Factor Investing
Selecting stocks by measurable shared characteristics such as value, size or momentum rather than by judging each company.
What the strategy is
Factor investing holds a broad set of stocks chosen by a measurable characteristic that has historically been associated with different average returns, rather than by an opinion about any individual company. It sits between index investing and stock picking: the selection rule is explicit and mechanical, but it is not the market.
How it works
The investor or fund defines a characteristic that can be computed for every stock, such as a low price relative to book value, a small market capitalisation, a strong recent price trend or high profitability. Every company in the universe is scored on that measure, and the portfolio holds the ones that score highest, weighted by the rule rather than by conviction.
The portfolio is rebalanced on a schedule so it keeps holding the current members of the group rather than the ones that qualified when it was built. This matters most for characteristics that change quickly. A momentum portfolio must trade frequently to remain a momentum portfolio; a value portfolio changes more slowly but still turns over as prices move.
Nobody looks at the individual companies. The claim being made is statistical: across hundreds of holdings the characteristic is expected to show up in the average, and any single company in the portfolio may be there for reasons that would not survive inspection. That is the deliberate design and it is also what makes the strategy uncomfortable to hold, since the portfolio will always contain businesses the investor would never choose one at a time.
Advantages
- The rule is written down in advance, which makes the strategy auditable. When it underperforms, the investor can see exactly which decision produced the result rather than guessing.
- It removes company-level judgment and the biases that come with it, including the tendency to prefer familiar names and compelling stories.
- The characteristics rest on decades of published research rather than on assertion, and the underlying data is public enough that the claims can be checked independently.
- It can be run cheaply at scale through funds, so an approach that once required a research department is available at a cost much closer to an index fund than to an active one.
Disadvantages
Stated at the same length as the advantages, because a strategy page that only lists upsides is marketing.
- The evidence is historical, and any characteristic identified in past data may weaken once enough money is invested in exploiting it.
- Underperformance can last a very long time. Value trailed growth for roughly a decade after 2007, which is longer than most investors will hold a strategy they cannot see working.
- Factors overlap and can conflict. A value screen and a momentum screen frequently want opposite positions, and combining them can leave a portfolio that expresses neither clearly.
- Higher turnover than indexing means more trading cost and, in a taxable account, more realised gains. Momentum is the worst case and can trade a large part of the portfolio each year.
- The published research is vulnerable to selection. Characteristics were found by searching data, and a portion of what has been reported over the years has not held up when tested on new periods.
Who typically uses it
- Systematic asset managers who build portfolios from rules rather than from analyst recommendations, which is the setting the approach was developed in.
- Institutional allocators using factor exposures to describe and control what their overall portfolio is actually tilted toward, often across several managers.
- Individual investors, usually through funds sold as smart beta or factor ETFs, which package a single characteristic or a blend of several.
- It fits poorly for anyone who will judge it over a few years. The claim is about long-run averages, and a holding period shorter than the periods over which factors have failed does not test the claim, it tests luck.
Historical examples
Specific, checkable episodes rather than illustrations, including the ones where the strategy cost money.
Fama and French, 1992 and 1993
Eugene Fama and Kenneth French published work showing that company size and the ratio of book value to market price explained differences in average stock returns that the prevailing single-factor model did not. Their three-factor model became the standard reference point, and they extended it to five factors in 2015 by adding profitability and investment. Fama shared the Nobel Memorial Prize in Economic Sciences in 2013, and French continues to publish the underlying return series publicly, which is why these particular claims can be checked by anyone.
Momentum, the characteristic that should not exist
Narasimhan Jegadeesh and Sheridan Titman documented in 1993 that stocks which had performed well over the preceding months continued to do so on average over the following months. The finding is awkward for efficient market theory because it is a pattern in past prices alone, and it has persisted in out-of-sample tests across markets and decades. It is the clearest example of a factor with strong evidence and no agreed explanation.
The value drawdown after 2007
Value stocks underperformed growth stocks for roughly a decade after 2007, a stretch long enough that a serious argument developed about whether accounting-based value measures had stopped capturing what they used to, given how much corporate value had moved into intangible assets that balance sheets do not record. Value returned strongly in 2021 and 2022. The episode is the standing illustration of how long an investor may have to hold a factor strategy that is not working before finding out whether it still does.
Risks
- Decay risk. A characteristic that is widely known and widely traded may deliver less than it did when it was obscure, and there is no way to observe this happening in real time.
- Data mining risk. Many published factors were found by searching historical data, and a meaningful share have failed to replicate on later periods or in other markets.
