The Fama Portfolio: Selected Papers of Eugene F. Fama

The Fama Portfolio: Selected Papers of Eugene F. Fama

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ISBN-13: 9780226426983
Publisher: University of Chicago Press
Publication date: 09/07/2017
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 584
File size: 44 MB
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About the Author

John H. Cochrane is a distinguished senior fellow at both the University of Chicago Booth School of Business and the Becker Friedman Institute at the University of Chicago. He is also a senior fellow of the Hoover Institution at Stanford University, an adjunct scholar of the Cato Institute, and a research associate of the NBER. He is the author of Asset Pricing.Tobias J. Moskowitz is the Fama Family Professor of Finance at the University of Chicago Booth School of Business, a principal and consultant at AQR Capital Management, and a research associate of the NBER.

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Eugene F. Fama


I was invited by the editors to contribute a professional autobiography for the Annual Review of Financial Economics. I focus on what I think is my best stuff. Readers interested in the rest can download my vita from the website of the University of Chicago, Booth School of Business. I only briefly discuss ideas and their origins, to give the flavor of context and motivation. I do not attempt to review the contributions of others, which is likely to raise feathers. Mea culpa in advance.

Finance is the most successful branch of economics in terms of theory and empirical work, the interplay between the two, and the penetration of financial research into other areas of economics and real-world applications. I have been doing research in finance almost since its start, when Markowitz (1952, 1959) and Modigliani and Miller (1958) set the field on the path to become a serious scientific discipline. It has been fun to see it all, to contribute, and to be a friend and colleague to the giants who created the field.


My grandparents emigrated to the U.S. from Sicily in the early 1900s, so I am a third generation Italian-American. I was the first in the lineage to go to university.

My passion in high school was sports. I played basketball (poorly), ran track (second in the state meet in the high jump — not bad for a 5'8? kid), played football (class B state champions), and baseball (state semi-finals two years). I claim to be the inventor of the split end position in football, an innovation prompted by the beatings I took trying to block much bigger defensive tackles. I am in my high school's (Malden Catholic) athletic hall of fame.

I went on to Tufts University in 1956, intending to become a high school teacher and sports coach. At the end of my second year, I married my high school sweetheart, Sallyann Dimeco, now my wife of more than 50 years. We have four adult children and ten delightful grandchildren. Sally's family contributions dwarf mine.

At Tufts I started in romance languages but after two years became bored with rehashing Voltaire and took an economics course. I was enthralled by the subject matter and by the prospect of escaping lifetime starvation on the wages of a high school teacher. In my last two years at Tufts, I went heavy on economics. The professors, as teachers, were as inspiring as the research stars I later profited from at the University of Chicago.

My professors at Tufts encouraged me to go to graduate school. I leaned toward a business school Ph.D. My Tufts professors (mostly Harvard economics Ph.D.s) pushed Chicago as the business school with a bent toward serious economics. I was accepted at other schools, but April 1960 came along and I didn't hear from Chicago. I called and the dean of students, Jeff Metcalf, answered. (The school was much smaller then.) They had no record of my application. But Jeff and I hit it off, and he asked about my grades. He said Chicago had a scholarship reserved for a qualified Tufts graduate. He asked if I wanted it. I accepted and, except for two great years teaching in Belgium, I have been at the University of Chicago since 1960. I wonder what path my professional life would have taken if Jeff didn't answer the phone that day. Serendipity!

During my last year at Tufts, I worked for Harry Ernst, an economics professor who also ran a stock market forecasting service. Part of my job was to invent schemes to forecast the market. The schemes always worked on the data used to design them. But Harry was a good statistician, and he insisted on out-of-sample tests. My schemes invariably failed those tests. I didn't fully appreciate the lesson in this at the time, but it came to me later.

During my second year at Chicago, with an end to course work and prelims in sight, I started to attend the Econometrics Workshop, at that time the hotbed for research in finance. Merton Miller had recently joined the Chicago faculty and was a regular participant, along with Harry Roberts and Lester Telser. Benoit Mandelbrot was an occasional visitor. Benoit presented in the workshop several times, and in leisurely strolls around campus, I learned lots from him about fat-tailed stable distributions and their apparent relevance in a wide range of economic and physical phenomena. Merton Miller became my mentor in finance and economics (and remained so throughout his lifetime). Harry Roberts, a statistician, instilled a philosophy for empirical work that has been my north star throughout my career.


