Yet another blog posting on stats, and this one was inspired by Wikipedia of all places. I was looking for some stats on High Frequency (HFT) and Algorithmic trading, and stumbled across a great range of facts and figures.
First, we need to just say that HFT and Algo Trading are different.
Algorithmic trading just refers to all and any form of trading using programmed systems that automate the trade cycle.
HFT is a more specific area, and uses algo tools to move equities fast. In other words, the holding of stock in an HFT strategy might be for seconds or parts of seconds, whilst algo focuses upon those plus holdings for the medium and long term.
So there is a difference.
That’s why, when Wikipedia quotes Aite Group as saying that high frequency trading firms account for 73% of all US equity trading volume in 2009, they’re wrong.
OK, as usual, Wikipedia have got it wrong as, according to Reuters who should know, high-speed trading “accounts for about 60 percent of U.S. equity volume”.
Bloomberg agree, quoting TABB Group who state that HFT accounts for 61% of equities volume, up from 35% in 2007.
There’s the key point. HFT is big news, especially when it causes trillion dollar rises and falls.
This is well illustrated when we think about my old forecast that the markets will be run by a man and his dog: the man’s there to feed the dog and the dog’s there to stop the man touching the keyboard.
Don’t think that’s true?
Back in 2006, 40% of all orders were entered by algo traders at the London Stock Exchange, rising to 60% in 2007 and now estimates believe that around 80% of trading is automated thanks to so many MTFs.
Long live the man and his dog!
On that note, I’ve used this analogy for a long while now (see Question 4 of Euromoney’s 2007 Christmas Quiz) but, in researching this stuff again, was surprised to see this conclusion of an interview with yours truly in June 2007:
“As brokers and exchanges scramble to adjust, those who face the biggest risks from algorithmic trading may very possibly be small-scale investors — those most reliant on market stability, good governance and fair access to information and technology.
“‘Many of the algorithmic trading strategies include risk strategies untested by market downturns,’ warns Balatro's Chris Skinner. ‘A lot of the traditional market stabilizers are being taken out’ by the trading speed and fluidity enabled by algorithmic trading, he adds.
“The biggest stabilizer of all, he notes, is perhaps the role of local and national financial regulators.“But in the new era of algorithmic trading, in which trading technologies and strategies are closely-held competitive secrets fiercely protected from scrutiny, Skinner says regulators will have a tough time gaining sufficient information to ‘understand what they are regulating.’ Especially if it's happening in 10 or 20 jurisdictions simultaneously. It all starts to make Enron's famously complex trading strategies look simple, he adds.
“After all, Skinner notes, if major Canadian banks find it necessary to outsource their algorithmic trading expertise because it's too expensive to go it alone, how likely is it that our notoriously parsimonious financial regulators — with their laughably small technology budgets — will be able to ensure the world's increasingly fleet-footed algo-experts always play fair?”