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Predictability of the daily high and low of the S&P 500 index

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  • Jones, Clive

Abstract

Ratios involving the current period opening price and the high or low price of the previous period are significant predictors of the current period high or low price for many stocks and stock indexes. This is illustrated with daily trading data from the S&P 500 index. Regressions specifying these “proximity variables” have higher explanatory and predictive power than benchmark autoregressive and “no change” models. This is shown with out-of-sample comparisons of MAPE, MSE, and the proportion of time models predict the correct direction or sign of change of daily high and low stock prices. In addition, predictive models incorporating these proximity variables show time varying effects over the study period, 2000 to February 2015. This time variation looks to be more than random and probably relates to investor risk preferences and changes in the general climate of investment risk.

Suggested Citation

  • Jones, Clive, 2015. "Predictability of the daily high and low of the S&P 500 index," MPRA Paper 62664, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:62664
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    File URL: https://mpra.ub.uni-muenchen.de/62664/1/MPRA_paper_62664.pdf
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    References listed on IDEAS

    as
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    5. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    predictability of stock prices; time varying parameters; proximity variable method for predicting stock prices; accuracy of proximity variable method compared with autoregressive and benchmark forecasts;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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