What Is Factor Investing? How It Works, and When It Stops Working

Factor investing means buying stocks for a trait you can measure instead of a forecast. The trait is called a factor. Cheapness is one. Recent price strength is another. Each has historically helped explain why one group of stocks beat another over long periods.

The rule does the picking. Nobody sits in a room deciding they like the CEO.

I build systematic models for advisers, so I spend a lot of time with rules like these. What follows is how a factor rule actually runs, with numbers, the five factors an adviser will meet, the dated stretches where each one failed, and how four fund managers from Behind the Ticker build around those failures.

What is factor investing, in plain terms?

Start with a universe of stocks. Score every name on one measurable trait. Buy the names that score highest. Rebalance on a fixed schedule and repeat.

The whole discipline is one rule, applied without discretion, for a long time.

The academic spine came from Eugene Fama and Kenneth French. Their 1992 paper showed that company size and the ratio of book value to price explained a large share of the differences in stock returns. In 1993 they turned that into the three-factor model: market exposure, size, and value. In 2015 they extended it to five factors by adding profitability and investment. Momentum entered the literature separately, through Narasimhan Jegadeesh and Sheridan Titman in 1993, and low volatility came from a long line of research showing that calmer stocks did not earn less than the theory said they should.

How does factor investing work, step by step?

Here is a value screen on six hypothetical stocks. The trait is book value divided by price. A higher number means you pay less for each dollar of book value.

The rule keeps the top third, which here is two names, and splits the money between them. A year later it scores all six again. Whatever falls out of the top two gets sold, and whatever climbs in gets bought.

Every factor product, however complicated its methodology document looks, runs some version of five steps.

1. Define the universe. Usually a broad index, like the S&P 500 or a set of developed-market stocks. 2. Define the metric. Book to price for value. Trailing twelve-month return for momentum. Return on equity and debt levels for quality. 3. Score and rank. Every name in the universe gets a number. 4. Select and weight. Take the top slice, often a decile or a quintile. Weight by score, by market cap, or equally. 5. Rebalance on a calendar. Monthly is common for momentum and annual for value. Faster rebalancing tracks the trait more closely and costs more to trade.

Notice what is missing. There is no step where the rule asks what the market is doing. A value screen in March 2020 looked exactly like a value screen in March 2019.

What are the main factors an adviser will run into?

- Value. Stocks that are cheap against book value, earnings, or cash flow.

- Size. Smaller companies over larger ones.

- Momentum. Stocks that have gone up over roughly the past year, usually skipping the most recent month.

- Quality. Profitable companies with low debt and stable earnings.

- Low volatility. Stocks whose prices move less than the market’s.

Each one has decades of published research behind it. Each one has also spent long stretches losing to a plain index fund.

The list is short on purpose. Rob Harvey, a vice president at Dimensional Fund Advisors, described the vetting problem on Behind the Ticker. "There are hundreds of factors or premiums, whatever you want to call it, that are out there in the market," he said, and a research team has to decide which ones deserve money. Dimensional held size and value for years before it added profitability to its portfolios in 2012. "We’re not chasing the flavor of the week," he said.

Two funds, one factor, different holdings

A factor label tells you the trait. It does not tell you how the trait is measured, and the measurement decides what you own.

Jon Clements, co-founder of Market Desk Research, gave the clearest example of this I have heard, about momentum. "Imagine two companies, right? They’re both up 30% over a six month period," he said. The first climbed slowly and steadily, gathering investors and news flow. The second got most of its gain in the last week of trading, on earnings. Most traditional momentum rules would rank those two equally, because they ended at the same place.

His team scores the path as well as the endpoint, and uses a six-month lookback instead of the academic standard of about twelve. Neither choice is the "right" momentum. They are two defensible definitions that will hold different stocks in the same month.

What does a factor failure actually look like?

Momentum in 2009 is the cleanest example on record, and the dates matter.

The S&P 500 closed at 676.53 on March 9, 2009. That was the bottom. A momentum rule on that date was doing what it was built to do. It ranked names by trailing price strength, so it held staples and defensive stocks and almost nothing in banks, homebuilders, or anything else that had been crushed over the prior eighteen months.

Then the market turned, and the most damaged names rose hardest. Kent Daniel and Tobias Moskowitz, whose paper on momentum crashes is the standard reference, measured it. From March through May 2009, the decile of past losers rose 163%. The decile of past winners rose 8%. By the end of June the S&P 500 was back near 919.

The signal was not broken. It fired correctly on a trailing window, and a sharp reversal is exactly what a trailing window cannot see.

Value ran the same trap over a much longer clock. From 2007 through 2020 it trailed growth in most years. 2020 was the widest gap of the run.

An adviser who bought a value fund in 2007 spent over a decade explaining it. Plenty of clients gave up after 2020. The ones who stayed were holding value in 2022, the year the S&P 500 fell about 25% from its January peak to its October low.

