Many investors have been getting excited about the so-called profitability factor, originally posed by Novy-Marx. It’s worth looking at it more closely, as not all is necessarily as it seems.
The basic idea is simple: Other things being equal, firms with high gross profits (revenue costs) have earned higher expected returns than firms with low gross profits.
Even market heavyweights Eugene Fama and Ken French have integrated the factor into their new "5-factor model," which consists of a market factor, size factor, value factor, profitability factor and an investment factor.
This research was not lost on Dimensional Fund Advisors (DFA), a quantitative asset manager that’s essentially an extension of the University of Chicago Finance Department. DFA has added the concept of profitability to its process (we assume it is the profitability factor identified by Fama and French).
In the words of Eduardo Repetto, DFA's chief investment officer, regarding profitability:
“New research has to be very robust, very reliable, and have real information that's not already captured in the other dimensions.”
Research On Profitability
But how robust and reliable is the so-called profitability factor, and is it possible that it might already be captured in other dimensions?
A new paper titled, "A Comparison of New Factor Models," by Kewei Hou, Chen Xue and Lu Zhang, shows that the profitability factor is not, in fact, a new "dimension," as has been suggested.
The authors find that the profitability factor highlighted by Fama and French is captured in cleaner ways by their simpler and more robust four-factor model, which consists of a market factor, a size factor, an investment factor and a return-on-equity factor.
The authors highlight that there are "four concerns with the motivation of the Fama and French model based on valuation theory," suggesting that the factors chosen by Fama and French are merely descriptive and/or data-mined, but not grounded in economic theory. Ouch.
But the critique of the five-factor model isn't only on theoretical grounds. It is also based on the evidence. The Hou, Xue and Zhang four-factor model captures all the returns associated with the new factors outlined by Fama and French. This suggests that the "new" profitability factor may not be a new dimension at all, since it can be explained via exposures to the market, size, and Hou, Xue and Zhang's investment and ROE factors.
Profitability is also questionable in international markets.
In a working paper, "The Five-Factor Fama-French Model: International Evidence," by Nusret Cakici, the author looks at the performance of the five-factor model in 23 developed stock markets. There is only marginal evidence the factor works globally.
In some markets, the factor is effective, but in other regions such as Japan and Asia Pacific, the factor simply doesn't explain returns. Our own internal research on the matter is consistent with this result. A lack of unified results often hints toward a lack of robustness and/or data mining.
Only time will tell if the out-of-sample performance of the so-called profitability factor will hold. There are certainly many smart academics and investment houses leveraging the factor as a way to capture higher returns, so we can't rule anything out.
However, our advice is to tread lightly in the factor jungle, being sure to always carry a heavy machete to chop away at noisy data and the overfitting problems that accompany them.
Inside Behavioral Finance
The baseline theory for understanding asset prices is the efficient market hypothesis (EMH) pioneered by Eugene Fama. Of particular interest is semi-strong market efficiency, which claims that markets prices reflect all publicly available information about securities.
As the story goes, when mispricings occur in markets, these arbitrage opportunities will be immediately eliminated by professional investors, who exploit these opportunities for a profit. And because of this competitive mechanism, in the EMH view, prices should always reflect fundamental value.
The EMH is a great theory, and there significant evidence to suggest it holds in many cases. However, there is a complementary framework—behavioral finance—that helps solve many of the puzzles in the stock market.
Behavioral finance is often considered a "new" thing, but the concepts have been around for a long time.
Looking At Keynes In A New Way
Consider John Maynard Keynes, who was a shrewd observer of financial markets, and a successful investor in his own right. His investing success, however, was uneven, and at one point he was reportedly wiped out while speculating on leveraged currencies. This led him to share one of the greatest market mantras of all time:
“The market can remain irrational longer than you can remain solvent.”
Keynes’ quip highlights two elements of real-world markets that the efficient market hypothesis doesn't consider: Investors can be irrational. and arbitrage is risky. In academic parlance, "investors can be irrational" boils down to an understanding of psychology, and "arbitrage is risky" boils down to what academics call limits to arbitrage, or market frictions.
These two elements—psychology and market frictions—are the building blocks for behavioral finance.
First, let's have a discussion of limits to arbitrage.
What’s Wrong With Arbitrage?
EMH predicts that prices reflect fundamental value. Why?
People are greedy, and any mispricings are immediately corrected by arbitragers. But in the real world, true arbitrage—profits earned with zero risk after all possible costs—rarely, if ever, exist. Most arbitragelike trades involve some form of cost or risk.
This could mean fundamental or basis risk, transaction costs or noise-trader risk.
- Oranges in Florida cost $1 per orange.
- Oranges in California cost $2 per orange.
- The fundamental value of an orange is $1 (assumption for the example).
- EMH suggests arbitragers will buy oranges in Florida and sell oranges in California until California oranges drop to $1. Prices will quickly correct and there is no free lunch.
- But what if it costs $1 to ship oranges from Florida to California? Prices are decidedly not correct—the fundamental value of an orange is $1. But there is also not a free lunch.
Next, a discussion of psychology is in order. First, the literature from psychology makes it fairly clear that humans are not 100 percent rational all the time.
Daniel Kahneman tells a story of two modes of thinking: system 1 and system 2. System 1 is an efficient heuristics-based decision-making component of the human brain. System 2 is the analytic and calculated portion of the brain—~100 percent rational.
As stand-alone topics, limits of arbitrage and psychology are interesting, but they have limited potential to affect prices via their individual influences. However, crafting a hypothesis that involves elements of silly investors and market frictions—simultaneously—is a potent combination.
For example, consider the concept of noise traders. J. Bradford De Long, Andrei Shleifer, Larry Summers and Robert J. Waldmann wrote an article called, “Noise Trader Risk in Financial Markets” in the Journal of Political Economy in 1990.
Here is the abstract from the paper:
“We present a simple overlapping-generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders’ beliefs creates a risk in the price of the asset that deters rational arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than rational investors do. The model sheds light on a number of financial anomalies, including the excess volatility of asset prices, the mean reversion of stock returns, the underpricing of closed-end mutual funds, and the Mehra-Prescott equity premium puzzle.”
Combining biased investors with an understanding of market frictions/incentives can create powerful investment concepts. This combination can also describe what behavioral finance is all about; namely, understanding how behavioral bias—in conjunction with market frictions—create interesting impacts on market prices.
Wesley R. Gray, Ph.D., is the chief investment officer for Alpha Architect (AlphaArchitect.com), a systematic asset manager based near Philadelphia, Pennsylvania.