Bubbles, Efficient Markets, and

Behavioral Finance

“Markets may remain irrational longer than you can remain solvent”

— John Maynard Keynes

From the beginning of 1995 to April 2000, the S&P 500 increased 227% from 466 to 1,524; even more impressive, the NASDAQ 100 index increased more than 12 fold from 347 to 4,692. This was the Dot-Com boom period, described by Fed Chair, Alan Greenspan as one of “irrational exuberance”. After peaking in April of 2000, the market crashed. One year later the S&P was back to 1,100 and the NASDAQ 100 index had decreased 70% to approximately 1,400; by April 2002, it had declined to approximately 1,000—the bubble had burst (see Exhibit 1).

            In general, a speculative bubble occurs when the price of assets such as stocks, gold, or houses, increase beyond a level that can be justified on economic grounds and once past that level their price continues to rise. History is replete with speculative bubbles: the South Sea Bubble, the internet bubble, and the more recent housing bubble. Bubbles tend to have stages. First, there is an initial period in which an event, announcement, or invention changes expectations. This period is almost always followed by a period of abnormal returns for early investors. In turn, other investors are attracted, even though fundamentals no longer make sense, leading to a boom period in which prices continue to increase. Finally, the fourth stage is the bust. In his book, Dot.Con, John Cassidy captures the essence of the internet bubble with the story of

“In March 1999, … was preparing to do an initial public offering … The word on Wall Street was that would follow the path of American Online, Yahoo!, and ebay to become an “internet blue chip”. The only question people in the investment community were asking was how much stock they would be able to lay their hands on…. On the morning of March 30, 10 million shares of opened on the Nasdaq National Market at $16 each, but the price immediately jumped to $85. At the close of trading, the stock stood at $68; it had risen 425% on that day. was valued at almost $10 billion—more than United Airline, Continental Airlines, and Northwest Airline combined. … started operating on April 6, 1998. By the end of the year, it had sold slightly more than $35 million worth of airline tickets, which cost it $36.5 million. …The loss did not include any of the money spent developing its Web site … A few weeks after’s IPO, its stock reached $150, at which point the tiny company was worth more than the entire U.S. airline industry. Two years later, the stock was trading at less than $2, and the entire capitalization of would not have covered the cost of two Boeing 747s.” (Cassidy, Dot.Con, pp. 2-8)
   Exhibit 1:  S&amp;P 500, NASDAQ 100 Index, and 1999-2017

Exhibit 1: S&P 500, NASDAQ 100 Index, and 1999-2017

Are Markets Efficient?

  Eugene Fama

Eugene Fama

One of the most influential theories to emerge out of financial literature over the last fifty years is the efficient market hypothesis (EMH). Introduced by Burton Malkiel in the 1960s, the EMH precipitated a considerable amount of controversy between proponents of the EMH (primarily academics) and practitioners who employed fundamental and technical analysis. EMH proponents argued that if the market consisted of a sufficient number of fundamentalists—those searching for securities priced below or above their intrinsic value—then their actions would inherently force the market price of a security to equal its intrinsic value. For example, if a security were underpriced, the fundamentalists would try to buy the security, pushing its market price towards its intrinsic value. Thus, according to the proponents of the EMH, with enough fundamentalists the market price of a security is equal to its true value. Similarly, EMH proponents argued that if the market consisted of enough technicians—those trading off price and volume trends—then their actions would eliminate the possibility of earning any abnormal returns from identifying trends in security prices. If a stock traded low on Monday and high on Friday, technicians would detect this trend and would buy on Monday and sell on Friday. These actions would augment the price of the stock on Monday and lower it on Friday, thereby eliminating the trend and the possibility of earning an excess profit. If the EMH holds, in the sense that the market consists of a sufficient number of fundamentalists and technicians, then investors would be unable to earn abnormal returns from either fundamental or technical strategies.

            One of the benefits of this controversial theory was that it spurred a considerable amount of research; in fact, there has been more research devoted to the EMH than to any other subject in investments. At the forefront of efficient market research are the seminal works of Eugene Fama. For over thirty years, the studies by Fama have served as a template for research in efficient market theory. For this body of work, he was awarded the 2013 Nobel Prize in Economic Sciences.

