Regulators and investors are struggling to meet the challenges posed by high-frequency trading. This ultra-fast, computerized segment of finance now accounts for most trades. HFT also contributed to the “flash crash,” the sudden, vertiginous fall in the Dow Jones Industrial Average in May 2010, according to U.S. regulators. However, the HFT of today is very different to that of three years ago. This is because of “big data.”
The term describes data sets that are so large or complex (or both) that they cannot be efficiently managed with standard software. Financial markets are significant producers of big data: trades, quotes, earnings statements, consumer research reports, official statistical releases, polls, news articles, etc.
The issue at stake is, how do we make sure that participants use big data responsibly? As a Harvard Business Review article put it, big data requires big judgment. A few years ago the CFTC considered whether regulators should certify traders’ algorithms. The potential for interference would be huge, not to mention the risk of intellectual property theft. A compromise may consist of market participants proposing a set of real-time indices that track “reckless” behavior, such as adding selling pressure to a market with dwindling buyers. If a trader crosses several “recklessness” thresholds, they could be prosecuted. These indices can be changed as markets evolve and, most importantly, they could be defined by consensus among all market participants.
One solution here is to employ the resources of the U.S.’s National Laboratories. The Lawrence Berkeley National Laboratory has the supercomputing power and analysis techniques required to monitor these “recklessness” indices in real time and advise regulators of reckless market behavior that threatens stability. While traditional circuit-breakers halt trading after a market plunge, real-time monitoring would allow for the shutting down of individual participants, preserving the market for bona fide actors.
Full article: Financial Markets Are at Risk of a ‘Big Data’ Crash (CNBC)