DocumentCode
1511599
Title
BYY harmony learning, independent state space, and generalized APT financial analyses
Author
Xu, Lei
Author_Institution
Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, China
Volume
12
Issue
4
fYear
2001
fDate
7/1/2001 12:00:00 AM
Firstpage
822
Lastpage
849
Abstract
First, the relationship between factor analysis (FA) and the well-known arbitrage pricing theory (APT) for financial market is discussed comparatively, with a number of to-be-improved problems listed. An overview is made from a unified perspective on the related studies in the literatures of statistics, control theory, signal processing, and neural networks. Next, we introduce the fundamentals of the Bayesian Ying Yang (BYY) system and the harmony learning principle. We further show that a specific case of the framework, called BYY independent state space (ISS) system, provides a general guide for systematically tackling various FA related learning tasks and the above to-be-improved problems for the APT analyses. Third, on various specific cases of the BYY ISS system in three typical architectures, adaptive algorithms, regularization methods and model selection criteria are provided for either or both of parameter learning with automated model selection and parameter learning followed by model selection. Finally, we introduce some other financial applications that are based on the underlying independent factors via the APT analyses
Keywords
costing; financial data processing; hidden Markov models; learning (artificial intelligence); neural nets; principal component analysis; state-space methods; Bayesian Ying Yang system; arbitrage pricing theory; factor analysis; financial market; harmony learning; hidden Markov model; independent component analysis; independent state space; neural networks; parameter learning; portfolio; Adaptive algorithm; Control theory; Hidden Markov models; Independent component analysis; Macroeconomics; Portfolios; Predictive models; Pricing; State-space methods; Statistics;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.935094
Filename
935094
Link To Document