Big Data Finance 2014 to Discuss Market Fragmentation, Order Book Dynamics, the Cost of Latency

NEW YORK - Jan. 23, 2014 - PRLog -- Today’s markets are characterized by “fragmentation.”  Once unique, exchanges have found themselves increasingly replaced by a cottage industry of trading venues: competitive exchanges, dark pools, even brokers’ internalizers.  The differences among the newly-minted trading outposts can be stark: exchanges are regulated, dark pools largely are not; exchanges themselves can differ by deploying normal or inverted pricing; and the internalizers are still another cup of tea. One feature, however, remains surprisingly common to all the matching engines, and that feature is the presence of the limit order book.

The modern limit order book was pioneered by NASD in the 1970s as a way to keep track of all the real-time market and limit orders posted on the exchange.  The idea quickly blossomed into a dominant trading model and was adopted by various trading houses on Wall Street and beyond, in multiple asset classes, spanning equities, futures, options, fixed income and even foreign exchange markets. The last hold-out, the Singaporean Stock Exchange, practiced a different auction model until about 2005, when even it chose to adopt the limit order book model.

A really popular area of research in finance today is the study of dynamics in the limit order book.  According to the groundbreaking research of Professor Steve Shreve from Carnegie Mellon University, limit order books are not only common, they also have laws that describe the possible evolution of orders within.  Specifically, Professor Shreve shows which spots in the limit order book are likely to attract new orders, and which ones are not.  The findings are critical for trading venues, brokers, as well as sophisticated institutional investors seeking to gain an edge in trading and increase their returns.  Professor Shreve will present his findings at the Big Data Finance 2014 at NYU Courant on February 14, 2014 (http://www.BigDataFinanceConference.com).

That trading edge may also be enhanced with increases in the speed of trading.  Higher speed results in a lower delay between the time the trader or the trading algorithm places an order and the time the order is executed.  The delay is known as latency.  The higher the technology speed, the lower the latency.

As with most things in life, higher speed and lower latency do not come free.  As is often the case, the existence of pay-for-speed services has garnered its own share of opponents, some claiming that the advanced connectivity services result in a technological “arms race” on Wall Street.  Two new research papers, slated for presentation at the Big Data Finance 2014 conference show that this is simply not the case.  Sasha Stoikov’s research to be presented at the conference explicitly quantifies the cost of latency-induced delay.  Irene Aldridge, author of “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems, 2^nd edition” (Wiley, http://www.amazon.com/High-Frequency-Trading-Practical-Al...) will show that not only pay-for-speed functionality has a well-defined equilibrium cost, the pay-for-speed framework creates an equilibrium in the trading universe and does not at all result in an arms race.

Modern market fragmentation in foreign exchange, a fascinating, but little understood topic, will be discussed at the conference by James Sinclair, industry veteran and CEO of MarketFactory, a foreign exchange aggregator.

Big Data Finance 2014 Conference will take place at NYU Courant on February 14, 2014, 1-6 PM.  Please see http://www.BigDataFinanceConference.com for further details and to register while the tickets last.