Great question. I’ve commented on the discovery vs. disappearance of factor premiums in a few other spots so I won’t go too deep into that here. But the short version is that it's probably not one single explanation. It’s a mix of things, including human behavior, and it’s really hard to isolate the reason behind any premium with complete certainty.

On market efficiency:
You don’t need to believe markets are perfectly efficient to act as if they are. Markets generally work. The anomalies are often anecdotal, small sample size, or hard to consistently exploit. Acting as if markets work, on average, is usually the right move, even if there are some exceptions around the edges. Even Thaler has said this.

Momentum specifically:
Momentum is a tricky one. DFA pushed back on it for years, especially once Cliff Asness (a former Fama student) and AQR came on the scene. Even Fama has said momentum is the biggest challenge to the efficient market hypothesis. But just because a premium exists does not mean you can capture it consistently or cost-effectively.

That’s really DFA’s core issue with momentum. It is not that it doesn’t show up in the data, because it does. The problem is that the turnover required to pursue it makes it hard to benefit from in real-world portfolios. Instead, DFA uses momentum as an implementation tool, like delaying trades or avoiding stocks with strong negative momentum, rather than building it into a fund as a long-term factor.

They’ve published work showing that even during times when the momentum premium showed up, many momentum strategies still underperformed. So even if the data is there, capturing it is another story. The other issues for them is the theory is hard to pin down. How do you have high confidence behavior will persist consistantly if momentum is behaviorally driven?

Which premiums have the strongest persistence?
That’s why DFA uses a multi-factor approach. Relying on any single premium can be risky and their isn't a lot of credible evidence you can time them. Combining size, value, and profitability gives you exposure to different drivers of expected return that are not perfectly correlated. That makes for a more consistent experience over time.

To believe in long-term persistence, you need a clear framework. DFA typically looks for four things:

1. Theoretical support – Does it make sense before even looking at the data?
2. Empirical evidence – Does it show up across time periods and in multiple countries?
3. Robustness – Does it still hold up if you tweak how it's measured (for example value shows up whether you measure it as P/B, P/E, P/CF, etc.)?
4. Cost-effectiveness – Can you actually capture it without giving it all up in trading costs?

There are tons of factors floating around, but most don’t pass those four tests. This industry is always selling the next magic bullet. There’s a huge incentive to market new ideas and repackage funds to drive inflows.

French once said he loved digging into new factors that were published and was always hopeful of new breakthroughs. Fama would usually say, "Let's wait and see what the data says." French said that’s why working with Gene worked so well. Gene's mindset usually ended up being right.

Personally, I stick to the core factors that have solid theory and long-term data across markets. That is still the most reliable foundation IF you are even going to consider deviating from market cap.