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Time Series Forecasting in Stock Trading Markets: The Turning Point Curiosity

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  • Edward J. Lusk

    (School of Business and Economics, State University of New York (SUNY) at Plattsburgh, Plattsburgh, NY, USA)

Abstract

General Context Univariate Time Series Models [TSM] use only a Panel of historical data to produce forecasts. The tacit belief in using TSM is that the past information portends the future of the longitudinal data-stream. This is likely in certain cases such as strictly Ergodic Panel segments of sufficient size in the overall Panel. A question of interest is: Is the success of TSM in these contexts generalizable? The test of this question used a Litmus-Test design to examine the performance profile of TSM for a longitudinal time series the last point of which is a Turning Point [TP]. Specifically, the inference measurewill use the Relative Absolute Error [RAE] of the TSM tested over three forecasting horizons. In this testing, five TSM configurations were employed; the TPs are identified using a fixed screening filter applied to randomly selected firm Panels actively traded on the S&P500 from 2005 through 2013. There is no evidence that any of the five TSM outperformed the RW model which is incidentally the TP. The impact of these results is that one cannot assume that the effectiveness of TSM generalizes to all domains—in particular—forecasting after TPs that seems to be a Domain Lacuna where the effectiveness of TSM will be compromised Key Words:Domain Lacuna; Time Series Models;TurningPoint;Panels; Random Walk

Suggested Citation

  • Edward J. Lusk, 2019. "Time Series Forecasting in Stock Trading Markets: The Turning Point Curiosity," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(4), pages 01-16, July.
    • Handle: RePEc:rbs:ijbrss:v:8:y:2019:i:4:p:01-16
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    References listed on IDEAS

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