A self-adaptive fuzzy-based optimised functional link artificial neural network model for financial time series prediction
by Soumya Das; Abhimanyu Patra; Sarojananda Mishra; Manas Ranjan Senapati
International Journal of Business Forecasting and Marketing Intelligence (IJBFMI), Vol. 2, No. 1, 2015

Abstract: In recent years, new data mining and machine learning techniques have been developed and applied to various fields of science. Out of these recently developed techniques few offer online support and are able to adapt to large and complex financial dataset. Therefore, the present research adopts Functional Link Artificial Neural Network (FLANN) model for predicting the closing price of three companies namely Yahoo Inc, Nokia and Bank of America. The FLANN model used is trained by fuzzy after normalisation of the data and closing price is forecasted for one day and one week ahead. The prediction result is compared with the parameters of the FLANN model trained by Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The proposed training method provides better accuracy and takes less time as compared to training the FLANN model using PSO or GA. The proposed approach has also been compared with a linear dataset for validation. The FLANN-fuzzy approach is seen to provide better results in predicting financial distress.

Online publication date: Tue, 15-Mar-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Forecasting and Marketing Intelligence (IJBFMI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com