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The Journal of Financial Data Science

The Journal of Financial Data Science

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Article

Fund Asset Inference Using Machine Learning Methods: What’s in That Portfolio?

David Byrd, Sourabh Bajaj and Tucker Hybinette Balch
The Journal of Financial Data Science Summer 2019, jfds.2019.1.005; DOI: https://doi.org/10.3905/jfds.2019.1.005
David Byrd
is a research scientist at the Georgia Institute of Technology in Atlanta, GA
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Sourabh Bajaj
is a former student of the Georgia Institute of Technology in Atlanta, GA
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Tucker Hybinette Balch
is a professor of computer science at the Georgia Institute of Technology and a co-founder of Lucena Research, Inc in Atlanta, GA
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Abstract

Given only the historic net asset value of a large-cap mutual fund, which members of some universe of stocks are held by the fund? Discovering an exact solution is combinatorially intractable because there are, for example, C(500, 30) or 1.4 × 1048 possible portfolios of 30 stocks drawn from the S&P 500. The authors extend an existing linear clones approach and introduce a new sequential oscillating selection method to produce a computationally efficient inference. Such techniques could inform efforts to detect fund window dressing of disclosure statements or to adjust market positions in advance of major fund disclosure dates. The authors test the approach by tasking the algorithm with inferring the constituents of exchange-traded funds for which the components can be later examined. Depending on the details of the specific problem, the algorithm runs on consumer hardware in 8 to 15 seconds and identifies target portfolio constituents with an accuracy of 88.2% to 98.6%.

TOPICS: Big data/machine learning, statistical methods, portfolio management/multi-asset allocation

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The Journal of Financial Data Science: 3 (1)
The Journal of Financial Data Science
Vol. 3, Issue 1
Winter 2021
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Fund Asset Inference Using Machine Learning Methods: What’s in That Portfolio?
David Byrd, Sourabh Bajaj, Tucker Hybinette Balch
The Journal of Financial Data Science Jun 2019, jfds.2019.1.005; DOI: 10.3905/jfds.2019.1.005

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Fund Asset Inference Using Machine Learning Methods: What’s in That Portfolio?
David Byrd, Sourabh Bajaj, Tucker Hybinette Balch
The Journal of Financial Data Science Jun 2019, jfds.2019.1.005; DOI: 10.3905/jfds.2019.1.005
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  • Article
    • Abstract
    • BACKGROUND AND RELATED WORK
    • THE PORTFOLIO INFERENCE PROBLEM
    • APPROACH
    • EXPERIMENTAL METHODOLOGY
    • EXPERIMENTAL RESULTS
    • DISCUSSION
    • ACKNOWLEDGMENTS
    • ADDITIONAL READING
    • REFERENCES
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More in this TOC Section

  • Managing Editor’s Letter
  • Managing Editor’s Letter
  • Portfolio Construction Using First Principles Preference Theory and Machine Learning
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