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

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.

[1]  Jens Timmer,et al.  Modeling Volatility Using State Space Models , 1997, Int. J. Neural Syst..

[2]  Lei Xu,et al.  Bayesian Ying-Yang learning based ICA models , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  S. Ross The arbitrage theory of capital asset pricing , 1976 .

[5]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[6]  Andrew R. Barron,et al.  Minimum complexity density estimation , 1991, IEEE Trans. Inf. Theory.

[7]  Michael I. Jordan,et al.  Convergence results for the EM approach to mixtures of experts architectures , 1995, Neural Networks.

[8]  H. Bozdogan Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[9]  Gregory Connor,et al.  Performance Measurement with the Arbitrage Pricing Theory: A New Framework for Analysis , 1985 .

[10]  M. Stone Cross-validation:a review 2 , 1978 .

[11]  Herman Rubin,et al.  Statistical Inference in Factor Analysis , 1956 .

[12]  Lei Xu,et al.  Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, Three-Layer Nets and ME-RBF-SVM Models , 2001, Int. J. Neural Syst..

[13]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[14]  R. Zemel,et al.  Learning sparse multiple cause models , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  H. Akaike A new look at the statistical model identification , 1974 .

[16]  Gregory Connor,et al.  The Three Types of Factor Models: A Comparison of Their Explanatory Power , 1995 .

[17]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[18]  Andrzej Cichocki,et al.  Robust techniques for independent component analysis (ICA) with noisy data , 1998, Neurocomputing.

[19]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[20]  Lei Xu,et al.  Bayesian Kullback Ying-Yang dependence reduction theory , 1998, Neurocomputing.

[21]  Eric Saund,et al.  A Multiple Cause Mixture Model for Unsupervised Learning , 1995, Neural Computation.

[22]  Michael S. Lewis-Beck,et al.  Factor analysis and related techniques , 1994 .

[23]  Lang Tong,et al.  Waveform-preserving blind estimation of multiple independent sources , 1993, IEEE Trans. Signal Process..

[24]  Lei Xu,et al.  RBF nets, mixture experts, and Bayesian Ying-Yang learning , 1998, Neurocomputing.

[25]  Vladimir Vapnik,et al.  Methods of Pattern Recognition , 2000 .

[26]  Lei Xu,et al.  Least mean square error reconstruction principle for self-organizing neural-nets , 1993, Neural Networks.

[27]  Lei Xu,et al.  A Unified Learning Scheme: Bayesian-Kullback Ying-Yang Machines , 1995, NIPS.

[28]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[29]  Christian Jutten,et al.  Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..

[30]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[31]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[32]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[33]  Andreas S. Weigend,et al.  Nonlinear Trading Models Through Sharpe Ratio Maximization , 1997, Int. J. Neural Syst..

[34]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[35]  Andrew D. Back,et al.  A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..

[36]  Lei Xu,et al.  Best Harmony Learning , 2000, IDEAL.

[37]  Lei Xu,et al.  BYY learning system and theory for parameter estimation, data smoothing based regularization and model selection , 2000, Neural Parallel Sci. Comput..

[38]  Shun-ichi Amari,et al.  Learned parametric mixture based ICA algorithm , 1998, Neurocomputing.

[39]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[40]  S. Amari,et al.  Nonlinearity and Separation Capability: Further Justiication for the Ica Algorithm with a Learned Mixture of Parametric Densities , 1997 .

[41]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[42]  Gregory Connor,et al.  A Test for the Number of Factors in an Approximate Factor Model , 1993 .

[43]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[44]  Geoffrey E. Hinton,et al.  Varieties of Helmholtz Machine , 1996, Neural Networks.

[45]  Lei Xu Bayesian Ying-Yang System and Theory as a Unified Statistical Learning Approach: (V) Temporal Modeling for Temporal Perception and Control , 1998, ICONIP.

[46]  A. Mahajan,et al.  A Test of the Apt In Pricing Uk Stocks , 1987 .

[47]  W. Sharpe The Sharpe Ratio , 1994 .

[48]  Andrzej Cichocki,et al.  New learning algorithm for blind separation of sources , 1992 .

[49]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[50]  C. Spearman General intelligence Objectively Determined and Measured , 1904 .

[51]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[52]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[53]  Lizhong Wu,et al.  Optimization of trading systems and portfolios , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[54]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[55]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[56]  Phoebus J. Dhrymes,et al.  A Critical Reexamination of the Empirical Evidence on the Arbitrage Pricing Theory , 1984 .

[57]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[58]  Erkki Oja,et al.  Subspace methods of pattern recognition , 1983 .

[59]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[60]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[61]  Geoffrey E. Hinton,et al.  An Alternative Model for Mixtures of Experts , 1994, NIPS.

[62]  Lei Xu,et al.  Bayesian Ying-Yang machine, clustering and number of clusters , 1997, Pattern Recognit. Lett..

[63]  Michael I. Jordan,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.

[64]  Jerome T. Connor,et al.  A Constrained Neural Network Kalman Filter for Price Estimation in High Frequency Financial Data , 1997, Int. J. Neural Syst..

[65]  Tomaso A. Poggio,et al.  Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.

[66]  Christopher M. Bishop,et al.  Current address: Microsoft Research, , 2022 .

[67]  Harold Hotelling,et al.  Simplified calculation of principal components , 1936 .

[68]  Lei Xu,et al.  Further studies on temporal factor analysis: comparison and Kalman filter-based algorithm , 2003, Neurocomputing.

[69]  Lizhong Wu,et al.  What is the "true price"? state space models for high frequency FX data , 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

[70]  Christian Jutten,et al.  Nonlinear source separation: the post-nonlinear mixtures , 1997, ESANN.

[71]  Lei Xu,et al.  Temporal BYY learning for state space approach, hidden Markov model, and blind source separation , 2000, IEEE Trans. Signal Process..

[72]  Lei Xu Temporal BYY learning and its applications to extended Kalman filtering, hidden Markov model, and sensor-motor integration , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[73]  Piet de Jong,et al.  The likelihood for a state space model , 1988 .