Stocks overvalued longer, more frequently than previously thought: Study

NEW YORK, May 4 (UPI) -- Data scientists at Columbia University crunched the numbers on stock bubbles and the results aren't pretty.

According to their research, the top 3,500 stocks trading in the United States stock market featured an average of four bubbles between 2000 and 2013. Their findings, detailed in the journal Mathematics and Financial Economics, suggest stocks become overvalued more frequently than previously thought, and remain so for longer periods of time.

"Our results add further evidence that financial markets are neither efficient nor rational," Philip Protter, a statistics professor at Columbia and a member of the university's Data Science Institute, said in a news release.

Before Protter and his colleagues arrived at their results, they had to develop a reliable formula for determining when a stock was overvalued -- when its trading price exceeded what a rational decision maker would be willing to pay given its expected future returns.

In a series of mathematical papers, researchers developed a statistical algorithm for identifying when a stock's market price outpaces its fundamental worth.

Researchers then used their algorithm to analyze a massive set of trading and financial transactions data. To isolate individual bubbles and quiet natural volatility in the market, researchers classified a bubble as any time a stock became overvalued while its trading price rose at least 5 percent. Conversely, a bubble was considered over -- or popped -- when a stock ceased to be overvalued and its price dropped at least 5 percent.

"I expected to see lots of bubbles in 2009, after the crash, but there were a lot before and a lot after," said study co-author Shihao Yang, now a Harvard graduate student.

Economists have previously developed a variety of methods to spot bubbles before they become especially problematic, but the latest is the first to look only at a stock's price over time.

"This is unique," explained Robert Jarrow, a finance professor at Cornell University who contributed to the research. "It's based on probability theory and the characteristics of a price process."