Date of Award

2018

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematics

First Advisor

Christopher M. Danforth

Second Advisor

William A. Gibson

Abstract

The U.S. National Market System (NMS), the largest marketplace in the world for securities and exchange traded funds, suffers from geographic market fragmentation which leads to reduced market efficiency.

Communication lines transmit price updates and other information between geographically isolated exchanges at varying speeds, bounded above by the speed of light.

Market participants have access to federally mandated information provided by the Securities Information Processor (SIP) and privately offered information provided by the exchanges, often called direct feeds.

These feeds are quantitatively and qualitatively distinct, with the direct feeds tending to provide more information at a faster rate than the SIP feed. Differences between the SIP and direct feeds can lead to information asymmetries between market participants, which in turn create arbitrage opportunities. Under the market conditions of the NMS in 2016, these arbitrage opportunities occur regularly and many can be captured by market participants with fast connectivity. Several methods exist which allow market participants to reduce their communication latency with trading centers, including the practice of co-location where market participants pay to have their trading infrastructure located in the same building as the matching engines of an exchange.

Such regularly occurring and executable arbitrage opportunities run counter to the Efficient-Market Hypothesis (EMH) in all forms, where even the weak form of the EMH claims that market participants should not be able to systematically profit from market inefficiencies.

This thesis investigates the market inefficiencies and related effects introduced by geographic market fragmentation in two baskets of stocks: the Dow Jones Industrial Average (Dow), and the 30 largest stocks by market capitalization in the Standard \& Poor's 500 index (S&P 30).

Language

en

Number of Pages

64 p.

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