The Uber Frequency Trader – Part II


Earlier in the week, we posted a rebuttal to a Modern Markets Initiative (MMI) post about Uber being similar to HFT.  We noted a few similarities that MMI seemed to have left out of the article.  Well, after thinking about it some more and seeing a few articles yesterday about Uber, we would like update our list of Uber/HFT similarities that MMI left out of their article:

High cancellation rates:

Uber drivers have ordered and canceled more than 5,000 rides of a competitor, Lyft, according to CNN . Apparently, they are doing this to reduce the availability of Lyft drivers and manipulate customers to call on Uber.

High frequency traders cancel more than 99% of their orders.  They claim they do this so that they can adjust their inventory to changing market conditions.  If a human trader were to do this, it would be considered a manipulative activity called “spoofing”.

Ride/Quote Stuffing:

According to the WSJ , “Uber (drivers) frequently order a Lyft and then ride for only a few blocks, sometimes repeating this process dozens of times a day.”

Eric Hunsader at Nanex has detailed extensively numerous quote stuffing events in the stock market.  His theory is that a HFT trader is intentionally stuffing quotes in a particular stock to slow down the channel that the stock trades on.  Once slowed, the price of another stock in that channel can then be targeted.

Data Mining:

Uber records a tremendous amount of data about their customers including information on passenger habits (how patient are they, how price sensitive are they, etc.). Their privacy policy for their app is a quite a long document and contains details on what information they share about you.

High frequency traders constantly scan exchange data feeds for information on institutional and retail trading patterns.  Exchanges supply detailed direct data feeds that contain much more information than simply price and quantity of clients orders.  HFT’s can use this data to predict the behavior of less sophisticated  algorithms and trade ahead of those algos.


Now, don’t get us wrong, we’re not looking to pick on Uber.  They offer a terrific service for a good price but there are some caveats.  Uber appears to be another example of the “no free lunch” theory.  The same holds true for high frequency trading.  HFT proponents will argue that they add liquidity and shrink spreads, but the question becomes at what cost do they provide these so called benefits?  While HFT proponents will claim they are using technology to democratize their industries, we think the devil is in the details.