A Facebook friend of mine forwarded me a video clip of Gene Fama’s latest interview on Bloomberg with Barry Ritholtz. I watched the full interview just now. And it occurs to me that I might have a few relevant things to say about the important issues discussed in the interview.

The high point (or low point, depending on whom you ask) of the interview is when Gene says:
 
“There is no behavioral finance. It’s all just a criticism of efficient markets, with no evidence.”
 
Gene went on to clarify that 20 years ago, he challenged behavioral finance to come up with a theory that people can take to the data and test (Fama 1998). Fast forward to 20 years later, we are still waiting.

Gene’s statement has caused an uproar on the internet. In particular, Dick Thaler responded with the following tweet:

To no one’s surprise, I find myself mostly in agreement with Gene. I would drop the bit “with no evidence” but I find Gene’s statement to be accurate. The case for behavioral finance is extremely weak.

On the theoretical side, it has been almost 35 years since De Bondt and Thaler (1985), yet there is still not a single coherent behavioral theoretical framework in sight, not to say its structural estimation that takes the theory to the data. Ideally, one would like to identify exactly what psychological biases are in play, the specific mechanisms via which the biases impact on equilibrium prices, and formal econometric estimation and tests to quantify the mechanisms in the data. Sure, there have been sporadic theoretical efforts, but the resulting models are typically so ad hoc (and so disconnected among themselves) that they have mostly been tested in an informal, reduced form way.

On the empirical side, EMH says that pricing errors are not forecastable. I view EMH as equivalent to the Muth-Lucas rational expectations, which says that forecasting errors are not forecastable (a hat tip to my Wharton advisors' long-lasting influence on my work).

Now, for the rubber to hit the road, one has to bundle EMH with an expected return model to isolate “pricing errors” or abnormal returns. The empirical literature has traditionally adopted the classic CAPM for this purpose and found its empirical performance to be lacking. The consumption CAPM performs even more poorly and remains mostly in theoretical papers on narrower topics. The Fama-French 3-factor model took over in 1993 as the workhorse empirical model, but the interpretation of SMB and HML is unclear. In all, the contribution of behavioral finance, which I acknowledge to be enormous, is to demonstrate the empirical failures of the (consumption) CAPM. However, the evidence does not reject EMH, at least not directly (the ubiquitous joint-hypothesis problem).

During the past 20 years, instead of behavioral finance, it is investment-based asset pricing that has risen up to meet Fama’s (1998) challenge. And this newer literature has done so within the scope of EMH.
 
The key insight of the investment literature is to price risky assets from the perspective of their suppliers (firms), as opposed to their buyers (investors) (Zhang 2017). Buyers are heterogeneous in preferences, beliefs, and information sets, all of which make the buy-side pricing exceedingly difficult. This statement just reflects the classic, largely intractable aggregation problem in equilibrium theory (the Sonnenschein-Mantel-Debrew theorem). Who’s the marginal investor of Apple Inc.? Your guess is as good as mine.

On the supply side, who’s the marginal supplier of Apple Inc.? Well, easy, that’s Apple Inc. No aggregation difficulty even remotely in the same magnitude as that plaguing the (consumption) CAPM (or any other demand-based theories). As a new class of Capital Asset Pricing Models, the investment CAPM arises from the first principle of real investment for individual firms. Building on the first principle, the investment CAPM is every bit as rigorous as any economic theory that I am aware of, including, in particular, the consumption CAPM.
 
The academic literature has been evaluating the empirical, explanatory power of the investment CAPM in the past decade. The evidence so far seems rather encouraging. Hou, Xue, and Zhang (2015) motivate the investment and return on equity factors in the q-factor model from the investment CAPM. Close cousins of the q-factor model have subsequently appeared in different disguises in the Fama-French (2015, 2018) 5- and 6-factor model, the Stambaugh-Yuan (2017) “mispricing” factor model, and the Daniel-Hirshleifer-Sun (2019) behavioral 3-factor model. See Hou et al. (2019a) for a detailed exposition.

In particular, the following table shows that the 4-factor q-model fully subsumes the Fama-French 6-factor model in head-to-head factor spanning tests.

In terms of structural estimation, Liu, Whited, and Zhang (2009) perform the first such estimation of the investment CAPM in a way that is analogous to what Hansen and Singleton (1982) did for the consumption CAPM. Although by no means perfect, Liu et al.’s first stab yields much more encouraging results than Hansen and Singleton’s at the consumption CAPM. The baseline investment model with only physical capital manages to explain value and momentum separately, albeit not jointly. The joint estimation difficulty has been largely resolved in Goncalves, Xue, and Zhang (2019), who introduce working capital into the investment framework. With plausible parameter estimates, the two-capital investment model manages to explain the value, momentum, investment, and return on equity premiums simultaneously. The next step is to investigate out-of-sample performance and to develop an ex-ante, expected return model that can compete with the implied costs of capital from the accounting literature.

I view the investment CAPM and the consumption CAPM as complementary in theory. Marshall (1890, Principles of Economics) writes:
 
“We might as reasonably dispute whether it is the upper or under blade of a pair of scissors that cuts a piece of paper, as whether value is governed by utility or costs of production. It is true that when one blade is held still, and the cutting is affected by moving the other, we may say with careless brevity that the cutting is done by the second; but the statement is not strictly accurate, and is to be excused only so long as it claims to be merely a popular and not a strictly scientific account of what happens (Marshall, 1890 [1961, 9th edition, p. 348]).”
 
