Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68332
Title: Companies Trading Signs Prediction Using Fuzzy Hybrid Operator with Swarm Optimization Algorithms
Authors: Panuwit Pholkerd
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Keywords: Computer Science
Engineering
Issue Date: 1-Jan-2020
Abstract: © 2020, Springer Nature Switzerland AG. Companies with the Trading Suspension (SP), and Non-Compliance (NC) sign posted might run a risk of bankruptcy. One would want to predict the SP or NC sign posted before it is posted to help in investing decision. In this paper, we introduce the prediction system using fuzzy hybrid operator with swarm intelligence optimization algorithm. In particular, we utilize the gamma operator with firefly, grey wolf, and social spider algorithms. The gamma operator with social spider yields 92.45% correct prediction result. We also compare our result with the support vector machine (SVM). The SVM yields 100% correct prediction. Although, the gamma operator is worse than SVM, the gamma operator can provide an influence information of inputs to the prediction output. The gamma operator provides that the debt ratio from the 8th previous quarter is the most influential input to the prediction whereas that from the 2nd to 6th previous quarters have small effect to the prediction.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075641829&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68332
ISSN: 21945365
21945357
Appears in Collections:CMUL: Journal Articles

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