The technique of factor investing entails a rules-based way of constructing portfolios around the characteristics that have historically helped explain returns. The approach I take with factor is investing is seeking the to answer the question:
“What are the consistently best qualities of a stock?”
A factor is the common return driver in those consistently best stocks. For example with value stocks, their characteristic or common return driver is that they are cheap relative to fundamentals. With momentum stocks, they are the recent winners. Quality companies are profitable, stable and conservatively financed. Low volatility stocks tend to move less than the market. Additionally, size tilts toward smaller companies. These are all tools in your tool kit.
What you should know is that not every factor beats the market every year. Thus long-term consistency is key. When combined properly, they can help investors build portfolios that are less dependent on a single index or one dominant theme.
Factor Investing began with the Capital Asset Pricing Model (CAPM). CAPM treated market beta as the primary driver of expected return. If a stock was more sensitive to the market, it was expected to have a higher return. In theory that made sense but it was impractical.
Then researchers started doing what they do best. Research. They began to find out that certain stock characteristics appeared to explain returns beyond beta. Eugene Fama and Kenneth French formalized this with the Fama-French 3-Factor Model. Those guys added size and value to the market factor. They created a model that said small cap stocks and high book-to-market value stocks were the characteristics that made portfolio returns better than with only beta.
Next was momentum. The Carhart 4-Factor Model added it, after realizing that recent winners tend to keep winning for a period, while recent losers tend to keep lagging. A little down the line the Fama-French-5-Factor Model included profitability and investment, recognizing that firms with stronger operating profitability and more conservative investment behavior also had explanatory power.
Currently, there are hundreds of factors. While this expansion can be useful to some, problems do arise as not every factor found is even real, investable or consistent.
Generally, factor investing has the following steps:
| Item | Step |
|---|---|
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Start with a liquid, investable pool of stocks like the S&P 500 Index. |
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Rank the stocks by valuation, price momentum, profitability, leverage, volatility, earnings quality or other chosen characteristics. |
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Clean the data, reduce outlier effects and make factor scores comparable. |
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Avoid accidentally turning a quality portfolio into a small cap portfolio or a momentum portfolio into a sector bet. You have to add some constraints. |
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Use equal weights, inverse-volatility weights, risk parity or optimization, depending on sophistication and mandate |
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Refresh slow-moving factors such as value and quality less frequently, while managing faster-moving factors such as momentum with cost-aware trading bands |
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Track drawdowns, turnover, crowding, sector bias, valuation spreads and tracking error. |
There may be extra steps or tweaks to the above but generally these are items used to build a factor portfolio.
With limited institutional tools, I align myself with the retail investors. However, with the tons of availability of data and access to basic tools such as ETFs and Index funds, factor investing has become significantly easier to do.
S&P Dow Jones Indices provide monthly data of various factor performance relative to the S&P 500.
Scatter plot from S&P Dow Jones Indices showing the 15-year annualized risk and return of S&P 500 factor indices as of April 2026
Tables comparing S&P 500 factor indices by total return and annualized volatility across different time periods.
Source: S&P Dow Jones Indices LLC
Through observing the scatter plots and the tables of total returns and volatility, there are 2 factors that have outperformed the S&P 500 Index over the last 15 years and by outperformed I mean these factors had a higher average rate of return and a lower annualized volatility than the S&P 500 Index. They are:
From there I identify the ETFs that track these factor indices. They can be easily found on the internet. Then I take a look at their holdings and pick the stocks that are in both of these ETFs.
From the last update the following stocks were selected.
| Ticker | Full Name | Sector |
|---|---|---|
| AAPL | APPLE INC | Technology |
| V | VISA INC CLASS A SHARES | Financial |
| MA | MASTERCARD INC A | Financial |
| PG | PROCTER + GAMBLE CO/THE | Consumer Staples |
| CSCO | CISCO SYSTEMS INC | Technology |
| LRCX | LAM RESEARCH CORP | Technology |
| MRK | MERCK + CO. INC. | Health Care |
| AMAT | APPLIED MATERIALS INC | Technology |
| KLAC | KLA CORP | Technology |
| GILD | GILEAD SCIENCES INC | Health Care |
| ACN | ACCENTURE PLC CL A | Technology |
| QCOM | QUALCOMM INC | Technology |
I then use my proprietary weighting methodology to weigh the stocks and rebalance my portfolio. The rebalancing is done on a monthly basis.
Indeed, this approach is practical, data-driven, and accessible. The combination of these factors along with a disciplined weighting methodology and rebalancing process, the portfolio is expected to stay invested in stocks that will outperform the S&P 500 Index over time.
Like any investment framework, factor investing works best when its strengths and limitations are understood from the beginning.
| Pros | Cons |
|---|---|
| Rules-based and transparent | Can suffer long periods of underperformance |
| Can improve diversification | Factor timing is difficult |
| Lower cost than many active strategies | Backtests can overstate real-world returns |
| Helps explain portfolio performance | Turnover and trading costs can erode returns |
| Can be implemented via ETFs and Index funds | Crowding can reduce future premia |
| Useful for risk management and attribution | Poor construction can create unintended factor exposures |
Indeed factor investing is very powerful, but there are limitations to it. It is better to understand it as structured exposure to compensated risks, behavioural inefficiencies and institutional frictions.
To limit the risks of factor investing you need to focus on well-researched factors with clear economic logic. Don’t go looking for exotic signals built mainly from data mining. Also, look beyond fund labels and actually review holdings, factor loadings, sector exposures, turnover, costs, and valuation risks to avoid any implementation drift and crowding.
Rebalancing discipline is also required. Trading bands, slower rebalancing, and tax-aware vehicles can help reduce costs drag. Patience is highly needed as well. You need to withstand tracking error and avoid abandoning a factor after a weak cycle, while using only modest adaptive tilts based on valuation, macro regime, factor momentum, and sentiment rather than relying too heavily on difficult to time factor rotation.
Factor investing won’t hand you easy alpha, and it may not even work in a single year. But if you stick with proven factors, keep costs tight, avoid chasing shiny signals and don’t bail during the rough patches, it’s a practical way to tilt the odds a bit more in your favour.
Cedric Thompson, CMT, CFA, is an investment strategist with experience in asset management, corporate strategy, and multi-asset investing.