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Forecasting Market Bubbles and Crashes

Extreme stock downturns are preceded by a sustained period of accelerating growth rates.

James X. Xiong, 08/14/2013

This article originally appeared in the August/September 2013 issue of MorningstarAdvisor magazine. To subscribe, please call 1-800-384-4000.

This article is an abbreviated version of a research paper undergoing peer review for journal publication.

Market crashes have profound consequences on societal wealth and portfolio management. Notorious examples include the Dutch tulip mania in 1637, the South Sea bubble in England in 1720, the Great Depression in 1929, and the Internet bubble that peaked in early 2000. One characteristic associated with those crashes was tremendous growth coupled with investors’ euphoria before the market crash. In these and many other similar cases, asset prices rose at an increasing rate, resulting in unsustainable growth and an eventual market crash.

Bubbles and crashes are often associated with each other. An asset bubble is often defined as an asset’s price that exceeds its fundamental value for an extended period of time. This definition is subject to criticism because it is difficult to identify a bubble before it pops and even more difficult to predict when a bubble will burst.

Standard neoclassical theory and the efficient market hypothesis imply the absence of bubbles, because the large number of well-informed arbitrageurs guarantees that any mispricing caused by investors’ behavior will be corrected. Blanchard and Watson (1983), however, argue that bubbles are consistent with rationality, and runaway asset prices and market crashes are consistent with rational bubbles. Abreu and Brunnermeier (2003) demonstrate that bubbles can exist for a substantial period as rational arbitrageurs understand that the market will eventually crash but will ride the bubble for a time to generate high returns.

There is little research, however, on stock crashes at the individual stock level. Chen, Hong, and Stein (2001) use skewness as a measure for stock crashes and show that three robust findings about conditional skewness emerged from their analysis of individual stocks. They found that, in the cross-section, negative skewness is greater in stocks that have experienced an increase in trading volume relative to the trend over the prior six months, have had positive returns over the prior 36 months, and are larger in terms of market capitalization. Their second finding shows the impact of past returns or growth rates on stock crashes. In the context of a bubble model, high past returns over the prior 36 months imply that the bubble has been building up for a long time, so there is a large correction or burst when prices fall back to fundamentals.

In this article, we extend Chen, Hong, and Stein and focus on a more powerful growth path of returns over the past two or three years: accelerated rate of price growth. We show that accelerated price growth is a strong contributor to stock crashes. This is meaningful because investors can better forecast crashes based on past accelerated growth rates.

A natural question to ask is how accelerated price growth can occur. One possibility is the well-known positive feedback process. For example, investors invest (or withdraw) money today, which causes more investors to invest (withdraw) money tomorrow. The positive feedback process is closely related to herd behavior. A number of studies have considered herd behavior as a possible explanation for the excessive volatility observed in financial markets. Shiller (2005) provides massive evidence to support his argument that irrational exuberance played a significant role in producing the ups and downs of the stock and real estate markets. He listed 12 precipitating factors that gave rise to the booms in the stock and housing markets. These factors are amplified through feedback loops and naturally occurring Ponzi schemes, aided by the news media, and can ultimately lead to market crashes.

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