An integrated stock market forecasting model using neural networks

Gary R. Weckman · Sriram Lakshminarayanan · Jon Marvel · Andy P. Snow

39 citations0/0 files storedInternational Journal of Business Forecasting and Marketing Intelligence

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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.

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1990Stock market prediction system with modular neural networks 1998Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks 2001An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network 1995Neural networks for financial forecasting 2000Option price forecasting using neural networks 2003An intelligent forecasting system of stock price using neural networks 2004iJADE Stock Advisor: An Intelligent Agent Based Stock Prediction System Using Hybrid RBF Recurrent Network 1999Designing personalized intelligent financial decision support systems

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