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      <namePart>Li, Wenyi</namePart>
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   <titleInfo>
      <title>MODELING THE INFORMATION CONTENT OF THE LIMIT ORDER BOOK BY BAGGING</title>
   </titleInfo>
   <originInfo>
      <dateCreated keyDate="yes">2018</dateCreated>
   </originInfo>
   <note displayLabel="Degree Awarded">Fall 2018</note>
   <typeOfResource authority="aat" valueURI="http://vocab.getty.edu/page/aat/300028029">Dissertation</typeOfResource>
   <name type="corporate">
      <affiliation>Illinois Institute of Technology</affiliation>
   </name>
   <name type="corporate">
      <namePart>SSB / Stuart School of Business</namePart>
   </name>
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      <namePart>Cooper, Ricky</namePart>
   </name>
   <subject>
      <topic>Finance</topic>
   </subject>
   <subject>
      <topic>Computer science</topic>
   </subject>
   <subject>
      <topic>Operations research</topic>
   </subject>
   <subject>
      <topic>bagging</topic>
   </subject>
   <subject>
      <topic>high frequency trading</topic>
   </subject>
   <subject>
      <topic>information content</topic>
   </subject>
   <subject>
      <topic>machine learning</topic>
   </subject>
   <subject>
      <topic>profitability</topic>
   </subject>
   <subject>
      <topic>the limit order book</topic>
   </subject>
   <language>
      <languageTerm type="code" authority="rfc3066">en</languageTerm>
   </language>
   <abstract>I propose a bagging tree framework to study the information content of the limit order book in U.S. equity market. By measuring the predictability and profitability of the order book data up to 5 levels, I find that the limit orders book is informative. In addition to market orders, limit orders behind the best bid and ask prices also contributes to short-term future price movements. Finally, I design simple strategies to show that this information content can be effectively and consistently translated to economic value. My results may provide important implications for both researchers and market practitioners.</abstract>
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