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      <namePart>Jiao, Zixuan</namePart>
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   <titleInfo>
      <title>Sharpen Quality Investing: A PLS-based Approach</title>
   </titleInfo>
   <originInfo>
      <dateCreated keyDate="yes">2022</dateCreated>
   </originInfo>
   <note displayLabel="Degree Awarded">Spring 2022</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>
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      <namePart>Cooper, Ricky</namePart>
   </name>
   <subject>
      <topic>Finance</topic>
   </subject>
   <subject>
      <topic>Asset Pricing</topic>
   </subject>
   <subject>
      <topic>Dimension Reduction</topic>
   </subject>
   <subject>
      <topic>Extreme Market</topic>
   </subject>
   <subject>
      <topic>Machine Learning</topic>
   </subject>
   <subject>
      <topic>PLS</topic>
   </subject>
   <subject>
      <topic>Quality Factor</topic>
   </subject>
   <language>
      <languageTerm type="code" authority="rfc3066">en</languageTerm>
   </language>
   <abstract>I apply a disciplined dimension reduction technique called Partial Least Square (PLS) to construct a new quality factor by aggregating information from 16 individual signals. It earns significant risk-adjusted returns and outperforms quality factors constructed by alternative techniques, namely, PCA, Fama-Macbeth regression, a combination of PCA and Fama-Mabeth regression and a Rank-based approach. I show that my quality factor performs even better during rough economic patches and thus appears to hedge periods of market distress. I further show adding our quality factor to an opportunity set consisting of the other classical factors increases the maximum Sharpe ratio.</abstract>
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