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Stock Market Forecastability and Volatility: A Statistical Appraisal

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  • N. Gregory Mankiw
  • David Romer
  • Matthew D. Shapiro

Abstract

This paper presents and implements statistical tests of stock market forecastability and volatility that are immune from the severe statistical problems of earlier tests. It finds that although the null hypothesis of market efficiency is rejected, the rejections are only marginal. The paper also shows how volatility tests and recent regression tests are closely related, and demonstrates that when finite sample biases are taken into account, regression tests also fail to provide strong evidence of violations of the conventional valuation model.

Suggested Citation

  • N. Gregory Mankiw & David Romer & Matthew D. Shapiro, 1991. "Stock Market Forecastability and Volatility: A Statistical Appraisal," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 455-477.
  • Handle: RePEc:oup:restud:v:58:y:1991:i:3:p:455-477.
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    References listed on IDEAS

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    1. Summers, Lawrence H, 1986. " Does the Stock Market Rationally Reflect Fundamental Values?," Journal of Finance, American Finance Association, vol. 41(3), pages 591-601, July.
    2. Phillips, Peter C B, 1988. "Regression Theory for Near-Integrated Time Series," Econometrica, Econometric Society, vol. 56(5), pages 1021-1043, September.
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    5. Mankiw, N Gregory & Romer, David & Shapiro, Matthew D, 1985. " An Unbiased Reexamination of Stock Market Volatility," Journal of Finance, American Finance Association, vol. 40(3), pages 677-687, July.
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    12. N. Gregory Mankiw & Matthew D. Shapiro, 1985. "Do We Reject Too Often? Small Sample Bias in Tests of Rational Expectations," Cowles Foundation Discussion Papers 743, Cowles Foundation for Research in Economics, Yale University.
    13. Scott, Louis O, 1985. "The Present Value Model of Stock Prices: Regression Tests and Monte Carlo Results," The Review of Economics and Statistics, MIT Press, vol. 67(4), pages 599-605, November.
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