A Model for Stock Price Prediction using the Soft Computing Approach | Neural Networks and ARIMA Model to Stock Price Prediction

Ayodele Adebiyi

ISBN 10: 3846509094 / ISBN 13: 9783846509098

Published by LAP Lambert Academic Publishing, 2012

About this Item

A Model for Stock Price Prediction using the Soft Computing Approach | Neural Networks and ARIMA Model to Stock Price Prediction | Ayodele Adebiyi | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783846509098 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: A Model for Stock Price Prediction using the Soft Computing Approach | Neural Networks and ARIMA Model to Stock Price Prediction
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3846509094
• ISBN 13: 9783846509098
• Binding: Taschenbuch
• Condition: Neu
• Seller catalogs:
  • Bücher

About this title

Synopsis

A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors’ confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts’ opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving

About the Author

Dr. Adebiyi Ayodele Ariyo holds B.Sc. in Computer Science, MBA, M.Sc. and Ph.D in Management Information System. He is a lecturer in the department of Computer and Information Sciences, Covenant University, Ota. His research interest include the application of soft computing techniques to real life problems and e-commerce solutions.