• 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