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Forecasting Stock Market Averages to Enhance Profitable Trading Strategies

Author

Listed:
  • Haefke, Christian

    (Department of Economics, Institute for Advanced Studies, Vienna)

  • Helmenstein, Christian

    (Department of Economics, Institute for Advanced Studies, Vienna)

Abstract

In this paper we design a simple trading strategy to exploit the hypothesized distinct informational content of the arithmetic and geometric mean. The rejection of cointegration between the two stock market indicators supports this conjecture. The profits generated by this cheaply replicable trading scheme cannot be expected to persist. Therefore we forecast the averages using autoregressive linear and neural network models to gain a competitive advantage relative to other investors. Refining the trading scheme using the forecasts further increases the mean return as compared to a buy and hold strategy.

Suggested Citation

  • Haefke, Christian & Helmenstein, Christian, 1995. "Forecasting Stock Market Averages to Enhance Profitable Trading Strategies," Economics Series 21, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:21
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    File URL: https://irihs.ihs.ac.at/id/eprint/879
    File Function: First version, 1995
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    Cited by:

    1. is not listed on IDEAS
    2. Sevastianov, P. & Dymova, L., 2009. "Synthesis of fuzzy logic and Dempster–Shafer Theory for the simulation of the decision-making process in stock trading systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(3), pages 506-521.

    More about this item

    Keywords

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    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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