Forecasting Stock Market Averages to Enhance Profitable Trading Strategies
Christian Haefke () and
Christian Helmenstein ()
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Christian Helmenstein: Department of Economics, Institute for Advanced Studies, Vienna
Computing in Economics and Finance 1996 from Society for Computational Economics
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. One of the most prominent mysteries of present day finance is the ample usage of such simple and dated concepts as the arithmetic and the geometric means as proxies for the aggregate price dynamics of leading international stock markets. While such undertakings may find their explanation, though not justification, in the inertia of the finance community to adopt more modern index concepts, it is even more astounding that during the last decade of the twentieth century some newly implemented stock market indexes are still constructed in the tradition of these principles.
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Working Paper: Forecasting Stock Market Averages to Enhance Profitable Trading Strategies (1995)
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More papers in Computing in Economics and Finance 1996 from Society for Computational Economics Department of Econometrics, University of Geneva, 102 Bd Carl-Vogt, 1211 Geneva 4, Switzerland. Contact information at EDIRC.
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