A meeting of opposites - the nobel prize in economics

My column for the Nov edition of Wealth Insight - here. The unedited version is below:


This month, the Nobel prize for economics was jointly awarded to Eugene Fama, Lars Hansen and Robert Shiller. In itself, the award to three economists is not comment worthy. What makes it interesting is the combination of individuals and the ideas they represent.  Fama and Shiller represent opposite views on how prices of financial assets are formed.

The Chicago Tradition

Between 1940-43, Friedrich Von Hayek, an economist at the London School of Economics, wrote a book – “The Road to serfdom”. Having watched the takeover of Vienna by Nazi Germany, this book outlined his opposition to “Big government”. In the book, Hayek says “when economic power is centralized as an instrument of political power it creates a degree of dependence scarcely distinguishable from slavery.”

The book was published by the Chicago University in 1944, and became the basis of “libertarianism” which found its chief proponent in Milton Friedman. Along with a group of friends, Friedman went on to form the “Chicago School”. Friedman believed that markets worked better than governments. Over time, the Chicago school started to believe that markets were “perfect”.

Fama and Efficient Markets

Fama arrived in Chicago as a MBA student in 1960 – and started work on stock price movements. His initial work was statistical – exploring the statistical distribution of stock price movements. Fama soon proposed that stock price movements were random – and that any non-random movement would be arbitraged away by traders so that any non-random patterns were essentially fleeting. He argued that “the actions of the many competing participants should cause the actual price of a security to wander randomly about its intrinsic value.” He called this the “Efficient Market“.

In 1964, Lawrence Fisher and James Lorie published a study covering data over 34 years – anyone who bought all stocks in the New York Stock Exchange and reinvested dividends would have earned 9% post costs over those 34 years. Subsequently, they demonstrated that a randomly selected assorted portfolio also performed as well. Monkeys with darts were as good as mutual fund managers. The markets were indeed efficient – and all available information seemed to be in the price.

Fama went on to teach portfolio theory and came across the work of Markowitz and Sharpe. He quickly realised that the Capital Asset Pricing Model (CAPM) was the model that proposed the economic theory of how stock prices moved – leading to efficient markets.

The analysis that mutual funds and fund managers rarely beat markets – especially after costs, led John Bogle to form the Vanguard Index fund. Indexed investing had come to the markets.

Behavioural Finance and inefficient markets

Later research into economic behaviour of individuals revealed that choices made by individuals were not all rational or “efficient”. People avoided certain loss while being willing to bet on uncertain gain. Somehow, efficient markets demanded that despite each individual decision being irrational, the collective was always rational. This was difficult to explain. Shiller, a PhD from MIT, started to test assertions of efficient markets. He found for example, that stock prices that were supposedly determined by future dividends, were way more volatile than the dividend paid. They were also more volatile than earnings, demand, replacement cost – or any other fundamental parameter. The “rationality” hypothesis of the market had no explanation for this volatility.

Shiller asserted that the leap from observing that stock prices were difficult to predict to that they were right, was “one of the most remarkable errors in the history of economic thought”.  Lawrence Summers a Harward economist, constructed an alternate finance universe where investors weren’t rational and prices did not reflect economic fundamentals. He was able to show that over a simulated 50 year period, no test could determine statistically the difference between the two market constructs – real market vs simulated market. Summers at a speech to finance professors said “Financial economists like Ketchup economists work only with hard data and are concerned with the interrelationships between the prices of different financial assets. They ignore what seems to many to be the more important question of what determines the overall level of asset prices.”

Slowly, the realization that markets could remain inefficient for long periods was being accepted by academicians. Fischer Black in a lecture titled “Noise” said that noise made financial markets possible but also imperfect. Without noise, prices would stay at their fundamental value and there would be no trading. Unfortunately, noise also made the detection of fundamental value impossible. 

As computers became more powerful, more instances of statistically significant “inefficiencies” started to be discovered. Some indicated that trends tended to persist – stocks prices continued to trend in one direction more often than random moves would imply. When corporate earnings differed from expectations, it took a while for the price to reflect the new expectations fully. The January effect and the “small stock” effect were also persistent.

Confronted with this data, Fama modified the CAPM to bring in factors other than just market moves to explain price behaviour. These were the 3 factor and 4 factor models. However, these turned out to be cases of data mining. Perhaps there was something to beating the market after all.

The Nobel committee in its press release summarises it thus “There is no way to predict the price of stocks and bonds over the next few days or weeks. But it is quite possible to foresee the broad course of these prices over longer periods, such as the next three to five years. These findings, which might seem both surprising and contradictory, were made and analyzed by this year’s Laureates, Eugene Fama, Lars Peter Hansen and Robert Shiller....The Laureates have laid the foundation for the current understanding of asset prices. It relies in part on fluctuations in risk and risk attitudes, and in part on behavioral biases and market frictions”. The search for a perfect market model continues.

