Eugene Fama, Lars Peter Hansen and Robert Shiller won this year’s Nobel in Economics.
If you ask an economist for stock portfolio advice, he’ll likely tell you to get a diversified index fund with low management fees because picking winners is awfully hard. How do we know this? Eugene Fama. Here’s Alex Tabarrok on Fama:
As an undergraduate Fama worked for a stock forecasting service and he was tasked with coming up with rules to make money in the market. Time and time again he would find profitable rules only to find that they didn’t work in new data or out of sample. In graduate school he started talking to Merton Miller, Lester Telser, and Benoît Mandelbrot and finally hit on the idea that in an efficient market price changes would not be forecastable. The rest is history.
Fama’s dissertation and famous 1970 review article, Efficient Capital Markets: A Review of Theory and Empirical Work made efficient markets a touchstone for modern economists and finance theorists but practitioners hated and still hate the idea. Nevertheless, test after test showed that very few mutual fund managers beat the market and those that beat the market this year are not more likely to beat the market the next year. Chance and perhaps a few, very rare, geniuses explain the data. Eventually, hundreds of billions of dollars began to flow into index funds and today index funds manage over $7 trillion dollars worth of assets worldwide, making Fama the 7 trillion dollar man. Fama’s ideas have made an enormous contribution to how people invest, saving them billions in fees which generated beautiful homes for fortunate mutual fund managers but less than nothing for their customers.
Here’s Tyler Cowen:
The remarkable thing about Fama — and a point which critics often neglect — is that Fama, working with Ken French, also has provided some of the best evidence against the efficient markets hypothesis. See for instance this 1992 piece. And see this piece too, with French from 1993. Contrary to the earlier Fama work, it turns out there are some empirical predictors of excess returns, if you look hard enough for them. The most notable of these are firm size and book to market value. To put that more concretely, small firms, when they sink in market valuation relative to their book values, appear to yield excess returns in the future periods. In this regard Fama’s later work is closely in tune with some of the later research by Robert Shiller. One possible way of reading this empirical result (which I believe by the way Fama has never endorsed) is that the share prices of small firms show some mean-reversion upwards after they are hit by bad news; that could result for instance from imperfect liquidity in those share markets, combined with some measure of contagious investor sentiment.
The 1992 and 1993 pieces with French are landmarks in empirical finance and they set off a much longer literature trying to find predictors of excess returns on stocks. Note that Fama holds open the possibility that real rates of return are changing in the economy, or risk premia are changing, and thus he does not automatically identify these empirical results with market inefficiency, as he had defined that concept in his articles from the 1960s.
If the short version of Fama is “Efficient markets (and things that look inefficient could just as easily be risk premia that vary over the course of the business cycle)”, then the the short version of his co-awardee Robert Shiller is “Financial markets can be pretty inefficient (but the best solution could easily be adding more markets)”. Cowen and Tabarrok summarise things well. Here’s Alex:
Robert Shiller is best known for warning about the internet stock market bubble and later the housing bubble. What is most impressive to me, however, is that most people who think that markets can be inefficient are anti-market. Shiller’s solution to market problems, however, is more markets! The housing market, for example, has traditionally had two problems. Since each house is unique there has been no market index of housing prices so that people couldn’t easily see bubbles and if they could see them on the ground there was no easy way to short the market (to try to profit from the bubble in a way that would moderate the bubble). Moreover, because there haven’t been good housing indexes a very large amount of each average person’s wealth has been tied up with an asset that can fluctuate substantially in price. Most house buyers, in other words, are putting all their eggs in one basket and crossing their fingers that the basket doesn’t go bust. In recent years, that has been a very unfortunate bet.
Shiller’s solution to the problems in the housing market has been to make the market better—he created with Case and Weiss–the Case-Shiller Index. For the first time, it’s possible to see in real time housing prices and compare with averages over time and it possible to buy options and futures on the index which will help for forecasting. Moreover, it’s possible that in the future insurance products can be built based on local versions of the index–thus you could insure yourself against big declines in the price of housing in your neighborhood.
I wish that iPredict would open markets on median price growth in the major New Zealand markets. How would this work? Take Auckland, for example. Open a set of markets for Auckland housing prices in 2015. One contract pays $1 if median house price in Auckland in 2015 is less than $350,000, another pays $1 if prices range from $350-375k, another pays $1 if prices range from $375-400k, and all the way to a contract paying $1 if prices are north of $800k. The distribution of prices across the set of contracts gives forecast price growth and market estimates of the variance around that expectation. The RBNZ should sponsor these markets. There would be great social utility in their existence. Those who are convinced that current housing prices are just a bubble could short the contracts. It’s currently very easy to go long on housing by buying a house or a second house. It’s hard to short housing, or to hedge against the risk of price drops. And we’d have some forecasts of the effects of various housing policies.
Lars Peter Hansen contributes to this Prize through his work in econometrics. He’s responsible for the General Method of Moments. I’m a Stata-puncher rather than an econometrician – I use the tools these guys develop rather than developing new ones. His contributions are mostly in time series econometrics; I mostly work in cross-sectional applications. So I’m the last person you want summarising Hansen’s contributions. I’ll note that Maximum Likelihood Estimation can reasonably be viewed as a specific case of Hansen’s GMM technique. Alex summarises GMM and explains why it’s needed in financial econometrics; Tyler’s here.
I continue to be disappointed that Gordon Tullock has not been awarded his Nobel. He deserved it jointly with James Buchanan in 1986. He still deserves it. But the Fama/Hansen/Shiller prize is a good one. Fama was also the most popular pick in our Departmental Nobel sweeps this year.