NDLI: Recognizing the Pattern of Systematic Risk Based on Financial Ratios and Rough Set-Neural Network System

Content Provider	IEEE Xplore Digital Library
Author	Jian-Guo Zhou Zhao-Ming Wu Xiu Xin
Copyright Year	2006
Description	Author affiliation: Sch. of Bus. Adm., North China Electr. Power Univ., Baoding (Jian-Guo Zhou; Zhao-Ming Wu)
Abstract	Systematic risk that is presented by beta is the avoidless risk on the stock market. Beta is calculated by linear analysis between the daily prices of stocks and the security index of stock market. However, many studies have showed there are stronger relationships between beta and financial ratios. In this paper, a hybrid intelligent system is applied to recognize the clusters of beta with financial ratios, combining rough set approach and BP neural network. We can get reduced information table with no information loss by rough set approach. And then, this reduced information is used to develop classification rules and train network to infer appropriate parameters. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and BP neural network for one that dose not match any of them. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	2408
Ending Page	2412
File Size	244447
Page Count	5
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258770
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Neural networks Rough sets Stock markets Information systems Hybrid intelligent systems Educational institutions Electronic mail Information security Pricing Systematic risk Rough set BP neural-network Financial ratios
Content Type	Text
Resource Type	Article

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