Cookies?
Library Header Image
LSE Research Online LSE Library Services

Using equity premium survey data to estimate future wealth

Freeman, Mark C. and Groom, Ben ORCID: 0000-0003-0729-143X (2014) Using equity premium survey data to estimate future wealth. Review of Quantitative Finance and Accounting, 45 (4). pp. 665-963. ISSN 0924-865X

[img]
Preview
PDF - Accepted Version
Download (591kB) | Preview

Identification Number: 10.1007/s11156-014-0451-7

Abstract

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.

Item Type: Article
Official URL: http://link.springer.com/article/10.1007%2Fs11156-...
Additional Information: © 2014 Springer Science and Business Media New York.
Divisions: Geography & Environment
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > H Social Sciences (General)
JEL classification: G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice; Investment Decisions
Date Deposited: 23 Jun 2014 15:50
Last Modified: 12 Dec 2024 00:39
URI: http://eprints.lse.ac.uk/id/eprint/57161

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics