- In Congress, Kerry unsure if Iran wishes to destroy US
- Officials: Iran may take own samples at alleged nuclear site
- Jonathan Pollard, jailed spy for Israel, to be paroled November 20
- China may use microwave weapon against maritime rivals
- China conducts South China Sea live drill ‘to improve at-sea combat ability’
- China’s crusade to remove crosses from churches ‘is for safety concerns’
- USAF Chief Engineer: Directed Energy for Missile Defense ‘At Tipping Point’
- UN Peacekeepers Need Rapid Response Force, More Equipment – Gen. Dempsey
- Boy Scouts of America ends ban on gay adult leaders
- North Korea threatens to ‘leave no Americans alive’ as Kim Jong-un boasts of nuclear arsenal, on Korean War armistice anniversary
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.