actively managed mutual funds. Fortunately for the investment management industry, while stock pickers tend to be ruthlessly efficient, fund investors are not.

A Scientist’s View of Investing

Scientists propose theories that are supposed to explain reality. The CAPM is a theory which suggests that average returns on financial assets are explained by beta, or the amount of systematic risk they exhibit compared to the market.

Scientists then test whether beta does in fact explain mutual fund returns. Generally it does, but sometimes it doesn’t. When theories don’t do a great job of explaining reality, then scientists propose new theories.

Maybe there is more to asset returns than just beta? Maybe investors prefer larger to smaller companies? Maybe investors tend to crowd into popular stocks and drive their prices up while leaving unpopular stocks to outperform?

These alternative theories of asset returns are also known as factors. The rise of factor-based investing stems from return anomalies observed by scientists over the years. This is the way science works. When a theory doesn’t do a perfect job of explaining reality, then scientists test new theories that help refine our knowledge of causation.

The original investing factors that, when combined with beta, do a consistent job of explaining fund performance over time are the size of the firm and the price investors are willing to pay for a dollar of profit (otherwise known as value). Investors have enjoyed a higher performance from choosing stocks from small firms with cheap prices.

The so-called Fama/French three-factor model has dominated academic finance for nearly three decades. Scientists found that many of the smartest fund managers were merely the best at identifying these factors before the academics were able to figure them out. Once they controlled for factors, the excess performance of these superstars faded away.

Scientists have since identified additional factors. Today’s winners also tend to outperform in the short run, a phenomenon known as momentum. Firms that trade less frequently tend to outperform, known as the liquidity effect. More profitable firms also tend to do better.

Evidence-based investing is about identifying the characteristics of stocks that have outperformed in the past, and building portfolio strategies that overweight stocks that have these particular characteristics.

It isn’t about finding good companies. It’s about finding companies that have the right factors. Instead of inspecting underground cables, a financial economist inspects whether a firm is small, cheap, profitable, or illiquid.

Evidence-Based Portfolios

In the recent book “The Incredible Shrinking Alpha” (an easy and essential read for any advisor), Larry Swedroe, director of research for Buckingham Strategic Wealth, and his co-author Andrew Berkin, director of research at Bridgeway Capital Management, lay out the convincing argument that the investment industry spends far too much time chasing any hint of Jensen’s alpha and not enough time paying attention to science.

This point has also been made by Eugene Fama’s frequent co-author Ken French, professor of finance at Dartmouth. In a 2008 presidential address to the American Finance Association, French estimated that over $100 billion is spent chasing elusive returns that cannot be explained by beta.

Swedroe and others point out that the actual alpha that can be captured by skilled investors is just a fraction of this amount. French estimates that 0.67% of the total stock market value is lost each year by investors who can’t let go of the active management story.

It appears that the ability to instantly make low-cost trades coupled with the remarkable flow of new information have further driven down opportunities for exploiting market mispricing. If it costs less than 10 basis points to fully capture beta, then fund managers needs to earn their keep if they are charging an additional 50 or 100 basis points to manage assets.

In other words, you could pay a fund manager $1,000 on a $1 million portfolio to capture beta. If you pay an additional $9,000 for something extra, is that a good investment? You’ve made a $9,000 bet on the story of managerial skill and you’re hoping for a good return on that bet.