Factor Exposure

Why this matters

Contrarian and Factor Investing introduced factors and the funds built on them. This lesson goes further in two directions. It looks at the record year by year, because an average premium tells you little about what owning it is like. And it shows how to find the factor exposures in a portfolio you already hold, because every portfolio has them. A fund of large technology companies and a fund of small banks are both "shares", and they carry very different tilts.

From one factor to several

Alpha and Benchmark Selection split a return into the reward for market risk and a leftover called alpha. A factor model adds more terms before the leftover.

Return = Risk-free rate + Market exposure × Market premium + Size exposure × Size premium + Value exposure × Value premium + Alpha

Each exposure works like beta: it's how many points the portfolio has tended to move for each point the factor moved. A broad index fund has a market exposure of 1 and size and value exposures near zero. Whatever isn't explained by the factors is alpha. What looks like a manager's skill against the market alone can turn out to be a steady tilt toward small or cheap companies.

How a factor is built

A factor is measured as the return of one group of shares minus the return of the opposite group. The best known are the three from Eugene Fama and Kenneth French, who found that size and the ratio of book value to market value captured much of the variation in average US share returns.¹ French publishes them, built as follows.²

• Market: the return on all US shares, weighted by value, minus the one-month Treasury bill rate.
• Size, called SMB for "small minus big": the average return on portfolios of small companies minus the average return on portfolios of big ones.
• Value, called HML for "high minus low": the average return on portfolios of value shares, those with a high book value compared with their price, minus the average return on portfolios of growth shares.

Each of the last two is a long-short portfolio: it owns one group and bets against the other. That matters later. An ordinary fund can own small or cheap shares, and it can't bet against the rest.

What the factors have paid

These are arithmetic averages of paper portfolios, before trading costs, fees, and taxes.²

Both premiums are positive over the century. Both are also small next to their own volatility. The size premium was negative in half of all years. Its standard error is 13.3% ÷ √99 = 1.3%, so an average of 2.5% is less than two standard errors from zero: by the usual test, 99 years haven't established that it exists. The value premium, at 4.2% with a standard error of 1.5%, passes that test.

The averages hide how the premiums arrived.

The first two rows cover the years before the research was published, and the third the years since.² In the third, the size premium is essentially nothing. The last row is the one to study. For 14 years, value shares lost to growth shares by an average of 6.7 points a year, and were behind in 10 of the 14. In 2020 alone the value factor lost 46.9%. Over the 20 years from 2006 through 2025, its average was still negative, at −2.2% a year. From 1984 through 2025, 42 years, the size factor averaged −0.8% a year.

Nobody can say from this whether the premiums have gone or are merely late. Researchers have long disagreed about why such patterns exist at all: as compensation for bearing risk, or as the result of investors' mistakes.³ If it's the first, a premium should persist, with painful stretches as the price of earning it. If it's the second, it can fade once enough money chases it. An investor has to choose a tilt without knowing which is true.

The rest of this lesson is for members

Still to come in this lesson: Finding the exposures in a fund, Factor funds, Deciding whether to tilt, the key takeaways, and 2 quiz questions with explanations.

Membership opens every lesson in the curriculum, the capstone projects, the interactive tools, and the members-only channels in the Hyperion Entities Discord. The first lesson of every module stays free.

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