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Nonlinear Mean Reversion in Stock Prices

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  • S. Manzan

Abstract

We investigate evidence for nonlinear mean reversion in yearly S\&P500 data from 1871 until 2001. We find that up to 1990 there is significant evidence of nonlinear mean reversion. In particular, stock prices are characterized by a persistent process close to the fundamental value. However, when prices deviate significantly a mean reverting regime is activated and prices adjust to fundamental values. Instead, the stock price run-up of the late 90s exacerbated the persistence of the deviations and there is no evidence for a mean reverting regime that drives prices back to fundamentals.

Suggested Citation

  • S. Manzan, 2004. "Nonlinear Mean Reversion in Stock Prices," Computing in Economics and Finance 2004 264, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:264
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    File URL: http://repec.org/sce2004/up.20169.1077974512.pdf
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    Cited by:

    1. Edward Bernard Bastiaan de Rivera y Rivera & Diógenes Manoel Leiva Martin & Emerson Fernandes Marçal & Leonardo Fernando Cruz Basso, 2012. "Present value model between prices and dividends with constant and time-varying expected returns: enterprise-level Brazilian stock market evidence from non-stationary panels," Brazilian Business Review, Fucape Business School, vol. 9(4), pages 51-86, October.
    2. Schlosser, William E., 2020. "Real price appreciation forecast tool: Two delivered log market price cycles in the Puget Sound markets of western Washington, USA, from 1992 through 2019," Forest Policy and Economics, Elsevier, vol. 113(C).
    3. Cunado, J. & Gil-Alana, L.A. & Gracia, Fernando Perez de, 2010. "Mean reversion in stock market prices: New evidence based on bull and bear markets," Research in International Business and Finance, Elsevier, vol. 24(2), pages 113-122, June.

    More about this item

    Keywords

    nonlinear time series; mean reversion;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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