The winners of this year’s Economics Nobel have shown that it is possible to reach out of the ivory tower to the real world

The Economics Nobel Prize has been shared by Eugene Fama, Robert Shiller and Lars Peter Hansen. Their work ranges from the foundations of modern finance (Fama) to defects in the mainstream models (Fama and Shiller) and improvements in statistical methodology (Fama and Hansen).

Everyone is keen to forecast financial prices. The man who can do so will rapidly make a lot of money. When economists first started looking at stock prices, they appeared to make no sense. In a year when output and profits of steel companies go up, stock prices will often go down. In the 1960s, researchers at the University of Chicago and MIT laid the foundations of modern thinking on the subject.

The key insight was to see that financial markets are competitive. There is no market more open than financial trading. Anyone can step in and try his hand at it. The oldest insight in economics is that fierce competition eliminates all easy profit opportunities. In similar fashion, fierce competition between traders on the market ensures that all obvious ideas for forecasting prices will be used up. The price then contains all known information.

If the price of last evening contains all known information, what makes the price fluctuate today? The answer is: the news of today. But as the news of today is unpredictable, the fluctuations of prices will also be unpredictable. Fama (with Paul Samuelson of MIT) was the key person who understood that stock prices are not predictable, and that this lack of predictability is not a flaw — it is the highest achievement of a competitive market.

Random walk

In a competitive market, the stock price is a “random walk.” Every day is a random percentage change. This may seem esoteric but the quantitative effects are very real and all around us. As an example, the random walk predicts that the variance of weekly returns must be five times bigger than the variance of daily returns, as there are five trading days in most weeks. Let’s look at what happens with Indian data.

In the latest five years, the one-day variance of Nifty returns is 2.35. We would then predict the variance over five days is five times bigger, or 11.76. In the data, the weekly variance comes out quite close, at 11.05. The random walk is not a perfect model for Nifty — but it’s pretty close.

Fama and other researchers kicked off thousands of studies on the predictability of stock prices and found there was very little predictability. This was consistent with what one would expect on an “efficient” market, one with fierce competition. This flies in the face of the beliefs of thousands and traders and fund managers all over the world, who believe they have a special ability to forecast prices and make money. The efficient markets hypothesis predicts that most of them do worse owing to their attempts at forecasting — and it is right in this regard.

In the aftermath of the global crisis, it is fashionable to denigrate the idea of market efficiency. But this is a naive response. Every serious finance scholar knows that if he tried to trade in the market, he would most probably lose money. That is a tribute to the informational efficiency of the market. More than any other field, trading in financial markets has zero entry barriers, and is hence fiercely competitive. This ensures that every scrap of information is analysed and fed, using the most sophisticated methods, into the market price.

Fama was a co-inventor of the “event study methodology,” a delicious tool for identifying cause and effect in econometrics that is far more powerful than the regressions that used to dominate econometrics. The event study methodology has become valuable far beyond its original applications; it is increasingly coming to be used in the analysis of households, firms and countries.

Robert Shiller found that while stock prices are not forecastable, they fluctuate by too much when compared with the fluctuations of dividends. His analysis was made more rigorous using a new statistical methodology — the “generalised method of moments” invented by Hansen. This methodology confirmed that there is an anomaly in the excessive volatility of stock prices. This is a mystery which has yet to be resolved.

With his co-author Kenneth French, Fama demonstrated that the first cut of an asset pricing theory — the Capital Asset Pricing Model — does not fit the data well. They proposed a “three factor” empirical asset pricing model, which works much better. This is the workhorse of empirical finance of the last 20 years.

In the old world, we used to think of economics as sitting inside university departments. But in the modern world, at its best, economics reaches out beyond the ivory tower and matters to the real world. This is amply the case with the work of Fama and Shiller. Efficient markets thinking has had an enormous influence in the real world. Every sophisticated finance practitioner knows that he is up against a daunting challenge when he tries to forecast stock prices, and has to have an excellent argument on why he will succeed over everyone else.

Index funds are a simple strategy where the investor says: “I do not want to even try to beat the market.” Index funds have been enormously influential the world over. In India, index funds have started gaining influence, particularly the “exchange traded funds” on the Nifty and Nifty Junior indexes. A novel twist on simple index funds is a set of three index funds on the three Fama-French factors. These are offered by many providers, but one of them is a company where Fama himself is involved, Dimensional Fund Advisors.

Robert Shiller has also had many successes in the real world. He was a pioneer in constructing price indexes in the field of real estate, which go by the name of Case-Shiller indexes. He has proposed utilising financial derivatives trading to help hedge macroeconomic risks, and his firm named “MacroMarkets” pursues translating these ideas into reality.

(Ajay Shah is an economist and senior fellow at the National Institute for Public Finance and Policy.)

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