Files in this item

FilesDescriptionFormat

application/pdf

application/pdfGerig_Austin.pdf (1MB)Restricted to U of Illinois
Gerig_AustinPDF

Description

Title:A Theory for Market Impact: How Order Flow Affects Stock Price
Author(s):Gerig, Nathaniel Austin
Doctoral Committee Chair(s):Hubler, A.W.
Department / Program:Physics
Discipline:Physics
Degree:Ph.D.
Genre:Dissertation
Subject(s):Stock Price
Theory of Asymmetric Liquidity
Theory of Market Impact
Abstract:It is known that the impact of transactions on stock price (market impact) is a concave function of the size of the order, but there exists little quantitative theory that suggests why this is so. I develop a quantitative theory for the market impact of hidden orders (orders that reflect the true intention of buying and selling) that matches the empirically measured result and that reproduces some of the non-trivial and universal properties of stock returns (returns are percent changes in stock price). The theory is based on a simple premise, that the stock market can be modeled in a mechanical way - as a device that translates order flow into an uncorrelated price stream. Given that order flow is highly autocorrelated, this premise requires that market impact (1) depends on past order flow and (2) is asymmetric for buying and selling. I derive the specific form for the dependence in (1) by assuming that current liquidity responds to information about all currently active hidden orders (liquidity is a measure of the price response to a transaction of a given size). This produces an equation that suggests market impact should scale logarithmically with total order size. Using data from the London Stock Exchange I empirically measure market impact and show that the result matches the theory. Also using empirical data, I qualitatively specify the asymmetry of (2). Putting all results together, I form a model for market impact that reproduces three universal properties of stock returns - that returns are uncorrelated, that returns are distributed with a power law tail, and that the magnitude of returns is iii highly autocorrelated (also known as clustered volatility).
Issue Date:2007-05
Genre:Dissertation / Thesis
Type:Text
Language:English
URI:http://hdl.handle.net/2142/34736
Rights Information:©2007 Austin Nathaniel Gerig
Date Available in IDEALS:2012-10-12
Identifier in Online Catalog:10247912


This item appears in the following Collection(s)

Item Statistics