            The efficient market studies of Fama and other scholars addressed the question of whether or not and how security prices reflect information. A perfectly efficient market is one in which all information is fully reflected in a security price. If such a market existed, then the price of the security would always be equal to its true value and investors would not be able to earn abnormal returns from fundamental and technical strategies or from inside information. The empirical studies that have tested market efficiency suggest that markets are not perfectly efficient. Studies have found that a number of anomalies do exist. The most notable are abnormal returns (returns above risk-adjusted returns) from investing in small-size firms, low price-to-earning per share stocks, high book-to-market value stocks, and stocks in the month of January. Studies also suggest that the market appears to react quickly and efficiently to events such as initial public offerings, new exchange listings, and expected earnings announcements, but appears to take some time to react to unexpected earnings announcements. Finally, studies have found that, with some exceptions, security analysts and portfolio managers do not seem to be able to consistently outperform the market on a risk-adjusted basis, but that insiders appear to have information not fully reflected in security prices (see Exhibit 2).

   Exhibit 2:    Total Return Graph -&nbsp;4/28/2000 to 2/24/2017

Exhibit 2: Total Return Graph - 4/28/2000 to 2/24/2017

   Exhibit 2:  January Effect: Monthly Rates of Return for S&amp;P 500, 2003-2013

Exhibit 2: January Effect: Monthly Rates of Return for S&P 500, 2003-2013

   Exhibit 3:  Hurst Exponent    Bloomberg’s Hurst coefficient is based on the work of Christopher May who applied the Hurst exponent to nonlinear price patterns. Bloomberg’s Hurst Exponent screen can be used to test for randomness. If a price trend is random, the Hurst coefficient would continuously have a value close to 0.5. If not, then there is pattern to the stock price movement.

Exhibit 3: Hurst Exponent


         The EMH has been the source of much debate, often pitting its proponents against technicians and fundamentalists alike. There is clearly merit to EMH, but as the aforementioned studies suggest there is also some merit to the arguments against it. For example, the Hurst exponent is a time-series measure name after Harold Hurst. The exponent was originally used in hydraulic engineering to study the volatility patterns of rain observed over a long period of time. In finance, the Hurst exponent is used to identify price patterns hidden within seemingly random stock price trends. In general, a time series can be defined as persistent with a tendency to continue its up or down pattern, anti-persistent in which it has a higher tendency to reverse its current pattern, or random. For example, a value of 0.5 for the Hurst coefficient indicates that the movements in the price trend are random. If not, then there is pattern to the stock price movement. For the period 2000-2013, the Hurst exponent value for the S&P 500 averaged 0.67 and over the past year it has averaged 0.62 (Exhibit 3). Using the Hurst screen for other stocks, one often finds that the stocks’ price patterns have Hurst exponent values higher or lower than 0.5, suggesting patterns to their movements and providing an argument for the use of technical analysis.






Behavioral Finance

            Behavioral finance is a psychology-based branch of finance that looks at the systematic impact human frailties and biases have on stock price movements. In the absence of market efficiencies, behaviorists try to show how such biases lead to anomalies and discernible trends that technical investors, as well as fundamentalists, could exploit. Behaviorists, in turn, counter the efficient market argument by pointing to fundamental risk that limits investors from taking advantage of mispricing. That is, a stock that is considered to be underpriced by fundamentalists, would earn an abnormal return if the stock moved to its intrinsic value. The fundamental risk, though, is that the stock could continue to be underpriced. Behavioral advocates contend that because of fundamental risk it takes time for a stock to reach its intrinsic value. They point to how such risk explains bubbles. Consider an analyst during the dot-com bubble who argued that stock was overpriced at $68 and recommended a sell or no buy, only to later see it reach $150. Contrasted against the analyst who said stock was overpriced but still issued a buy recommendation. It is worth noting the epilogue to story: From 2002 to 2008, traded between $50 and $65. Since 2009, the stock’s price continued to skyrocket. As of 4/3/2017, it was trading at $1,769!  Depending on the time, a different investment philosophy provided the right answer.

            After breaking the 20,000 barrier in January, the Dow passed 21,000 earlier this month. A new internet firm Snap Inc., increased nearly 50% on its first day of trading before falling back. Furthermore, the market’s average price-to-earnings ratio reached a high of 30 in March, matching the level last seen in late the 1990s and in 1929. Has the market become irrationally exuberant? There are some fundamental economic factors supporting these levels: expectations of tax cuts, infrastructure spending, deregulations, and repatriation of corporate cash. However, there are concerns: higher interest rates, potential inflation, rejection of globalization and integration. To quote the Economist (March 11, 2017), “Stocks may fly high for some time yet, but investors should keep a parachute handy”.