A thesis on algorithmic trading

Yu, Yingjie

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

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

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

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

Copyright and License Information

Copyright 2015 Yingjie Yu

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