EMH is in trouble only when paired with the consumption CAPM. When paired with the investment CAPM, EMH stands tall, as shown in the empirical performance of the q-factor model. The NPV rule is a standard neoclassical economic principle. I like simple economic models. When I can understand how the world works with a simple model, I hesitate to add ingredients like sentiment, mispricing, under-, and over-reaction. These ingredients are nebulous to conceptualize in theory and (close to) impossible to measure in practice. For example, they would imply that professional investors have been making systematic mistakes for 50 years, in the case of Ball and Brown’s (1968) post-earnings-announcement drift. To me, it is more natural to think of the earnings drift as an equilibrium phenomenon from the investment CAPM.
I agree with Fama (2017) when he says: “Asset pricing and market efficiency are forever joined at the hip (p. 6).” The consumption CAPM has many strong assumptions such as a representative investor, assumptions that make the model largely untestable. The investment CAPM has no such problems, and it seems to me it should be the next generation workhorse theoretical framework for asset pricing. More broadly, I agree with Fama (1970) on EMH. I just disagree with him on what theory and empirical models best represent EMH. But these are not fundamental disagreements. In particular, I feel that I might have done more than anyone else in my generation in defending EMH.
I do fundamentally disagree with Thaler and Shiller in that I think capital markets are working well, to the first order, as indicated in my large-scale empirical work. That said, I am keenly aware of several open gaps in the rational side of the academic literature. For example, we do not yet have a unified general equilibrium theory that can explain, quantitatively, the coexistence of value and momentum and/or the coexistence of investment and profitability premiums. For another example, a large fraction of profits for many anomaly strategies is realized on earnings announcement dates, giving rise to the impression of expectation errors. While the investment CAPM is qualitatively consistent with this pattern, careful empirical work needs to be done to evaluate the quantitative impact. If you ask me these questions again in five years, l may have better answers to these important questions.
Jack: In your paper Replicating Anomalies you looked at over 400 factors derived from published research and found that the vast majority of them do not hold up to statistical scrutiny when microcap companies are eliminated. Can you talk about the main conclusions that investors should take from this paper? What were the anomalies that held up best in your testing?
Lu: Dick Thaler sent the following tweet on May 8, 2017, shortly after we first circulated “Replicating anomalies” via NBER working paper series. In the tweet, Dick claims that we have excluded microcaps from our sample. Dick might have been suffering from the limited attention bias. We have in fact never excluded microcaps from our sample ever since the first draft of our work. We assign value-weights to microcaps in the first draft. We also furnish results from equal-weights in subsequent drafts.
The frictions anomalies replicate poorly, with only 3.8% success rate. Most of the liquidity variables are only priced within microcaps. Even with equal-weights, the replication rate for the frictions anomalies is less than 40%. There is a ton of fake news in this category. Note that we are not saying that liquidity, microstructure, and other frictions, such as transaction costs, do not matter in practice. What we are saying is that these friction variables are not nearly as important as value, momentum, investment, and profitability in terms of generating factor premiums in a broad universe of stocks.
The main lesson for investors is to always do replication before taking any results, published or unpublished, seriously. “Trust but verify.” Nowadays I just verify, if I am interested in any results that I come across. I have learned through pain to trust only results that survive independent, internal replication within my teams. The referee process in top academic journals leaves much to be desired. The self-correcting mechanism of science only works in the long run, and only for most important results.
Jack: One of the things I find most interesting is the intersection between the academic research and how it is applied in the real world of managing money. To help me better understand an academic paper when I read it, I like to try to envision what a portfolio managed using that research would look like in the real world. If you were to hypothetically manage an equity portfolio using the most significant conclusions from your research, what would it look like? What factors would it use to select stocks?
Lu: I would build mutual funds and/or ETF products based on the q-factors and the expected growth factor in our q^5 model, which augments the q-factor model with the expected growth factor (see our q^5 paper, Hou et al. 2018).
