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Activity Number:
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344
- Methods in Financial Econometrics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #312670
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Title:
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Data Driven Individualized Portfolio Recommendation
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Author(s):
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Duyeol Lee* and Emily Butler and Eric B. Laber and Michael R. Kosorok
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Companies:
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University of North Carolina at Chapel Hill and GSK and North Carolina State University and University of North Carolina at Chapel Hill
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Keywords:
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Portfolio selection;
Utility functions;
Item response theory;
Market risk;
Risk preference
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Abstract:
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Conventional models that financial advisors use to construct individual investors' portfolios are based in part on the level of investor risk preference. However, little is known about how advisors measure risk preference and incorporate it into their models. A recent study using Canadian household data showed that client characteristics explained only 12% of the variation in risky share. We propose an estimator of an optimal portfolio strategy that is deeply tailored to individual investor risk preference. This estimator is both consistent with economic theory and suitable for practical implementation. Simulation results indicate that the proposed method outperforms less tailored strategy and fixed strategies.
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Authors who are presenting talks have a * after their name.
