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Published: February 26, 2013
 / Spring 2013 / Issue 70

 
 

Predicting “Flash Crashes”

A controversial financial market indicator may be able to prevent short-term crises in the modern computerized trading world.

Shortly after 2:40 p.m. on May 6, 2010, a downbeat day for U.S. financial markets turned chaotic. Prices for the Chicago-based E-mini S&P 500 futures—the most liquid equity index contract in the world, with US$140 billion in average daily volume—fell rapidly. Before the Chicago Mercantile Exchange’s (CME’s) automatic stabilizer was triggered at 2:45 p.m., momentarily pausing trading, the Dow Jones Industrial Average had lost about 600 points (and was down nearly 1,000 for the day). After trading was allowed to resume, the markets recovered and regained most of the points within 20 minutes. 

The so-called flash crash of 2010 was the second-largest point swing, following the largest one-day point decline, in the history of the Dow. The crash seriously damaged the confidence of investors, who withdrew $19.1 billion from domestic equity funds that month—the highest monthly outflow since the peak of the financial crisis in October 2008. It also raised questions about the reliability and effectiveness of today’s financial market indicators, as well as the increasing role played by high-frequency traders, whose computerized programs have reduced transaction times to thousandths and even millionths of a second. (It’s been estimated that as of 2010, high-frequency transactions account for about 70 percent of U.S. equity trading, and the majority of U.S. stock market transactions.)

In the end, a joint investigation by the Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission reported no evidence of wrongdoing. It concluded that the crisis began when Waddell & Reed Financial Inc., based near Kansas City, tried to aggressively hedge its investment position by selling $4.1 billion in futures contracts in 20 minutes. This statement was met with widespread criticism; the CME itself issued a rare press release, expressing skepticism that one trade could have had so many ripple effects. Among the culprits that various experts have blamed are managers at Waddell & Reed, the technology, the stock market’s structure, and the evolution of the hedge fund industry.

But the flash crash didn’t just trigger a wave of recrimination. It led to a development with potential lasting impact on financial markets: A new proposed metric for future volatility, which its proponents say can be used to prevent short-term crashes in the modern computerized trading world. Such crashes occur suddenly and quickly spiral out of control. This new metric, volume-synchronized probability of informed trading (VPIN)—if it works as its creators claim—could become a crucial mechanism that uses probability analysis on past trading behavior to monitor imbalances in trading, predict future behavior, and alert traders when a crisis is imminent. Proponents say this warning signal would enable analysts or regulators to slow down or halt trading before getting sucked into a crash. Yet even if its critics are right and the metric isn’t reliably consistent, the debate over its usefulness has further demonstrated the need to address the vulnerabilities inherent in the financial system.

VPIN is designed to calculate “order flow toxicity,” or the probability that informed traders (such as hedge funds, which tend to all buy or sell at the same time) are taking advantage of uninformed traders (such as market makers, who typically lose money when order imbalances occur—that is, when there are so many buy or sell orders for a specific security that it becomes all but impossible to match all the orders). It uses real-time statistical analysis of trading behavior to estimate the relationship between informed traders’ orders and how much liquidity market makers are providing. If the order flow becomes too toxic from informed traders’ activity, electronic market makers will stop supplying liquidity to help stem their losses, which can create a cascading effect on other market makers and trigger an avalanche of similar withdrawals—the sort of frenzied activity that precipitates a flash crash.

 
 
 
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