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oai:www.ideals.illinois.edu:2142/78679 2023-07-11 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

Yu, Yingjie 2015-07-22T22:33:58Z 2015-07-22T22:33:58Z 2017-07-23T09:15:35Z 2015-05 2015-04-28 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. Submission published under a 24 month embargo labeled 'U of I only', the embargo will last until 2017-05-01 The student, Yingjie Yu, accepted the attached license on 2015-04-27 at 15:02. The student, Yingjie Yu, submitted this Thesis for approval on 2015-04-27 at 15:22. This Thesis was approved for publication on 2015-04-28 at 09:11. DSpace SAF Submission Ingestion Package generated from Vireo submission #8128 on 2015-07-22 at 14:18:55 Made available in DSpace on 2015-07-22T22:33:58Z (GMT). No. of bitstreams: 2 YU-THESIS-2015.pdf: 560461 bytes, checksum: b2c22947c75c89ac366852f60043d6b4 (MD5) LICENSE.txt: 4207 bytes, checksum: 7374f392f6d20eb41cba05f553ff876c (MD5) Previous issue date: 2015-04-28 Embargo set by: Seth Robbins for item 79920 Lift date: 2017-07-22T22:34:16Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 79920 on 2017-07-23T09:15:35Z. application/pdf http://hdl.handle.net/2142/78679 en Copyright 2015 Yingjie Yu Algorithmic trading Dynamic programming A thesis on algorithmic trading text text 2015-5 Industrial&Enterprise Sys Eng Industrial Engineering University of Illinois at Urbana-Champaign Thesis M.S.