Tuesday, May 21, 2013

Big Data and Big Crashes

Today the FT carries an op-ed by two of the most respected finance academics in market microstructure, Maureen O'Hara and David Easley.

The heavy number-crunching behind many hedge-funds nowadays poses a new type of risk to financial markets arising from erroneous interpretation of information. They write:
"
About two years ago, it became common for hedge funds to extract market sentiment from social media. The idea is to develop trading algorithms based on the millions of messages posted by users of Twitter, Facebook, chat rooms and blogs, and detect demand trends in relation to individual companies. However, these algorithms typically do a bad job when it comes to making guesses on small data sets. In recent months, it has become very popular to develop algorithms that fire off orders as soon as unscheduled information is published, such as natural disasters or terrorist attacks. More hash crash-type events, which are caused by a single erroneous data point, are disasters waiting to happen."

 Computers need not only to process things fast but also correctly. If something is mistakenly posted in AP's newsfeed, a human being might check it before acting while a computer wouldn't (at least until AI is here for good).

Nowadays, I'm leaning more and more towards setting some sort of minimum trading time. I guess that 0.1secs won't help much with the type of crash described above, but that's another story.

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