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Title:A thesis on algorithmic trading
Author(s):Yu, Yingjie
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(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.
Issue Date:2015-04-28
Rights Information:Copyright 2015 Yingjie Yu
Date Available in IDEALS:2015-07-22
Date Deposited:May 2015

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