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

Clive Jones

MPRA Paper from University Library of Munich, Germany

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.

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 (search for similar items in EconPapers)
JEL-codes: C32 C58 G11 G17 (search for similar items in EconPapers)
Date: 2015-03-01
New Economics Papers: this item is included in nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:62664

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