Soon after the Great Recession, the U.S. stock markets plunged – and rebounded within 36 minutes. The Dow Jones Industrial Average dropped more than 9%, losing more than 1,000 points before suddenly recovering.
This May 6, 2010 event was the first recorded “flash crash.” While it didn’t have long-term effects, it raised concerns among investors about the stability of stock market.
Computers have made trading faster and more efficient, but they also can create instability in the markets. Today, quantitative analysts use complex algorithms to make many trades in many markets within a fraction of a second. These new algorithms now account for more than half of all trades. But this may lead to even more flash crashes.
As engineers, we were interested in that May 2010 crash. No single reason can explain why flash crashes happen. But are there ways to predict and mitigate these anomalies? We took on the challenge of developing a theory that may help predict flash crashes.
The flow of markets
We started with fluid dynamics, the study of the flow of fluids such as water. These principles can be applied to other problems; one of us previously used fluid physics to examine the movement of traffic.
In fluid dynamics, researchers look at how measured quantities, like velocity and pressure, affect the dynamics of the flow. For example, weather forecasters use the changes in wind speed and pressure to predict the movement of severe storms.
We asked ourselves: Could this science give insight into the dynamics of the stock market?
We felt that the existing theories predicting flash crashes are inadequate, because they focus on only a small part of the whole picture, like the performance of some subset of stocks. Dow Jones or the S&P 500 indices provide a limited amount of information about the market, by observing the behavior of a subset of appropriately chosen stocks.
Our approach was to include all the stocks in the market. In contrast, our model gives this information for almost the whole range of stocks, broken down by specific price ranges. All of these ranges can be simultaneously observed, generating an early warning.
We found analogues for the measurements that scientists typically use to understand flow. For example, for our model, the “density” of the flow was the number of stocks per unit price, and “pressure” was the upward or downward force on the price caused by the buying and selling activity of traders.
Observing stock markets from a computer screen, we could see that the movement of stock prices resembles the flow of a fluid like air or water.