Forecasting the stock market with linguistic rules generated from the minimize entropy principle and the cumulative probability distribution approaches
type: Item
instance of: scholarly article
title: Forecasting the stock market with linguistic rules generated from the minimize entropy principle and the cumulative probability distribution approaches
zbMATH Open document ID: 1229.91377
author: Chung-Ho Su
author: Tai-Liang Chen
author: Ching-Hsue Cheng
author: Ya-Ching Chen
published in: Entropy
publication date: 2012-01-09T00:00:00Z
review text: Summary: To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment decisions; and (2) the time complexity of the algorithms to produce forecasting outcomes is very high. Therefore, to provide understandable rules for investors and to reduce the time complexity of forecasting algorithms, this paper proposes a novel model for the forecasting process, which combines two granulating methods (the minimize entropy principle approach and the cumulative probability distribution approach) and a rough set algorithm. The model verification demonstrates that the proposed model surpasses the three listed conventional fuzzy time-series models and a multiple regression model (MLR) in forecast accuracy.
Mathematics Subject Classification ID: 91G80
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 91B80
zbMATH DE Number: 5995911
zbMATH Keywords: minimize entropy principle approach
zbMATH Keywords: cumulative probability distribution approach
zbMATH Keywords: rough set theory
zbMATH Keywords: stock market forecasting
describes a project that uses: LERS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e12122397
OpenAlex ID: W2122513151
cites work: Rough sets
cites work: Q3835838
cites work: Fuzzy sets
cites work: Rudiments of rough sets
cites work: An improved accuracy measure for rough sets
cites work: Forecasting enrollments using high-order fuzzy time series and genetic algorithms
cites work: Q4353916
DOI: 10.3390/E12122397
description: scientific article; zbMATH DE number 5995911