Miller, Roberts, Telser, and Mandelbrot were intensely involved in the burgeoning work on the behavior of stock prices (facilitated by the arrival of the first reasonably powerful computers). The other focal point was MIT, with Sydney Alexander, Paul Cootner, Franco Modigliani, and Paul Samuelson. Because his co-author, Merton Miller, was now at Chicago, Franco was a frequent visitor. Like Merton, Franco was unselfish and tireless in helping people think through research ideas. Franco and Mert provided an open conduit for cross-fertilization of market research at the two universities.

At the end of my second year at Chicago, it came time to write a thesis, and I went to Miller with five topics. Mert always had uncanny insight about research ideas likely to succeed. He gently stomped on four of my topics, but was excited by the fifth. From my work for Harry Ernst at Tufts, I had daily data on the 30 Dow-Jones Industrial Stocks. I proposed to produce detailed evidence on (1) Mandelbrot's hypothesis that stock returns conform to non-normal (fat-tailed) stable distributions and (2) the time-series properties of returns. There was existing work on both topics, but I promised a unifying perspective and a leap in the range of data brought to bear.

Vindicating Mandelbrot, my thesis (Fama 1965a) shows (in nauseating detail) that distributions of stock returns are fat-tailed: there are far more outliers than would be expected from normal distributions — a fact reconfirmed in subsequent market episodes, including the most recent. Given the accusations of ignorance on this score recently thrown our way in the popular media, it is worth emphasizing that academics in finance have been aware of the fat tails phenomenon in asset returns for about 50 years.

My thesis and the earlier work of others on the time-series properties of returns falls under what came to be called tests of market efficiency. I coined the terms "market efficiency" and "efficient markets," but they do not appear in my thesis. They first appear in "Random Walks in Stock Market Prices," paper number 16 in the series of Selected Papers of the Graduate School of Business, University of Chicago, reprinted in the Financial Analysts Journal (Fama 1965b).

From the inception of research on the time-series properties of stock returns, economists speculated about how prices and returns behave if markets work, that is, if prices fully reflect all available information. The initial theory was the random walk model. In two important papers, Samuelson (1965) and Mandelbrot (1966) show that the random walk prediction (price changes are iid) is too strong. The proposition that prices fully reflect available information implies only that prices are sub-martingales. Formally, the deviations of price changes or returns from the values required to compensate investors for time and risk-bearing have expected value equal to zero conditional on past information.

During the early years, in addition to my thesis, I wrote several papers on market efficiency (Fama 1963, 1965c, Fama and Blume 1966), now mostly forgotten. My main contribution to the theory of efficient markets is the 1970 review (Fama 1970). The paper emphasizes the joint hypothesis problem hidden in the sub-martingales of Mandelbrot (1966) and Samuelson (1965). Specifically, market efficiency can only be tested in the context of an asset pricing model that specifies equilibrium expected returns. In other words, to test whether prices fully reflect available information, we must specify how the market is trying to compensate investors when it sets prices. My cleanest statement of the theory of efficient markets is in chapter 5 of Fama (1976b), reiterated in my second review "Efficient Markets II" (Fama 1991a).

The joint hypothesis problem is obvious, but only on hindsight. For example, much of the early work on market efficiency focuses on the autocorrelations of stock returns. It was not recognized that market efficiency implies zero autocorrelation only if the expected returns that investors require to hold stocks are constant through time or at least serially uncorrelated, and both conditions are unlikely.

The joint hypothesis problem is generally acknowledged in work on market efficiency after Fama (1970), and it is understood that, as a result, market efficiency per se is not testable. The flip side of the joint hypothesis problem is less often acknowledged. Specifically, almost all asset pricing models assume asset markets are efficient, so tests of these models are joint tests of the models and market efficiency. Asset pricing and market efficiency are forever joined at the hip.


My Ph.D. thesis and other early work on market efficiency do not use the CRSP files, which were not yet available. When the files became available (thanks to years of painstaking work by Larry Fisher), Jim Lorie, the founder of CRSP, came to me worried that no one would use the data and CRSP would lose its funding. He suggested a paper on stock splits, to advertise the data. The result is Fama, Fisher, Jensen, and Roll (1969). This is the first study of the adjustment of stock prices to a specific kind of information event. Such "event studies" quickly became a research industry, vibrant to this day, and the main form of tests of market efficiency. Event studies have also found a practical application — calculating damages in legal cases.

