Originally the Fama-French model only had 3 fundamental risk factors. If things don’t quite work out after the first 3, it seems awfully ad-hoc to just find 2 more and then add them to the back. There also seems to be a belief in academia that getting higher risk adjusted returns through analysis of company fundamentals is more possible than getting them through historical price data.
I’m a bit confused here—the core Fama-French insight is that if a given segment of the market have a large common correlation, then it’ll be under-invested in by investors constrained by a portfolio risk budget. In this framework, I think it’s perfectly valid to identify new factors as the research progresses.
(1)
As a toy example, say that we discover all the stocks that start with ‘A’ are secretly perfectly correlated with each other. So, from a financial perspective, they’re one huge potential investment with a massive opportunity to deploy many trillions of dollars of capital.
However, every diversified portfolio manager in the world has developed the uncontrollable shakes—they thought they had a well-diversified portfolio of 2600 companies, but actually they have 100 units of general-A-company and 2500 well-diversified holdings. Assuming that each stock had the same volatility, that general-A position quintuples their portfolio variance! The stock-only managers start thinking about rotating As into Bs through Zs, and both the leveraged managers and the stocks+plus+bonds managers think about how much they’ll have to trim stock leverage, and how much is that should be As vs the rest...
Ultimately, when it all shakes out, many people have cut their general-A investments significantly, and most have increased their other investments modestly. A’s price has fallen a bit. Because A’s opportunities to generate returns are still strong, A now has some persistent excess return. Some funds are all-in on A, but they’re hugely outweighed by funds that take 3x leveraged bets on B-Z, and so the relative underinvestment and outperformance persist.
(2)
In this case, the correlation between the A stocks is analogous to an extreme French-Fama factor (in the sense the original authors mean the term). It “predicts higher risk-adjusted returns”, but not in a practically exploitable way, because the returns go along with a “factor”-wide correlation that limits just how much of it you can take on, as an investor with a risk budget.
If you could pick only one stock in this world, you would make it an A. Sure. But any sophisticated portfolio already has as much A as it wants, and so there’s no way for them to trade A to eliminate the excess return.
(3)
And in this universe, would it be valid for Fama and French to write their initial model, notice this extra correlation (and that it explains higher risk-adjusted returns for A stocks), and tack it on to the other factors of the model? I think that’s perfectly valid.
It seems ad hoc to me because they continue to add “fundamental” factors to their model, instead of accepting that the risk-return paradox just existed. Why accept 5 fundamental factors when you could just accept one technical factor?
Suppose that in 20 years we discover that although currently in 2021 we are able to explain the risk-return paradox with 5 factors and transaction costs, the risk-return paradox still exists despite 5 factors in this new out of sample data from the future. What do we do then? Find 2 more factors? Or should we just conclude that the market for the period of time up until then was just not efficient in a weak-form sense?
I think of the Fama-French thesis as having two mostly-separate claims: (1) correlated factors create under-investment + excess return, and (2) the “right” factors to care about are these three—oops five—fundamentally-derived ones.
Like you, I’m pretty skeptical on the way (2) is done by F-F, and I think the practice of hunting for factors could (should) be put on much more principled ground.
It’s worth keeping in mind, though, that (1) is not just “these features predict excess returns”, but “these features have correlation, and that correlation arrows excess returns”. So it’s not the same as saying there’s a single excess-return factor, because the model has excess return being driven specifically by correlation and portfolio under-investment.
Example: In hypothetical 2031, it feels valid to me to say “oh, the new ‘crypto minus fiat’ factor explains a bunch of correlated variance, and I predict it will be accompanied by excess returns”. The fact that the factor is new doesn’t mean its correlation should do anything different (to portfolio weightings, and thus returns) than other correlated factors do.
I also don’t think the binary of “the risk-return paradox exists” vs “the market is efficient in a weak-form sense” is a helpful way to divide hypothesis-space. If there’s a given observed amount of persistent excess return, F-F ideas might explain some of it but leave the rest looking like inefficiency. The fact that some inefficiency remains doesn’t mean that we should ignore the part that is explainable, though.
I think I would agree with you that if you could really find the “right” factors to care about because they capture predictable correlated variance in a sensible way, then we should accept those parts as “explainable”. I just find that these FF betas are too unstable and arbitrary for my liking, which is a sentiment you seem to understand.
I focus so much on the risk-return paradox because it is such a simple and consistent anomaly. Maybe one day that won’t be true anymore, but I’m just more willing to accept that this phenomenon just exists as a quirk of the marketplace than that FF explains “part of it, and the rest looks like inefficiency”. FF could just as easily be too bad a way to explain correlated variance to use in any meaningful way.
