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A quantitative equity strategy based on factors formed by industries in the S&P500

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dc.contributor.advisor John DeTore. en_US Liu, Jack Wen-Hao en_US
dc.contributor.other Sloan School of Management. en_US 2011-09-13T17:55:34Z 2011-09-13T17:55:34Z 2011 en_US 2011 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 25). en_US
dc.description.abstract This paper presents and simulates a long-short market-neutral quantitative equity trading strategy for US stocks. First, economic intuition and academic researches for which this trading strategy is based upon will be explained. Second, to ensure that the trading strategy simulation would be as realistic as possible, I will introduce some trading constraints, investment guidelines, and other assumptions/ restrictions about the strategy's backtest setting. Third, I will put in detail how the trading model is built and how the strategy is executed. Fourth, the strategy's backtest result will be presented. Fifth, I will use some risk factors to analyze the strategy's performance as well as compare the strategy's results against these risk factors. Lastly, I conclude with several insights drawn from this research on quantitative investment. en_US
dc.description.statementofresponsibility by Jack Wen-Hao Liu. en_US
dc.format.extent 31 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights 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. en_US
dc.rights.uri en_US
dc.subject Sloan School of Management. en_US
dc.title A quantitative equity strategy based on factors formed by industries in the S&P500 en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Sloan School of Management. en_US
dc.identifier.oclc 750498676 en_US

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