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Article

Factor-Timing Model

Ronald Hua, Dmitri Kantsyrev and Edward Qian
The Journal of Portfolio Management Fall 2012, 39 (1) 75-87; DOI: https://doi.org/10.3905/jpm.2012.39.1.075
Ronald Hua
is a chief investment officer at Goldman Sachs Asset Management in New York, NY.
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  • For correspondence: ronald.hua@gs.com
Dmitri Kantsyrev
is a director of Equity at PanAgora Asset Management in Boston, MA.
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  • For correspondence: dkantsyrev@panagora.com
Edward Qian
is the chief investment officer of Multi Asset at PanAgora Asset Management in Boston, MA.
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  • For correspondence: eqian@panagora.com
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Abstract

In this article, the authors propose a novel framework for building a factor-timing model. They introduce the concept of Akaike’s information criterion for selecting conditioning variables and evaluating various factor-timing specifications. Balancing the tradeoff between in-sample precision and complexity of the problem is the central theme of the considered methodology. In addition, the authors show how to employ information ratio quantities to track improvements in the resulting model efficacy and quantify sources of added value of factor-timing models over traditional static models.

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The Journal of Portfolio Management: 39 (1)
The Journal of Portfolio Management
Vol. 39, Issue 1
Fall 2012
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Factor-Timing Model
Ronald Hua, Dmitri Kantsyrev, Edward Qian
The Journal of Portfolio Management Oct 2012, 39 (1) 75-87; DOI: 10.3905/jpm.2012.39.1.075

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Factor-Timing Model
Ronald Hua, Dmitri Kantsyrev, Edward Qian
The Journal of Portfolio Management Oct 2012, 39 (1) 75-87; DOI: 10.3905/jpm.2012.39.1.075
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  • Article
    • Abstract
    • OPTIMAL WEIGHTS FACTOR-TIMING MODEL WEIGHTS
    • CONDITIONING VARIABLE SELECTION
    • AKAIKE’S INFORMATION CRITERION
    • STEPWISE CONDITIONING VARIABLE SELECTION
    • INFORMATION RATIO QUANTITIES
    • CONDITIONAL MODEL IR
    • EXPECTED MODEL IR
    • EXPECTED MODEL IR SQUARED
    • MULTI-PERIOD IR
    • AN EMPIRICAL EXAMPLE
    • DESCRIPTION OF FACTORS AND CONDITIONING VARIABLES
    • CONDITIONING VARIABLE SELECTION
    • SOURCES OF IR IMPROVEMENT
    • CONCLUSION
    • APPENDIX
    • ENDNOTES
    • REFERENCES
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  • PDF (Subscribers Only)

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