- Utah man dies of bubonic plague
- Deaths from MERS virus surge in Saudi Arabia
- Planned Parenthood admits it manipulates rules on fetal organ sales to maximize profit
- Scientists fear tsunami would devastate Greece and Italy if ‘moderate’ earthquake hits
- South Korea plans ‘decapitation’ strike against North’s leadership if nuclear war is likely
- Tropical Storm Erika Soaks Puerto Rico, Hispanola; Warnings in the Bahamas; Uncertain Threat to Florida, Southeast U.S.
- Poroshenko Signs Secret Military Tech Deal With Anonymous Allies
- Huge explosions at US army base in Japan as warehouse burns and emergency services rush to scene
- China rocked by another fatal chemical plant explosion
- Russia, China kick off active phase of Sea of Japan naval drills
Financial Markets Are at Risk of a ‘Big Data’ Crash
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.
Companies that have relied on the first generation of HFT, where unsophisticated speed exploits price discrepancies, have had a tough few years. Profits from ultra-fast trading firms were 74 percent lower in 2012 compared with 2009, according to Rosenblatt Securities. Being fast is not enough. We, along with Marcos Lopez de Prado of the Lawrence Berkeley National Laboratory, have argued that HFT companies increasingly rely on “strategic sequential trading.” This consists of algorithms that analyse financial big data in an attempt to recognize the footprints left by specific market participants.