The refereeing process for the split study was a unique experience. When more than a year passed without word from the journal, we assumed the paper would be rejected. Then a short letter arrived. The referee (Franco Modigliani) basically said: it's great, publish it. Never again would this happen!

There is a little appreciated fact about the split paper. It contains no formal tests (standard errors, t-statistics, etc.) The results were apparently so convincing as confirmation of market efficiency that formal tests seemed irrelevant. But this was before the joint hypothesis problem was recognized, and only much later did we come to appreciate that results in event studies can be sensitive to methodology, in particular, what is assumed about equilibrium expected returns — a point emphasized in Fama (1998).

Michael Jensen and Richard Roll are members of a once-in-a-lifetime cohort of Ph.D. students that came to Chicago soon after I joined the faculty in 1963. Also in this rough cohort are (among others) Ray Ball, Marshall Blume, James MacBeth, Myron Scholes, and Ross Watts. I think I was chairman of all their thesis committees, but Merton Miller and Harry Roberts were deeply involved. Any investment in these and about 100 other Ph.D. students I have supervised has been repaid many times by what I learn from them during their careers.


In 1975 I published a little empirical paper, "Short-Term Interest Rates as Predictors of Inflation" (Fama 1975). The topic wasn't new, but my approach was novel. Earlier work uses regressions of the interest rate on the inflation rate for the period covered by the interest rate. The idea is that the expected inflation rate (along with the expected real return) determines the interest rate, so the interest rate should be the dependent variable and the expected inflation rate should be the independent variable. The observed inflation rate is, of course, a noisy proxy for its expected value, so there is a measurement error problem in the regression of the ex ante interest rate on the ex post inflation rate.

My simple insight is that a regression estimates the conditional expected value of the left-hand-side variable as a function of the right-hand-side variables. Thus, to extract the forecast of inflation in the interest rate (the expected value of inflation priced into the interest rate) one regresses the ex post inflation rate on the ex ante interest rate. In hindsight, this is the obvious way to run the forecasting regression, but again it wasn't obvious at the time.

There is a potential measurement error problem in the regression of the ex post inflation rate on the ex ante (T-bill) interest rate, caused by variation through time in the expected real return on the bill. The model of market equilibrium in "Short-Term Interest Rates as Predictors of Inflation" assumes that the expected real return is constant, and this seems to be a reasonable approximation for the 1953–1971 period of the tests. (It doesn't work for any later period.) This result raised a furor among Keynesian macroeconomists who postulated that the expected real return was a policy variable that played a central role in controlling investment and business cycles. There was a full day seminar on my paper at MIT, where my simple result was heatedly attacked. I argued that I didn't know what the fuss was about, since the risk premium component of the cost of capital is surely more important than the risk-free real rate, and it seems unlikely that monetary and fiscal actions can fine tune the risk premium. I don't know if I won the debate, but it was followed by a tennis tournament, and I think I did win that.

The simple idea about forecasting regressions in Fama (1975) has served me well, many times. (When I have an idea, I beat it to death.) I have many papers that use the technique to extract the forecasts of future spot rates, returns, default premiums, etc., in the term structure of interests rates, for example Fama (1976a, c, 1984b, 1986, 1990b, 2005), Fama and Schwert (1979), Fama and Bliss (1987). In a blatant example of intellectual arbitrage, I apply the technique to study forward foreign exchange rates as predictors of future spot rates, in a paper (Fama 1984a) highly cited in that literature. The same technique is used in my work with Kenneth R. French and G. William Schwert on the predictions of stock returns in dividend yields and other variables (Fama and Schwert 1977, Fama and French 1988, 1989). And regressions of ex post variables on ex ante variables are now standard in forecasting studies, academic and applied.


In 1976 Michael Jensen and William Meckling published their groundbreaking paper on agency problems in investment and financing decisions (Jensen and Meckling 1976). According to Kim, Morse, and Zingales (2006), this is the second most highly cited theory paper in economics published in the 1970–2005 period. It fathered an enormous literature.

When Mike came to present the paper at Chicago, he began by claiming it would destroy the corporate finance material in what he called the "white bible" (Fama and Miller, The Theory of Finance 1972). Mert and I replied that his analysis is deeper and more insightful, but in fact there is a discussion of stockholder-bondholder agency problems in chapter 4 of our book. Another example that new ideas are almost never completely new!

