Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Accurate forecasting of share prices is needed for fund managers and institutional investors for hedging decisions. Robust forecasting results will not only increase the effectiveness of hedging and reduce the hedging costs but also provide benchmarks for controlling and decision making. Existing traditional models for forecasting share prices rarely produce fair results. In this paper we have applied neural net work ADALINE approach to forecast the share prices listed in the Malaysian stock exchange. Adaptive linear neural net uses a moving window approach in updating its weights while training and this improves the accuracy of forecasting. We applied this technique on four share prices at four learning rates and the results nicely converge with the actual prices at higher learning rates. Our findings will increase the confidence in forecasting and will be helpful for stakeholders immensely.
. 2013. \u201cConvergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 13 (GJCST Volume 13 Issue E2): .
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Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Accurate forecasting of share prices is needed for fund managers and institutional investors for hedging decisions. Robust forecasting results will not only increase the effectiveness of hedging and reduce the hedging costs but also provide benchmarks for controlling and decision making. Existing traditional models for forecasting share prices rarely produce fair results. In this paper we have applied neural net work ADALINE approach to forecast the share prices listed in the Malaysian stock exchange. Adaptive linear neural net uses a moving window approach in updating its weights while training and this improves the accuracy of forecasting. We applied this technique on four share prices at four learning rates and the results nicely converge with the actual prices at higher learning rates. Our findings will increase the confidence in forecasting and will be helpful for stakeholders immensely.
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