University of Illinois Urbana-Champaign

A thesis on algorithmic trading

Yu, Yingjie

Content Files
YU-THESIS-2015.pdf

Permalink

https://hdl.handle.net/2142/78679

Description

Title
A thesis on algorithmic trading
Author(s)
Yu, Yingjie
Issue Date
2015-04-28
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Industrial Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2015-07-22T22:33:58Z
Keyword(s)
  • Algorithmic trading
  • Dynamic programming
Abstract
Algorithmic trading is one of the most phenomenal changes in the financial industry in the past decade. While the impacts are significant, the microstructure of algorithmic trading remains unknown.By using Diff-in-Diff analysis, this paper shows that for low price securities, algorithmic trading activities are more active than high price securities. Besides, algorithm trading per se may also trigger significant price impact. As a result, algorithmic order execution has to be dynamically adapted to real-time market environments. This makes dynamic programming (DP) the most natural approach. This paper builds a optimal order execution model using dynamic programming. It works with the mean-variance utilities of Almgren and Chriss (J. Risk, 3, 2000) to effectively express risk aversion of a typical trader. The new framework is demonstrated through building one particular style called MV-MVP, i.e., the mean-variance (MV) objective formulated upon the state variables of moneyness and volume participation (MVP). The MV-MVP style generalizes the VWAP strategy by facilitating dynamic reactions to moneyness and by embodying the popular street practice of trading aggressively or passively while in the money. Simulated dynamic trading paths illustrates the MV-MVP style oscillates around the VWAP strategy.
Graduation Semester
2015-5
Type of Resource
text
Permalink
http://hdl.handle.net/2142/78679
Copyright and License Information
Copyright 2015 Yingjie Yu

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