BYY harmony learning, independent state space, and generalized APT financial analyses

Area of Science:

• Financial econometrics
• Statistical learning theory
• Machine intelligence

Background:

• The relationship between factor analysis (FA) and arbitrage pricing theory (APT) presents several challenges.
• Existing literature spans statistics, control theory, signal processing, and neural networks, lacking a unified perspective.

Purpose of the Study:

• To provide a unified framework for factor analysis and arbitrage pricing theory.
• To address limitations in current factor analysis and APT applications.
• To introduce novel adaptive algorithms, regularization methods, and model selection criteria.

Main Methods:

• Introduction of the Bayesian Ying Yang (BYY) system and harmony learning principle.
• Development of the BYY independent state space (ISS) system as a generalized framework.
• Application of BYY ISS to specific architectures, including adaptive algorithms, regularization, and model selection.

Main Results:

• The BYY ISS system provides a systematic approach to tackle FA learning tasks and APT problems.
• Novel algorithms and criteria are presented for parameter learning with automated or sequential model selection.
• Demonstration of financial applications based on independent factors derived from APT.

Conclusions:

• The BYY ISS framework offers a powerful and unified approach to factor analysis and financial econometrics.
• The proposed methods enhance parameter learning and model selection in APT.
• This research opens avenues for advanced financial modeling and analysis.