PREDICTING THE NEXT STOCK MARKET
graphs looked like the printout from a seismographic device monitoring shocks from an earthquake. Graphs of the pressure also clearly showed a major event at the flash crash time. Looking at all stocks also gave us the ability to predict the near future of their behavior. Our model showed signs of a flash crash 10 minutes before the big event – before any issues would have been evident to human observers. We have observed similar results from the model in the case of other crashes. For example, we studied Facebook’s initial public offering on May 18, 2012. The company raised about $16 billion, but this massive offering was plagued by unexpected technical difficulties. The IPO was delayed for 30 minutes, causing turmoil across the markets for a few hours. In our effort to analyze what actually happened on this day, we tested our model to see if it could observe signals prior to the offering time. The model showed a high volatile period that affected many stocks, creating chaos in the markets. THE NEXT BIG CRASH Our next step is to generate an intelligent system that will automatically consider the data and warn the overseers about the impending disturbances in the market. If this warning arrives early enough, investors could potentially use it to take preventative measures. However, there are a few ways that the
model still needs to improve. Relying on early signals can lead to false positives, which we want
to minimize. Some of our recent results, not yet reported, suggest how many false positives and negatives occur. In a different study, not yet published, we describe a system that analyzes trading days when abnormalities were reported. We have found that our system was able to produce low false positive and negative rates. In light of these alarms, market regulators might then take the appropriate action. For example, they might prevent certain trades from being made or block particular traders from any activity for a period of time. Traders might choose to purchase shares at times when the stocks are falling in price, and sell during the times the prices are rising back to their precrash values. These actions might help reduce the crash’s impact. In other words, investors could exploit the crash to improve their positions – and simultaneously help dampen some of the crash’s effects.
Romesh Saigal is a professor of Industrial & Operations Engineering at the University of Michigan. Abdullah AlShelahi is a Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan.
Made with FlippingBook - Online Brochure Maker