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Using equity premium survey data to estimate future wealth

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journal contribution
posted on 12.02.2015, 12:19 authored by Mark Freeman, Ben Groom
We present the first systematic methods for combining different experts' responses to equity premium surveys. These techniques are based on the observation that the survey data are approximately gamma distributed. This distribution has convenient analytical properties that enable us to address three important problems that investment managers must face. First, we construct probability density functions for the future values of equity index tracker funds. Second, we calculate unbiased and minimum least square error estimators of the future value of these funds. Third, we derive optimal asset allocation weights between equities and the risk-free asset for risk-averse investors. Our analysis allows for both herding and biasedness in expert responses. We show that, unless investors are highly uncertain about expert biases or forecasts are very highly correlated, many investment decisions can be based solely on the mean of the survey data minus any expected bias. We also make recommendations for the design of future equity premium surveys.

History

School

  • Business and Economics

Department

  • Business

Published in

Review of Quantitative Finance and Accounting

Citation

FREEMAN, M. and GROOM, B., 2015. Using equity premium survey data to estimate future wealth. Review of Quantitative Finance and Accounting, 45(4), pp.665-693.

Publisher

© Springer Science+Business Media

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2015

Notes

This article was published in the journal, Review of Quantitative Finance and Accounting. The final publication is available at Springer via http://dx.doi.org/10.1007/s11156-014-0451-7

ISSN

0924-865X

eISSN

1573-7179

Language

en