- Tracking risk. A factor portfolio is not the market and will spend long periods behind it, which is a portfolio risk and, more often, a behavioural one.
- Implementation risk. Turnover costs, the price impact of trading and the specific rules a fund uses can consume a large part of the difference the research described.
Common mistakes
- Switching factors after a period of underperformance, which systematically sells the one that is cheap and buys the one that has just worked.
- Holding several factor funds that cancel each other out, leaving a portfolio that looks like the market and costs more than it.
- Assuming factors are additive, so that holding five is five times better. Correlations between them mean the combined exposure is usually far less than the parts suggest.
- Running momentum in a taxable account without accounting for the turnover, which can hand a large share of the difference to tax.
- Reading a factor's long-run average as something that arrives steadily. The averages are built from periods of strong performance separated by long stretches of nothing.
Common misconceptions
- The claim
Factor investing means the market is inefficient.
What is actually the caseBoth explanations are live and the researchers themselves disagree. One reading is that factors are compensation for a genuine risk, which is consistent with efficient markets. The other is that they reflect persistent behavioural errors. Momentum is hardest to fit into the first explanation, which is why it remains the most argued about.
- The claim
Factors are certain to reward patience over a long enough period.
What is actually the caseNothing makes that certain. A factor can weaken permanently once exploited, or turn out to have been a statistical artefact. The honest position is that several characteristics have persisted across decades and markets, that this is meaningful evidence, and that it is not a promise.
- The claim
Smart beta is a different thing from factor investing.
What is actually the caseSmart beta is largely a marketing term for index-like funds that weight by a characteristic instead of by market capitalisation. The mechanics are the same as factor investing; the label tends to signal how the product is sold rather than how it is built.
Investors associated with this strategy
Listed because of a documented connection to the approach, not because they are well known.
In their words
“Size and value characteristics help explain differences in average stock returns.”
Context: The Fama-French finding that company size and valuation explain returns a single market factor does not.
“I take the market efficiency hypothesis to be the simple statement that security prices fully reflect all available information.”
Context: Fama's statement of the efficient-market hypothesis, the idea behind the case for index funds.
“A strategy that works most of the time but not all of the time is exactly what you should expect.”
“Investing is about having a strategy you can stick with, not the theoretically best one.”
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Frequently asked questions
What is a factor in factor investing?
A measurable characteristic shared by many companies that has historically been associated with different average returns. The most studied are size, value, momentum, profitability and low volatility. What makes something a factor rather than an observation is that it can be computed for every stock, applied consistently and tested on data the researcher did not choose.
Which factors have the strongest evidence behind them?
Value, size, momentum and profitability have the longest records and have been tested across many markets and decades. Momentum has the most consistent evidence and the weakest agreed explanation, which is an uncomfortable combination. Many other published factors have failed to replicate outside the data they were found in.
How is factor investing different from stock picking?
Stock picking makes a claim about a particular business after examining it. Factor investing makes a statistical claim about a large group selected by a rule, and never examines the members. A factor portfolio will always contain companies its own holder would reject individually, and removing them would break the strategy.
Why did value investing underperform for so long?
Value trailed growth for roughly a decade after 2007. The most substantive explanation offered is that traditional measures like book value capture the assets of an industrial company well and the assets of a software or pharmaceutical company badly, because research and brands are expensed rather than recorded. Whether that permanently weakens the measure or was a long cycle is still argued.
Can factor investing be combined with index investing?
It is commonly done, most often by holding a broad market fund and adding a tilt toward one or two characteristics. The main thing to check is overlap: a large-cap growth tilt added to a market-cap weighted index fund frequently doubles an exposure the index already carries rather than adding anything new.
How long does it take to know whether a factor strategy is working?
Longer than most investors are prepared to wait. Factors have gone a decade without delivering, so a three or five year assessment cannot distinguish a broken strategy from a normal bad stretch. That is a genuine problem for the approach rather than a defence of it, because in practice the answer arrives after the decision to abandon it has been made.
Sources
Where the dates, figures and claims on this page come from. Book and paper citations carry no link because the durable reference is the title rather than any one copy of it.
- Eugene Fama and Kenneth French, The Cross-Section of Expected Stock Returns, Journal of Finance, 1992
- Eugene Fama and Kenneth French, Common Risk Factors in the Returns on Stocks and Bonds, Journal of Financial Economics, 1993
- Kenneth French, data library of factor return series
- Nobel Memorial Prize in Economic Sciences 2013, Fama, Hansen and Shiller
- Narasimhan Jegadeesh and Sheridan Titman, Returns to Buying Winners and Selling Losers, Journal of Finance, 1993
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