Asset-Pricing Bubbles

 
 
 
 
Over the several hundred during which organized asset trading has occurred, there have been several spectacular pricing bubbles (rapidly increasing asset prices that surpass supportable values for the underlying assets). Following these asset bubbles, there have been dramatic market reversals (rapidly plunging asset prices to levels well below supportable values for the underlying assets). Lastly in the cycle, there have been eventual recoveries, wherein asset prices roughly equate to supportable values for the underlying assets (Brunnermeier, 2001).

Supporters of the efficient markets hypothesis contend that such long-term volatility and major market reversals cannot occur. Their explanations for these events follow the line that, for reasons not known, all relevant information was not available to investors when such outcomes occurred. Thus, according to this line of reasoning, investors behaved rationally based on what was known at the time (Mishkin & White, 2002). Behavioral finance theory contends, however, that long-term volatility in asset pricing and the resulting market reversals are the products of irrational behavior on the part of investors, such as discounting available information that does not support their assessments (Shiller, 2000).

Neither supporters of the efficient markets hypothesis nor behavioral finance theorists deny that bubbles, crashes, and recoveries occur. The two camps, however, disagre


     
 
 
 
    

 

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The proponents of this argument steadfastly content that long-term stock prices are effectively described by the random walk hypothesis (Brealey, Myers, & Marcus, 2004). Random walk refers to the process of determining whether or not a statistical pattern is present in a particular dynamic activity, or whether movements within the activity are random. The random walk concept, as it is used in common stock portfolio analysis and investment, refers to the application of the runs test to stock market prices and returns. Specifically, the runs test seeks to ascertain the presence in a set of data of a recurring pattern that would enhance the ability to predict future movements in the data set. If such a recurring pattern does not exist, the assumption is that movements in the data set follow a random walk (Sharpe, 1999). Since the introduction of the modern use concept of the random walk for common stock prices in London in the early-1950s, great scepticism has existed toward the idea of detectable patterns in the movement of common stock prices. This scepticism, however, has not appreciably dampened the enthusiasm of the purveyors of such investment information (Brealey, Myers, & Marcus, 2004). Most research on the empirica

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