Contrary to a widespread belief among practitioners of value investing, value has been the focus of much academic finance research for at least three decades. The literature on the value premium — a term researchers use to mean something very precise, but that is closely related to practitioners’ notion of value — is simply staggering. On the practitioner side, value investing stretches back to 1934, when Columbia Business School professors Benjamin Graham and David Dodd ’21 published the first edition of their magnum opus, Security Analysis. Practitioners of value investing thus have a considerable head start over academics. But this does not mean that academic research is of no use to practitioners, if only because academics have a habit of working a topic to death and, in the process, uncovering otherwise unknown regularities in the data.
When academics think of value, they think of stocks with high book-to-market — that is, stocks that trade at a significant discount relative to the book value of their equity. Growth (or glamour) stocks are those that have low book-to-market. Given that individual stock returns are so volatile, academics prefer to test their theories, rightly or wrongly, on portfolios rather than on individual stocks.
For our purposes here, consider 10 portfolios of stocks formed according to book-to-market. Put all publicly traded stocks on a list with the stocks with the lowest book-to-market at the top and the ones with the highest at the bottom. Then bundle these stocks in 10 portfolios. Take the top 10 percent, then the second 10 percent, and so on until the bottom 10 percent, where each stock has a weight that is proportional to its market cap relative to the market cap of all the stocks in the portfolio. Academics refer to the first portfolio — the one with firms with the lowest book-to-market — as the extreme growth portfolio. They refer to the bottom portfolio — which contains the stocks with the highest book-to-market — as the extreme value portfolio. (This procedure is familiar to many value investors who use similar lists as an initial screen to find interesting investment ideas.) We then hold these portfolios for an entire year and measure the returns of each portfolio month by month.
Notice that when financial academics construct these portfolios, they lose the wonderful granularity that is the bread and butter of the value investor, the detailed knowledge of the firm’s economics and books or the quality of management, and so many other things to which the practitioners of value investing pay, rightly, so much attention. This is the first step in which academics and practitioners part ways. They do so because they simply have different objectives. The value investor is interested in whether it is better to commit capital to one firm versus another and here, obviously, understanding the many details of a specific company is essential to obtain the appropriate margin of safety that protects the investors against a permanent impairment of capital. The academic is interested in assessing whether the market is an efficient allocator of capital in some aggregate sense, which requires understanding whether some assets are appropriately priced as a starting point. The question that the value investor is interested in is not uninteresting to the academic; on the contrary, I would say it is the fundamental question in economics. The issue is, again, what is the best way to test whether the market is “broadly” efficient in allocating capital. Many believe that it is easier to test theories using those portfolios as laboratories than individual stocks.
With this in mind, we can return now to our portfolios. Using monthly data stretching back more than four decades, we find that the average (annualized) monthly return of the extreme growth portfolio is about 3.8 percent. Instead the average (annualized) monthly return of the extreme value portfolio is a whopping 10.9 percent. Thus, on average, value stocks earn a nice 7.1 percent annualized monthly return over growth. Academics call that 7.1 percent the value premium.
When academics find a premium, any premium, the first question they ask themselves is: Why does this particular strategy or portfolio command such a premium? Or to put it differently: What is the source of risk embedded in value stocks that requires compensation in the form of large returns if these stocks are to be held by investors? And if risk cannot explain it, what can?
The canonical model of risk in finance is the Capital Asset Pricing Model (CAPM). According to the CAPM, beta, the extent to which the returns of a particular security co-vary with the return of the market portfolio, should be the only source of variation in average returns across securities. Thus, if the CAPM is the right model of risk, then it has to be the case that the value premium is only attributable to differences in the betas. Well then, does the extreme value portfolio have a higher beta than the extreme growth portfolio? The answer is no, at least for the most recent part of the sample covering the last four or five decades.
Because an image is worth a thousand words, I have included a plot (JPEG, 121 KB) that captures the essence of the issue.
I have taken our 10 portfolios described above and plotted their average returns on the horizontal axis: You can see that the extreme value portfolio on the right of the plot commands that 10.9 percent return that we mentioned above and that the extreme growth on the left commands a puny 3.8 percent. The fact that value is to the right of the plot and growth to the left is the visual expression of the value premium.
In the vertical axis, I have plotted what the CAPM says those average returns should be. While a single such plot cannot reflect the enormous body of work on this issue, the point that it delivers is nevertheless striking: the CAPM cannot explain any of the variation in returns associated with variation in book-to-market. In particular notice that the CAPM says that value stocks should have much lower returns than what they have in the data! The inability of the CAPM to explain the value premium is what academics call the value premium puzzle. Many of the debates in academia can be understood simply by following where people stand on the lessons one can draw from this plot.
For instance, does it follow from the above plot that the value premium is an anomaly to be exploited profitably by the savvy investor? No, argue many: the puzzle is simply stating the inadequacies of the CAPM as a model of risk. As a model, the CAPM is a contrived representation of reality; thus the search for new models of risk to substitute the CAPM, which is the subject of a voluminous literature. Others argue that the value premium is the anomaly par excellence and irrefutable proof of market inefficiency. This debate is endless for profound reasons. Indeed, the inability to distinguish between whether the CAPM is a good model of risk and mispricing is such an important feature of the debate that academics have given it a name, somewhat pedantically: the joint hypothesis problem. In layman’s terms: When you see alpha, are you mismeasuring risk or finding value? This debate is at the heart of modern finance.
So why do academics keep arguing over what perhaps many a practitioner considers an obvious issue: that Mr. Market is prone to temporary periods of insanity, euphoria, and depression? Because the market is the fundamental allocation mechanism and if in some loose sense the market is not efficient . . . well perhaps society should consider the appropriate public interventions to improve on what otherwise would be a suboptimal allocation of capital. You can see now why academics worry so much about this issue: The question is not academic at all! It affects the organization of our economic life in ways that are sometimes not fully appreciated by even the best informed investors.
Financial academics and practitioners of value investing are thus closer in their interests than they both suspect, and it is good for both sides of the aisle to keep an eye on what the other is doing. There is much to be learned.
Tano Santos is the Franklin Pitcher Johnson Jr. Professor of Finance and Economics and co-director of the Heilbrunn Center for Graham & Dodd Investing at Columbia Business School.
Professor Santos' research focuses on two distinct areas. A first interest is the field of asset pricing with a particular emphasis on theoretical and empirical models that can account for the predictability of returns, both in the time series and the cross section. A second interest of Professor Santos is applied economic theory, specifically, the economics of financial innovations as well as theory of...