An integrated stock market forecasting model using neural networks

Gary R. Weckman, Sriram Lakshminarayanan, Jon H. Marvel and Andy Snow

International Journal of Business Forecasting and Marketing Intelligence, 2008, vol. 1, issue 1, 30-49

Abstract: This paper focuses on the development of a stock market forecasting model based on artificial neural network architecture. A baseline neural network model was developed using GFF architecture. The performance of the baseline model was evaluated by using representative large-cap stocks in six critical industrial sectors. Key performance measures, which included correlation coefficient and mean square error, were identified and used to compare the different models. A self-organising map network was developed to reduce the set of 56 stock market indicators into a final set of 11 indicators that covered market momentum, market volatility, market trend, broad market indictors and general momentum indicators. The model still required additional developments to better forecast turning points in the market. Based on Elliot's Wave Theory, two additional indicators were introduced to improve the forecast accuracy for turning points.

Keywords: artificial neural networks; ANNs; stock market forecasting; self organising maps; fuzzy set theory; technical indicators; generalised feed forward networks; turning points; stock markets; performance measures; market momentum; market volatility; market trends; broad market indictors; general momentum indicators; forecast accuracy. (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (2)

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