Spurred by Jensen and Meckling (1976), my research took a turn into agency theory. The early papers on agency theory emphasized agency problems. I was interested in studying how competitive forces lead to the evolution of mechanisms to mitigate agency problems. The first paper, "Agency Problems and the Theory of the Firm" (Fama 1980a) argues that managerial labor markets, inside and outside of firms, act to control managers faced with the temptations created by diffuse residual claims that reduce the incentives of individual residual claimants to monitor managers.

I then collaborated with Mike on three papers (Fama and Jensen 1983a, b, 1985) that study more generally how different mechanisms arise to mitigate the agency problems associated with "separation of ownership and control" and how an organization's activities and the special agency problems they pose, affect the nature of its residual claims and control mechanisms. For example, we argue that the redeemable residual claims of a financial mutual (for example, an open end mutual fund) provide strong discipline for its managers, but redeemability is cost effective only when the assets of the organization can be sold quickly with low transactions costs. We also argue that the nonprofit format, in which no agents have explicit residual claims to net cash flows, is a response to the agency problem associated with activities in which there is a potential supply of donations that might be expropriated by residual claimants. Two additional papers (Fama 1990a, 1991b) spell out some of the implications of Fama (1980a) and Fama and Jensen (1983a, b) for financing decisions and the nature of labor contracts.


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Table of Contents

Preface, by John H. Cochrane and Tobias J. Moskowitz
I. Introductions
My Life in Finance Eugene F. Fama Things I’ve Learned from Gene Fama Kenneth R. French Gene Fama’s Impact: A Quantitative Analysis G. William Schwert and René M. Stulz II. Efficient Markets
Efficient Markets and Empirical Finance John H. Cochrane and Tobias J. Moskowitz The Great Divide Clifford Asness and John Liew Efficient Capital Markets: A Review of Theory and Empirical Work Eugene F. Fama Efficient Capital Markets: II Eugene F. Fama Market Efficiency, Long-Term Returns, and Behavioral Finance Eugene F. Fama III. Efficiency Applied: Event Studies and Skill
Fama, Fisher, Jensen, and Roll (1969): Retrospective Comments Ray Ball Eugene Fama and Industrial Organization Dennis W. Carlton The Adjustment of Stock Prices to New Information Eugene F. Fama, Lawrence Fisher, Michael C. Jensen, and Richard Roll Luck versus Skill John H. Cochrane and Tobias J. Moskowitz Luck vs. Skill and Factor Selection Campbell R. Harvey and Yan Liu Luck versus Skill in the Cross-Section of Mutual Fund Returns Eugene F. Fama and Kenneth R. French IV. Risk and Return
Risk and Return John H. Cochrane and Tobias J. Moskowitz Risk, Return, and Equilibrium: Empirical Tests Eugene F. Fama and James D. MacBeth The Cross-Section of Expected Stock Returns Eugene F. Fama and Kenneth R. French Common Risk Factors in the Returns on Stocks and Bonds Eugene F. Fama and Kenneth R. French Multifactor Explanations of Asset Pricing Anomalies Eugene F. Fama and Kenneth R. French V. Return Forecasts and Time-Varying Risk Premiums
Return Forecasts and Time Varying Risk Premiums John H. Cochrane Short-Term Interest Rates as Predictors of Inflation Eugene F. Fama Forward Rates as Predictors of Future Spot Rates Eugene F. Fama Forward and Spot Exchange Rates Eugene F. Fama Dividend Yields and Expected Stock Returns Eugene F. Fama and Kenneth R. French The Information in Long-Maturity Forward Rates Eugene F. Fama and Robert R. Bliss VI. Corporate Finance and Banking
Corporate Finance Amit Seru and Amir Sufi Agency Problems and the Theory of the Firm Eugene F. Fama Separation of Ownership and Control Eugene F. Fama and Michael C. Jensen Dividend Policy: An Empirical Analysis Eugene F. Fama and Harvey Babiak Disappearing Dividends: Changing Firm Characteristics or Lower Propensity to Pay? Eugene F. Fama and Kenneth R. French Financing Decisions: Who Issues Stock? Eugene F. Fama and Kenneth R. French Banking in the Theory of Finance Eugene F. Fama Conclusion: Our Colleague, by John H. Cochrane and Tobias J. Moskowitz

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