Forecasting Stock Market Averages to Enhance Profitable Trading Strategies
Christian Haefke () and
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Christian Helmenstein: Department of Economics, Institute for Advanced Studies, Vienna
No 21, Economics Series from Institute for Advanced Studies
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.
Keywords: Trading Strategy; Stock Market Index; Neural Networks; Cointegration (search for similar items in EconPapers)
JEL-codes: G14 C43 C45 C53 (search for similar items in EconPapers)
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http://www.ihs.ac.at/publications/eco/es-21.pdf First version, 1995 (application/pdf)
Working Paper: Forecasting Stock Market Averages to Enhance Profitable Trading Strategies
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