Clearly, by only looking at demand, the consumption CAPM is incomplete even in theory. The investment CAPM is the missing “blade.” The covariance, SDF-centric view of the world only describes the optimal demand behavior. The supply side is all about characteristics. So much for the covariances versus characteristics debate.

In my big-picture, there is no obvious place for behavioral finance. The field has gained its prominence by documenting non-zero means of the CAPM residuals and sticking labels such as under- and over-reaction to them. However, the evidence has been piling up that the investment CAPM alphas are not that big to begin with.

I should acknowledge that the investment CAPM is silent about investor rationality. And that’s the whole point. Investor rationality and EMH are two different things. Remember EMH only says that pricing errors are not forecastable. The investment CAPM alphas are mostly not forecastable. And the expectations in the investment CAPM are entirely rational. Investors might be optimistic and attempt to bid up the equity prices too high. But with a manager’s cool head, the supply of risky shares goes up. In the special case of no adjustment costs, in particular, Tobin’s q will forever be one, regardless of how irrationally optimistic investors are. This equilibrating role of the supply side seems to be greatly underappreciated in the existing literature. We are blind to this parallel universe thanks to the consumption CAPM's single-minded, dogmatic focus on demand.

I can empathize that after more than 50 years of the classic CAPM, it’s intellectually hard to divorce EMH from the (consumption) CAPM. But the (consumption) CAPM is just the blunt “blade” due to its inescapable, intractable problem of aggregation. After marrying EMH with the sharp “blade” of the investment CAPM, we see that capital markets simply obey standard economic principles. The world makes sense! Investor behavior is a separate, important field, but is partial equilibrium in nature. Without tackling aggregation, investor behavior has close to nothing to say about equilibrium pricing. Again, investor rationality and EMH are not the same thing.

I certainly do not agree with everything that Gene said in the interview. Wall Street research is definitely not “business-related pornography.” Financial analysts are an important component of financial intermediary that facilitates the information flow and smooth functioning of our capital markets. Graham and Dodd’s (1934) Security Analysis works in the data, as shown by a mountain of evidence in the accounting literature. And it is perfectly consistent with the investment CAPM, which predicts cross-sectionally varying expected returns, depending on investment, profitability, and expected growth (Hou et al. 2019b).
 
In Thaler’s tweet, he claims that Gene owes him everything. I think Dick got the chronology exactly backward. Gene founded modern finance with EMH, against which Dick has successfully built his entire career. If anything, Dick owes Gene everything. I, on the other hand, owe much of my career to behavioral finance, whose tremendously important empirical contributions, with little in the way of theory, have left a glaring gulf for a theory-minded economist to fill.
 
In the interview, Gene seemed a bit skittish when the phrase “post-Fama” was used. He needn’t be. EMH will continue occupying the front and center of finance for a long time to come. I provide no such guarantee for the Fama-French 6-factor model.  


References

Daniel, Kent D., David Hirshleifer, and Lin Sun, 2019, Short- and long-horizon behavioral factors, forthcoming, Review of Financial Studies.

De Bondt, Werner F. M., and Richard Thaler, 1985, Does the stock market overreact? Journal of Finance 40, 793-805.

Fama, Eugene F., 1998, Market efficiency, long-term returns, and behavioral finance, Journal of Financial Economics 49, 283-306.

Fama, Eugene F., and Kenneth R. French, 2015, A five-factor asset pricing model, Journal of Financial Economics 116, 1–22.

Fama, Eugene F., and Kenneth R. French, 2018, Choosing factors, Journal of Financial Economics 128, 234–252.

Goncalves, Andrei S., Chen Xue, and Lu Zhang, 2019, Aggregation, capital heterogeneity, and the investment CAPM, forthcoming, Review of Financial Studies. 

Graham, Benjamin, and David L. Dodd, 1934, Security Analysis, 1st ed., New York: Whittlesey House, McGraw-Hill Book Company.

Hansen, Lars P., and Kenneth J. Singleton, 1982, Generalized instrumental variables estimation of nonlinear rational expectations models, Econometrica 50, 1269–1288.

Hou, Kewei, Haitao Mo, Chen Xue, and Lu Zhang, 2019a, Which factors? Review of Finance 23, 1–35.

Hou, Kewei, Haitao Mo, Chen Xue, and Lu Zhang, 2019b, Security analysis: An investment approach, working paper, The Ohio State University.

Hou, Kewei, Chen Xue, and Lu Zhang, 2015, Digesting anomalies: An investment approach, Review of Financial Studies 28, 650-705.

Liu, Laura X. L., Toni M. Whited, and Lu Zhang, 2009, Investment-based expected stock returns, Journal of Political Economy 117, 1105-1139.

Marshall, Alfred, 1890, Principles of Economics (9th ed.) (London: Macmillan, first published in 1890, 1961).

Stambaugh, Robert F., and Yu Yuan, 2017, Mispricing factors, Review of Financial Studies 30, 1270–1315.

Zhang, Lu, 2017, The investment CAPM, European Financial Management 23, 545-603.