Forecasting – The art of being precisely inaccurate

My column in the Economic times on the 30th of sep

The unedited version is here :

Of late, equity markets have behaved as someone with a multiple personality disorder – euphorically up one day, manically depressed the next. Much of this movement is attributed to market moving policy pronouncements. Equally, volatility arises when key economic numbers are released.

Talking heads on TV often attribute market moves to differences between forecast economic numbers (in particular, GDP growth and inflation) and the “actual”. But are the forecast figures really useful for market participants?

What makes a good forecast?

An obvious requirement for a good forecast is its “accuracy” - how good was the forecast compared to the actual outcome? Since the “event” is yet to occur, another important consideration is if the forecast is “honest”. In other words, was it the best prediction the forecaster could make when making the forecast, or was it deliberately coloured or biased. A third factor could be the “value” of the forecast – did it convey information that was useful in making decisions.

Understanding predictions

When dealing with the future, we deal with probabilities. In making a forecast, the forecaster deals with multiple scenarios, and, either explicitly or implicitly, assigns a probability to each scenario. A “point forecast” –a weighted average of future expectations – is, usually, less useful.  An old joke goes – a statistician drowned while crossing a river that was three feet deep on average! Making dramatic statements with high degree of certainty makes for good television, and studies show that “bold” predictions make for better entertainment. They certainly do not guarantee greater accuracy though, or help in better decision making.

A wide distribution of possible outcomes best represents the uncertainty inherent in the real world. Our minds however tend to regard probability based forecasts as somehow not so satisfying –as if the forecaster is “hedging” his bets.

Professional Forecasting – dismal performance

Starting 2007, the Reserve Bank of India conducts a survey of professional economists. Those surveyed offer forecasts for a number of macro-economic variables. These forecasts are made every quarter, and the RBI publishes the results on its website. Among other variables, the professional forecasters offer their estimates for GDP growth, its key components, and inflation.

The forecasts for GDP are made in the form of a probability table. RBI combines the forecasts to yield a min and max forecast range, as well as the median. The picture summarises GDP growth forecasts made in April-May for the next 12 months for every financial year since the survey started.

A quick glance reveals that forecasters do not cover themselves in glory. In each of the 5 years where data is available, the actual GDP growth is outside the maximum-minimum range forecast. Take a minute to digest that – it is not just different from the median, it is outside the forecast range – and in many cases by a wide margin.

Governments own forecasters – even worse?

The prime ministers economic advisory council (PMEAC) too, makes estimates for the same figures twice a year. Unfortunately, it offers a “point estimate” instead of a range. While this gives the perception of being more decisive and accurate, as the table shows, their track record is equally poor – with some estimates having an error of over 10% from the actual - when made only 30 days before the end of the financial year!

GDP Growth
Date Of PMEAC  Report				Forecast for	Actual
Outlook		Review
30-Jul-08	7.7	Jan-09	7.1	Mar-09	6.7
Oct-09	6.5	Feb-10	7.2	Mar-10	8.6
Jul-10	8.5	Feb-11	8.6	Mar-11	9.3
Jul-11	8.2	Feb-12	7.1	Mar-12	6.2
Aug-12	6.7	Apr-13	5	Mar-13	5
Aug-13	5.3			Mar-14

The one redeeming feature is that forecast directions seem to be correct – each subsequent forecast moving in the direction of the eventual number.

Important forecasts, but with spurious accuracy

Economic forecasting is important in that the forecast can itself affect the outcome as policies are adjusted to move the economy in the desired direction. What causes these forecasts to be as poor as they are?

Assuming that forecasters are not wholly incompetent, forecast errors can arise out of three possible factors–

(a) data used to make forecasts is of poor quality. Remember, revisions to the final GDP growth continue for almost 2 years – and advance estimates are notoriously poor. This calls in question the importance attached to near term data. When RBI/government say that they will formulate policy basis data, it is only fair to ask – what data?

(b) forecasts are biased. This is also a real possibility. Median forecasts tend to cluster around the 6%-8% growth. Forecasts are understated in years of high growth and overestimated in those of poor growth. There is a visible tendency to cluster around the “centre”. It seems no one wants to rock the boat. But then, this “conservatism” also reduces the value of forecast in policy formulation.

(c) models are poor – an economy is a dynamic system feeding on itself and other external stimuli. Models attempting to replicate economic performance have to factor in a large number of inputs. Additionally, models are likely to be susceptible to “initial conditions” – which themselves may be sources of errors.

In all cases, the important point that the investor needs to note is that market volatility caused by data releases is largely unwarranted. It provides the patient investor an opportunity to make abnormal gains by betting against short term moves. It is important to remember that the apparent accuracy of most economic numbers is a mirage – and the best one can use them for is to determine the direction of the trend.