2026-09-19T16:58:23Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1059822026-06-06T00:49:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jacob K. White.
Leung, Jason W
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:17:23Z
2016-12-22T15:17:23Z
2016
2016
http://hdl.handle.net/1721.1/105982
965785890
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-65).
Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be employed as an artificial trader used to trade on any given stock exchange. The performance of the model is evaluated using the S&P 500 index.
by Jason W. Leung.
M.Eng.
65 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Application of machine learning : automated trading informed by event driven data
Automated trading informed by event driven data
Thesis
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