It is well known that DFA has built its line of investment products on the Fama-French size and value factors. We have learned much from the 3-factor model over the past 25 years. However, the 3-factor model is by now horribly obsolete, in terms of both economic foundation and empirical performance. I think I called it a “fossil” earlier in this interview. There seems to be a lot of room for the investment management industry to leverage on the latest developments in the academic world of finance.
The DFA investment philosophy makes sense to me. My understanding of it is to help investors better span the risk-return tradeoff in a multidimensional world. Now, the q-factor and the q^5 papers have never interpreted our factors as risk factors, only as common factors that capture large, common cross-sectional variations in stock returns. Unlike Fama and French (1993, 1996), the theorist in me hesitates to go for the risks interpretation, without either a clear understanding of economic mechanisms or evidence linking our factors to primitive risks facing the economy, such as business cycles or endogenous economic growth. However, for practice, the distinction between risk factors and common factors is only academic.
In this sense, the investment CAPM provides an equilibrium, economic foundation for active investment management in practice. It is “active” in the sense that it goes beyond the market portfolio, which only anchors the average return in the time series, even in theory. For the cross-sectional variation in the average return, the theory zeros in on investment, profitability, and the expected growth. The academic literature has recently debated on the merits of different profitability measures and has started on the merits of different investment measures. And our q^5 paper is among the first to even put the expected growth on the map. Graham and Dodd (1934) have long warned us about the danger of speculative growth. Yet, in theory the expected growth is an important part of the expected return. Lots of work remains in both academia and industry to better measure the key drivers of the expected returns.
Jack: The academic research on factor investing has come a long way in the past few decades and we continue to learn more about what drives long-term stock returns, Since you are leading the way in a lot of the new research that is coming out, I was wondering what topics you are looking into now and what you might be looking at going forward. What areas of research do you find the most interesting for the future?
Lu: I am actively working on a number of projects simultaneously, but three lines of work stand out as paramount. First, we are extending the q-factor model to the global data in a project on “Global q-factors.” The preliminary results indicate the investment and return on equity factors work in most countries. We are hoping to (finally!) finish a first draft by the end of this year. More broadly, I view global data as the future of empirical finance. Both behavioral finance and the investment CAPM have proposed economic explanations for anomalies, but it is hard to disentangle the two competing sets of explanations using the U.S. data alone. The cross-country data might provide a sufficient amount of variation in investor sophistication, limits to arbitrage, corporate governance, etc., for identification purposes.
Second, we are also pursuing a project on “The fundamental costs of capital.” It is well known that factor returns are noisy, and, as a result, the out-of-sample performance of the expected return estimates based factor models is weak (Fama and French 1997). In practice, one often just uses firm characteristics directly to forecast returns, but it is not clear how to form expected returns from reduced-form regressions.
An illustrious literature in accounting, pioneered by Gebhardt, Lee, and Swaminathan (2001), has built expected return estimates from accounting-based valuation models. However, it has become clear in the past two decades that these implied costs of capital do not forecast returns. We think the main reason is that these implied costs of capital are in fact internal rates of returns, which are constant over time by construction. Taken literally, they are not supposed to forecast returns, at least in the time series. We think the investment CAPM is a better theory for the 1-period-ahead expected return. The q-factor and q^5 models are basically linear factor approximations of this expected return. In the ongoing project on “The fundamental costs of capital,” we are experimenting to see if we can construct an expected return proxy directly from the nonlinear characteristics model via recursive structural estimation.
Finally, I am going back to my theoretical roots in a project on “An equilibrium theory of factors.” The goal of this project is to extend our disasters framework in a recent article (Bai et al. 2019) to explain both value and momentum as well as investment and profitability premiums in general equilibrium.
In short, a lot of work, and a lot of fun. Stay tuned.
Jack: Thank you again for taking the time to talk to us today. If investors want to find out more about you and your work, where are the best places to go?
Lu: My website is http://theinvestmentcapm.com/. You can also find me on Twitter at @zhanglu_osu.
Photo: Copyright: christianchan / 123RF Stock Photo
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