Convergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach

Dr. Ravindran Ramasamy; Tan Chee Siang

Convergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach

Dr. Ravindran Ramasamy * §

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Tan Chee Siang

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§ University Tun Abdul Razak

Convergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach

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Abstract

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.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Ravindran Ramasamy, Tan Chee Siang. 2013. "Convergence of Actual and Predicted Share Prices a An ADALINE Neural Network Approach". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 13 (GJCST Volume 13 Issue E2).

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GJCST Volume 13 Issue E2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

adaline learning rate Neural Network neuron share return synapse

Classification

GJCST-E Classification I.2.6

Submission ReceivedDecember 16, 2012
Peer Review Double Blind
Handling Editor
Accepted January 3, 2013
Published January 15, 2013

Version of record

v1.2

Issue date

March 9, 2013

Language

English

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Total Score: 147

Country: Unknown

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Ravindran Ramasamy, Tan Chee Siang (PhD/Dr. count: 1)

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