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The Likelihood of Various Stock Market Return Distributions, Part 1: Principles of Inference


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  • Markowitz, Harry M
  • Usmen, Nilufer


This is the first of two articles which apply certain principles of inference to a practical, financial question. The present article argues and cites arguments which contend that decision making should be Bayesian, that classical (R. A. Fisher, Neyman-Pearson) inference can be highly misleading for Bayesians as can the use of diffuse priors, and that Bayesian statisticians should show remote clients with a variety of priors how a sample implies shifts in their beliefs. We also consider practical implications of the fact that human decision makers and their statisticians cannot fully emulate Savage's rational decision maker. Copyright 1996 by Kluwer Academic Publishers

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Bibliographic Info

Article provided by Springer in its journal Journal of Risk and Uncertainty.

Volume (Year): 13 (1996)
Issue (Month): 3 (November)
Pages: 207-19

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Handle: RePEc:kap:jrisku:v:13:y:1996:i:3:p:207-19

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Cited by:
  1. Turnbull, Bradley C. & Ghosh, Sujit K., 2014. "Unimodal density estimation using Bernstein polynomials," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 72(C), pages 13-29.
  2. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, Elsevier, vol. 234(2), pages 346-355.
  3. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, Elsevier, vol. 44(2), pages 169-203, May.
  4. Kevin Fergusson & Eckhard Platen, 2005. "On the Distributional Characterization of Log-returns of a World Stock Index," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 153, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Kevin Fergusson & Eckhard Platen, 2006. "On the Distributional Characterization of Daily Log-Returns of a World Stock Index," Applied Mathematical Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 13(1), pages 19-38.


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