Reasonable beliefs! I feel like we’re mostly at a point where our perspectives are mainly separated by mood, and I don’t know how to make forward progress from here without more data-crunching than I’m up for at this time.
I’m a bit confused here—the core Fama-French insight is that if a given segment of the market have a large common correlation, then it’ll be under-invested in by investors constrained by a portfolio risk budget. In this framework, I think it’s perfectly valid to identify new factors as the research progresses.
(1) As a toy example, say that we discover all the stocks that start with ‘A’ are secretly perfectly correlated with each other. So, from a financial perspective, they’re one huge potential investment with a massive opportunity to deploy many trillions of dollars of capital.
However, every diversified portfolio manager in the world has developed the uncontrollable shakes—they thought they had a well-diversified portfolio of 2600 companies, but actually they have 100 units of general-A-company and 2500 well-diversified holdings. Assuming that each stock had the same volatility, that general-A position quintuples their portfolio variance! The stock-only managers start thinking about rotating As into Bs through Zs, and both the leveraged managers and the stocks+plus+bonds managers think about how much they’ll have to trim stock leverage, and how much is that should be As vs the rest...
Ultimately, when it all shakes out, many people have cut their general-A investments significantly, and most have increased their other investments modestly. A’s price has fallen a bit. Because A’s opportunities to generate returns are still strong, A now has some persistent excess return. Some funds are all-in on A, but they’re hugely outweighed by funds that take 3x leveraged bets on B-Z, and so the relative underinvestment and outperformance persist.
(2) In this case, the correlation between the A stocks is analogous to an extreme French-Fama factor (in the sense the original authors mean the term). It “predicts higher risk-adjusted returns”, but not in a practically exploitable way, because the returns go along with a “factor”-wide correlation that limits just how much of it you can take on, as an investor with a risk budget.
If you could pick only one stock in this world, you would make it an A. Sure. But any sophisticated portfolio already has as much A as it wants, and so there’s no way for them to trade A to eliminate the excess return.
(3) And in this universe, would it be valid for Fama and French to write their initial model, notice this extra correlation (and that it explains higher risk-adjusted returns for A stocks), and tack it on to the other factors of the model? I think that’s perfectly valid.
It seems ad hoc to me because they continue to add “fundamental” factors to their model, instead of accepting that the risk-return paradox just existed. Why accept 5 fundamental factors when you could just accept one technical factor?
Suppose that in 20 years we discover that although currently in 2021 we are able to explain the risk-return paradox with 5 factors and transaction costs, the risk-return paradox still exists despite 5 factors in this new out of sample data from the future. What do we do then? Find 2 more factors? Or should we just conclude that the market for the period of time up until then was just not efficient in a weak-form sense?
I think of the Fama-French thesis as having two mostly-separate claims: (1) correlated factors create under-investment + excess return, and (2) the “right” factors to care about are these three—oops five—fundamentally-derived ones.
Like you, I’m pretty skeptical on the way (2) is done by F-F, and I think the practice of hunting for factors could (should) be put on much more principled ground.
It’s worth keeping in mind, though, that (1) is not just “these features predict excess returns”, but “these features have correlation, and that correlation arrows excess returns”. So it’s not the same as saying there’s a single excess-return factor, because the model has excess return being driven specifically by correlation and portfolio under-investment.
Example: In hypothetical 2031, it feels valid to me to say “oh, the new ‘crypto minus fiat’ factor explains a bunch of correlated variance, and I predict it will be accompanied by excess returns”. The fact that the factor is new doesn’t mean its correlation should do anything different (to portfolio weightings, and thus returns) than other correlated factors do.
I also don’t think the binary of “the risk-return paradox exists” vs “the market is efficient in a weak-form sense” is a helpful way to divide hypothesis-space. If there’s a given observed amount of persistent excess return, F-F ideas might explain some of it but leave the rest looking like inefficiency. The fact that some inefficiency remains doesn’t mean that we should ignore the part that is explainable, though.
I think I would agree with you that if you could really find the “right” factors to care about because they capture predictable correlated variance in a sensible way, then we should accept those parts as “explainable”. I just find that these FF betas are too unstable and arbitrary for my liking, which is a sentiment you seem to understand.
I focus so much on the risk-return paradox because it is such a simple and consistent anomaly. Maybe one day that won’t be true anymore, but I’m just more willing to accept that this phenomenon just exists as a quirk of the marketplace than that FF explains “part of it, and the rest looks like inefficiency”. FF could just as easily be too bad a way to explain correlated variance to use in any meaningful way.
Reasonable beliefs! I feel like we’re mostly at a point where our perspectives are mainly separated by mood, and I don’t know how to make forward progress from here without more data-crunching than I’m up for at this time.
Thanks for discussing!