An in-depth look at the Efficient Market Hypothesis

Markets are powerful at aggregating information, and they do it more quickly and cheaply than any known alternative

Paul Kaplan 16 January, 2019 | 6:00PM
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One of the things I like about Andrew Lo’s Adaptive Markets which I previously reviewed is how he thoroughly reviews each theory and finding on which his thesis is based. He not only explains each idea, but also presents its history and the evidence for and against it. A great example of this is in Chapter 1 which covers one of the foundational theories of modern finance, the Efficient Market Hypothesis (EMH). Here’s a brief outline of the contents of that chapter:

The Challenger Disaster and Market Efficiency

Lo starts his discussion of the EMH with a dramatic example. On January 28, 1986, the Space Shuttle Challenger exploded 73 seconds into its launch. A blue-ribbon commission of the best minds in spaceflight, physics, and engineering was assembled to identify the reason for the explosion. After five months of an in-depth investigation, the commission concluded that the explosion was due to the failure of the O-rings around the joints of the booster rocket.

There were four publicly traded companies that were major NASA contractors. The commission’s report was good news for three but bad news the maker of the booster rocket, Morton Thiokol, which would cause a drop in the firm’ stock price. But the impact of the disaster did not occur on the day of the report, but rather, five months earlier, within minutes of the explosion. The stock market can be so efficient that it figured out in minutes what took a panel of experts five months to figure out.

A History Lesson

Lo takes us on a fascinating tour through history, that includes stops in ancient Greece and shogun-era Japan. He discusses the first mathematical model of financial market prices, which, not surprisingly, comes from gambling. This model first appeared in a book published in 1565 by the Italian mathematician Girolamo Cardano. Cardano described a “fair game” which came to be known as a martingale. As with security markets, in a martingale, future performance cannot be forecasted from past performance.

The next breakthrough in the history of the EMH came in 1900, from the doctoral thesis of a mathematics student at the Sorbonne in Paris. Studying data from the Paris Bourse, Louis Bachelier discovered that changes in stock prices are random. He reasoned that this must be the case since all trades should be fair. Trading in the stock market is a martingale.

Bachelier’s work remain in obscurity until it was accidentally rediscovered by the statistician Leonard J. Savage in 1954 at the University of Chicago. He wrote to colleagues, including MIT’s Paul Samuelson. According to Lo, Savage’s letter to Samuelson changed the course of financial history.

Samuelson explained that all information from past prices are embodied in current price as is all relevant information. So future price changes cannot be predicted based on any information today. This is the essence of would become the EMH.

Almost simultaneously, University of Chicago finance professor Eugene Fama discovered EMH independently from Samuelson. Fama started his study of stock market prices in the 1950s while an undergraduate student in Romance languages at Tufts University. He continued his study of stock market prices as a Ph.D. student in economics at the University of Chicago. Through statistical analysis, Fama came to the same conclusion that Samuelson did through economic logic: changes in stock prices are unpredictable. In 1965, he introduced the term efficient market. As he explained, in an efficient market, “prices fully reflect all available information.” In 2013, Fama was awarded a share of the Nobel Prize in Economic Sciences for his role in the development of the EMH. (Samuelson was awarded the Nobel prize in 1970 for his many other contributions to economics.)

Later, Fama broke down market efficiency into three versions:

1. Weak-form. In a weak-form efficient market, current prices reflect all information contained in past prices. Hence, trying to beat the market using “technical analysis” of past prices is futile.

2. Semistrong-form. In a semistrong-form efficient market, current prices reflect all publicly available information. Hence, strategies based on any publicly available information are futile.

3. Strong-form. In strong-form efficient market, all information, public and private is reflected in current prices. Hence, trading on insider information is futile.

In 1969, Fama and three coauthors published a groundbreaking paper that gave strong empirical support for the EMH. According to the EMH, all relevant information is reflected in market prices immediately when it becomes known. For example, stock splits often indicate that an increase in dividends is coming soon. What this study showed was that the impact of stock splits on stock prices comes at the time that the split is announced. Hence, there is no effect on the day the split occurs because the market has already taken the split into account. In 1978, one of the coauthors of 1969 paper, Michael Jensen remarked “there is no other proposition in economics which has more solid evidence supporting it than the Efficient Market Hypothesis.”

The EMH has huge implications for the asset management business. If all active strategies are useless, what should investors do? The EMH laid the groundwork for passive investing in index funds which track market indices rather than try to beat them.

Efficient Markets in Action

Long before Samuelson and Fama enunciated their theories of efficient markets, prediction markets were used in the 19th and early 20th centuries to predict elections. Suppose that there are two candidates running for office, A and B. In a prediction market, two securities are traded: one that pays $1 if candidate A wins and one that pays $1 if candidate B wins. The prices of these securities are basically the market’s assessment of the probability of each candidate winning. So, for example, if the price of the security that pays $1 if candidate A wins is $0.58, the market is assessing that there is a 58% probability of A winning.

Prediction markets were important in their day because it was difficult to conduct accurate polls. Today, prediction markets have been revived and are accessible on the internet. One of the best known of these is the Iowa Electronic Markets. To this day, prediction markets are often more accurate than polls.

Where Do We Go from Here?

In this chapter, Lo writes, “[e]fficient markets are powerful, practical tools to aggregate information and they do it more quickly and cheaply than any known alternative.” Yet, this is only the first of a book of 12 chapters. There is much more to the story than the EMH. While in the early days of the EMH, there was much data that supported it, further research showed that it does always hold. One of the lines of criticism comes from behavioral economists who argue that investors do not behave in the completely rational fashion as assumed in standard economics and finance. In my next article in this series, I will summarize what Professor Lo has to say about behavioral economics and finance.

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Paul Kaplan

Paul Kaplan  Paul Kaplan is Director of Research for Morningstar Canada.

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