Why factor droughts last so long

Catherine LeGraw, an asset allocation strategist at GMO, offered a theory for value’s long run on Behind the Ticker. Through the long stretch of value underperformance, she said, investors and allocators stepped away. They moved to core or growth strategies. And she pointed to "the number of deep value managers who’ve gone out of business in the past five years, more than any other time in history" (at 14:10).

That is the uncomfortable mechanics of a factor premium. Part of the reward may be payment for holding something that looks wrong for years. If it felt comfortable, more money would own it, and prices would adjust before the rule ever fired.

The managers who run these strategies say this out loud. Elena Khoziaeva, co-CIO of Bridgeway Capital Management, runs a small cap value strategy and is direct about what that means. "When the two factors are out of favor and for a long period of time, we will tend to underperform. And that’s expected," she said. Earlier in the conversation she put the premium itself in terms of time: "The risk is still there. The returns are there. It’s just a matter of the time frame to capture that" (at 13:17).

Why practitioners combine factors

Cheap stocks are sometimes cheap for a reason, and a pure value screen cannot tell the difference. Momentum often can.

Harvey described what happened at Dimensional in early 2020. Airlines and cruise lines had been hammered, so on paper they looked like value stocks. "And the reason is because of momentum," he explained. Stocks that are falling hard against their peers have tended to keep underperforming, so the momentum signal kept those names out. "That’s a nice way for us to avoid value traps," he said.

Khoziaeva’s team does the same thing in small caps. They screen out negative momentum names and penny stocks, which she described as a way to remove "fallen knives," the deepest value names that keep going down (at 26:57). They also use several valuation measures instead of one ratio, which tilts the portfolio toward higher quality without a separate quality screen.

This is why most of the money in factor investing sits in multi-factor portfolios. Blending smooths the ride, because the factors tend to fail at different times, and it dilutes each one.

When does factor investing stop working?

Four failure modes come up again and again, and only one of them is about the research being wrong.

The holding period is longer than the client. Factor premiums are measured over decades. Advisory relationships get judged over quarters. A thirteen-year drought is survivable on a spreadsheet and not always survivable in a review meeting.

Crowding shrinks the premium. When a factor gets popular, more money chases the same names and prices adjust before the rule fires. LeGraw’s argument runs the same mechanism in reverse: when everyone leaves, the cheap names get cheaper.

Every provider defines the factor differently. Clements’s two stocks show how far apart two momentum funds can be. One quality fund screens on return on equity, another on earnings stability, another on accruals. Read the methodology document, not the fact sheet.

The rule answers the wrong question in a drawdown. A factor screen tells you what to own inside the equity sleeve. It never tells you how much equity to own. From February 19 to March 23, 2020, the S&P 500 fell 34% in five weeks. Long-only factor portfolios fell with it, because each one was fully invested by construction. The same happened in 2008.

That last one is the part advisers underestimate. Factor selection and risk posture are two separate decisions, and a factor fund only makes the first one.

How is factor investing different from smart beta?

Mostly they are the same idea under two names. Factor investing is the academic term for the underlying effect. Smart beta is the product term, and it drifted until it covered almost anything that was not cap-weighted. We wrote about how that happened in our piece on smart beta.

When you see either label, go to the methodology. The label tells you very little about what the fund holds.

Where a systematic risk overlay fits

THOR’s work sits next to factor investing rather than inside it. A factor rule scores what to own. An adaptive model scores how much to be invested, and shifts exposure when trend and volatility conditions deteriorate. The approach seeks to reduce drawdowns rather than pick the cheaper stocks.

Advisers often run both. A factor tilt inside the equity sleeve, and a systematic rule deciding the size of that sleeve. The low volatility factor is where the two overlap most directly, since lower-volatility names have tended to fall less in a selloff on their own.

What to ask before you buy a factor fund

- What is the exact metric? "Value" and "quality" are labels. Ask for the formula.

- How often does it rebalance, and what does that turnover cost?

- What was its longest stretch behind the market? If the manager cannot name it, the client will find out the hard way.

- What decides how much equity the client holds? The factor fund will not.

Definitions

Factor. A measurable stock characteristic that has historically helped explain differences in return across a universe.

Factor premium. The excess return a factor has delivered over a broad market index across a long measurement period.

Fama-French three-factor model. A 1993 framework explaining stock returns through market exposure, company size, and value.

Momentum. A factor that ranks stocks by trailing price strength. The window is usually twelve months, skipping the most recent one.

Value trap. A stock that screens as cheap and keeps falling, because the price was reflecting a real problem.

Crowding. The condition where wide adoption of a factor bids up its target names and compresses the premium.

Multi-factor fund. A fund that blends several factor screens, which smooths results and dilutes each one.

Educational content only. This is not investment advice and not a recommendation regarding any security. Index figures are cited as historical illustration, and the six-stock screen is hypothetical. Fund managers quoted are speaking about their own approaches on Behind the Ticker. Investing involves risk, including possible loss of principal. Past performance does not guarantee future results.