IFA Charts: Fama French Five-Factor Model

What actually explains differences in stock returns over the long run? Not headlines. Not hunches. Data.

In 2014, Nobel laureate Eugene Fama and his colleague Kenneth French published a paper that identified five specific, measurable factors associated with differences in  stock returns — factors that have historically appeared  again and again, in market after market, decade after decade.

This table puts their model to the test across ninety-eight years of US data, and decades of international and emerging markets data. Let's walk through what it shows.

The five-factor model says returns are historically explained by five premiums:

Market — stocks versus safe T-Bills; Size — small companies versus large ones; Value — cheap stocks versus expensive ones; Profitability — highly profitable companies versus weak ones; and finally Investment — companies that spend conservatively versus those that spend aggressively.

Each row in this table isolates one of those five factors. Each column repeats the test in a different part of the world — US stocks, international developed markets, and emerging markets.

You'll notice the starting dates shift by region and by factor. That's not a selective choice — it reflects when reliable, investable index data first became available for each market. US market data reaches back to 1928. International index records begin in 1975, and emerging markets data begins in 1990. Profitability and investment weren't reliably tracked until the 1960s in the US and later abroad. Every period shown runs through December 31, 2025, the most recent complete year available.

If a premium is real — not a fluke of one country or one time period — it would be expected to appear across multiple marketrs. That's exactly what we're checking.

Start with the Market Premium — the reward for owning stocks instead of Treasury bills. In the US, from 1928 through 2025, that's been 6.70% a year. In international markets, from 1975 through 2025, 5.89%. In emerging markets, from 1990 through 2025, 5.68%. Three different markets, three different start dates based on data availability — and in every period measured, stocks meaningfully beat T-Bills.

Next, the Size Premium. From 1928 to 2025, small companies beat large companies by 2.39% a year in the US. From 1975 to 2025, that's 3.42% internationally. And from 1990 to 2025, a smaller 0.95% in emerging markets — a gap too small in this sample to rule out random noise — a reminder that premiums can vary in strength, even when the direction holds.

The Value Premium — cheap "value" stocks over expensive "growth" stocks — measured from 1928 to 2025, comes in at 3.09% in the US. From 1975 to 2025, 4.60% internationally. From 1990 to 2025, 4.95% in emerging markets. Look at the t-stats next to each number — 2.75, 3.65, 3.07

A t-stat above roughly 2 suggests the observed pattern is less likely to be attributable to random chance.

Profitability is the newest factor Fama and French added in 2014. Measured from 1964 to 2025, highly profitable companies have outpaced weak ones by 2.65% in the US. From 1991 to 2025, 2.06% internationally — right at the edge of what we can call statistically reliable. From 1992 to 2025, 3.36% in emerging markets — later start dates reflecting when profitability data first became reliably available in each region.

And the Investment Premium — companies that invest conservatively beating those that overspend — measured from 1964 to 2025 shows the widest range: 4.10% in the US. From 1991 to 2025, 3.42% internationally. From 1993 to 2025, a striking 6.82% in emerging markets.

Five factors. Three markets. Five separate observations, not just one.

This didn't happen by accident. In 2013, Fama received the Nobel Prize in Economic Sciences for his work on market efficiency — research that also helped establish the original three-factor model he built with French in 1993, covering market, size, and value. In 2014, they added profitability and investment because the data suggested those two factors captured patterns not fully explained by the original three.

What's remarkable is what they found next: once profitability and investment were added, the value factor became largely redundant for explaining returns in their sample. The model didn't just get bigger — it got more precise.

That's the mark of real science: not clinging to the original idea, but refining it as better evidence arrives.

So what does this mean for you? Every one of these premiums — size, value, profitability, investment — has historically been  targeted through  evidence-based portfolio strategies. Not by guessing which stock will win next quarter, but by tilting toward the entire class of companies that decades of global data have historically shown higher average returns.

That's the difference between investing on a hunch and investing on ninety-eight years of evidence, observed across three separate markets around the world.

The data doesn't guarantee tomorrow. But it tells you where the odds have stood for a very long time.

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DISCLOSURES:

The information presented is for educational purposes only and should not be construed as investment, tax, or legal advice, or as a recommendation to buy, sell, or hold any security. References to factor premiums, academic research, and historical returns are based on historical market data and research available through December 31, 2025, the most recent complete calendar year available at the time of production. Past performance does not guarantee future results. Historical relationships, factor premiums, and market trends may not persist in the future and may experience extended periods of underperformance. Investing involves risk, including the possible loss of principal. The views expressed are based on information believed to be reliable but are subject to change without notice. Artificial intelligence tools were used to assist with research, drafting, editing, and production of this content. All content was reviewed by humans prior to publication.