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The Journal of Financial Data Science

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Modeling Analysts’ Recommendations via Bayesian Machine Learning

David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor and Andrew Sinclair
The Journal of Financial Data Science Winter 2019, 1 (1) 75-98; DOI: https://doi.org/10.3905/jfds.2019.1.1.075
David Bew
is principal engineer at Man-AHL in Oxford, United Kingdom
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Campbell R. Harvey
is professor of finance at Duke University in Durham, NC, and Investment Strategy Advisor to Man Group in London, UK
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Anthony Ledford
is chief scientist at Man-AHL and an associate at Oxford-Man Institute of Quantitative Finance in Oxford, United Kingdom
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Sam Radnor
is senior vice president at quant PORT in London, United Kingdom
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Andrew Sinclair
is senior quantitative analyst at Realindex Investments in Sydney, Australia
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Abstract

Individual analysts typically publish recommendations several times per year on the handful of stocks they follow within their specialized fields. How should investors interpret this information? How can they factor in the past performance of individual analysts when assessing whether to invest long or short in a stock? This is a complicated problem to model quantitatively: There are thousands of individual analysts, each of whom follows only a small subset of the thousands of stocks available for investment. Overcoming this inherent sparsity naturally raises the question of how to learn an analyst’s forecasting ability by integrating track-record information from all the stocks the analyst follows; in other words, inferring an analyst’s ability on Stock X from track records on both Stock X and stocks other than X. The authors address this topic using a state-of-the-art computationally rapid Bayesian machine learning technique called independent Bayesian classifier combination (IBCC), which has been deployed in the physical and biological sciences. The authors argue that there are many similarities between the analyst forecasting problem and a very successful application of IBCC in astronomy, a study in which it dominates heuristic alternatives including simple or weighted averages and majority voting. The IBCC technique is ideally suited to this particularly sparse problem, enabling computationally efficient inference, dynamic tracking of analyst performance through time, and real-time online forecasting. The results suggest the IBCC technique holds promise in extracting information that can be deployed in active discretionary and quantitative investment management.

TOPICS: Security analysis and valuation, big data/machine learning, equity portfolio management

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Modeling Analysts’ Recommendations via Bayesian Machine Learning
David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor, Andrew Sinclair
The Journal of Financial Data Science Jan 2019, 1 (1) 75-98; DOI: 10.3905/jfds.2019.1.1.075

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Modeling Analysts’ Recommendations via Bayesian Machine Learning
David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor, Andrew Sinclair
The Journal of Financial Data Science Jan 2019, 1 (1) 75-98; DOI: 10.3905/jfds.2019.1.1.075
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  • Article
    • Abstract
    • DESCRIPTION OF THE DATA MODELING PROBLEM
    • THE IBCC MODEL: PROBABILISTIC SPECIFICATION AND CONSTRUCTION OF THE POSTERIOR
    • VARIATIONAL BAYESIAN INFERENCE
    • EMPIRICAL RESULTS AND ROBUSTNESS CHECKS
    • MACHINE LEARNING IN ACTION
    • CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
    • ACKNOWLEDGMENT
    • ENDNOTES
    • REFERENCES
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  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

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