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Using equity premium survey data to estimate future wealth

Listed author(s):
  • 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. Copyright Springer Science+Business Media New York 2015

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File URL: http://hdl.handle.net/10.1007/s11156-014-0451-7
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Article provided by Springer in its journal Review of Quantitative Finance and Accounting.

Volume (Year): 45 (2015)
Issue (Month): 4 (November)
Pages: 665-693

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Handle: RePEc:kap:rqfnac:v:45:y:2015:i:4:p:665-693
DOI: 10.1007/s11156-014-0451-7
Contact details of provider: Web page: http://springer.com

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  1. Narasimhan Jegadeesh & Woojin Kim, 2010. "Do Analysts Herd? An Analysis of Recommendations and Market Reactions," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 901-937, February.
  2. Mehra, Rajnish & Prescott, Edward C., 1985. "The equity premium: A puzzle," Journal of Monetary Economics, Elsevier, vol. 15(2), pages 145-161, March.
  3. Vivek Singh, 2013. "Did institutions herd during the internet bubble?," Review of Quantitative Finance and Accounting, Springer, vol. 41(3), pages 513-534, October.
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  12. Eric Jacquier & Alex Kane & Alan J. Marcus, 2005. "Optimal Estimation of the Risk Premium for the Long Run and Asset Allocation: A Case of Compounded Estimation Risk," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(1), pages 37-55.
  13. Ferstl, Robert & Weissensteiner, Alex, 2011. "Asset-liability management under time-varying investment opportunities," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 182-192, January.
  14. Ramnath, Sundaresh & Rock, Steve & Shane, Philip, 2008. "The financial analyst forecasting literature: A taxonomy with suggestions for further research," International Journal of Forecasting, Elsevier, vol. 24(1), pages 34-75.
  15. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
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  19. repec:dau:papers:123456789/2326 is not listed on IDEAS
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