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.
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!