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A First Application of Independent Component Analysis to Extracting Structure from Stock Returns
1997
International Journal of Neural Systems
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This paper discusses the application of a modern signal processing technique known as independent component analysis ICA or blind source separation to multivariate nancial time series such a s a portfolio ...
We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. ...
Andrew Back acknowledges support of the Frontier Research Program, RIKEN and would like to thank Seungjin Choi and Zhang Liqing for helpful discussions. ...
doi:10.1142/s0129065797000458
fatcat:ljd6fnwvevaf3fe5dbllxq43i4
Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange
2018
Journal of Computacion y Sistemas
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-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. ...
Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis ...
Acknowledgments The authors thank Aapo Hyvärinen from the University of Helsinki for the technical advice on some topics related to this investigation, and Cristina Urbano at Gaesco for the financial data ...
doi:10.13053/cys-22-4-3083
fatcat:onmtgxb3drbzzhieymwe4cxfle
Neural Networks Principal Component Analysis for estimating the generative multifactor model of returns under a statistical approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange
2019
Journal of Computacion y Sistemas
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analysis, where the principal components are generalized from straight lines to curves. ...
The NLPCA belongs to the family of nonlinear versions of dimension reduction or the extraction techniques of underlying features, including nonlinear factor analysis and nonlinear independent component ...
Acknowledgments The authors thank Matthias Scholz from the Fundazione Edmund Mach for the technical advice on some topics related to this investigation, and Cristina Urbano at Gaesco for the financial ...
doi:10.13053/cys-23-2-3193
fatcat:yit32hili5do5kvri5nbk4vali
Independent variable selection: Application of independent component analysis to forecasting a stock index
2005
Journal of Asset Management
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Black and Weigand (1997) use IC analysis to extract estimates of the structure from a set of common stock returns. ...
We propose to use a technique called Independent Component Analysis (ICA) to extract the independent components (ICs) from monthly time series on a wide range of economic variables. ...
In our research we use a technique called Independent Component Analysis (ICA) to extract the independent components (ICs) from a set of monthly time series on a wide range of economic variables. ...
doi:10.1057/palgrave.jam.2240179
fatcat:ngt74s7zovecpgi2gnmky2h23e
Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange
2021
Revista Finanzas y Política Económica
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Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage ...
First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method ...
ACKNOWLEDGEMENTS The authors acknowledge Cristina Ubago at Gaesco, Spain, for the financial data provided as well as to the referees of this paper for their valuable comments. ...
doi:10.14718/revfinanzpolitecon.v13.n2.2021.9
fatcat:vm3amov7rnfq7kg567jjobipae
Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities
2021
Revista Mexicana de Economía y Finanzas
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The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component ...
The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. ...
In a second study, Ladrón de Guevara, Torra & Monte (2018) tried to make apparent a more realistic latent systematic risk factor structure utilizing the Independent Component Analysis 3 , to find out ...
doi:10.21919/remef.v16i0.697
fatcat:kwbcp35l2reqbdyakqcpsg7jpe
ICA-based High Frequency VaR for Risk Management
2007
The European Symposium on Artificial Neural Networks
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Independent Component Analysis (ICA, see Comon, 1994 and Hyvärinen et al., 2001) is more appropriate when non-linearity and non-normality are at stake, as mentioned by Back and Weigend (1997) in a financial ...
Various methods for specifying stress scenarii are discussed, compared to other published ones and classical tests of rejection are presented (Christoffersen and Pelletier, 2003) . ...
Definition Independent Component Analysis (ICA, Comon, 1994; Hyvärinen et al., 2001 ) is a well-known method of finding latent structure in data. ...
dblp:conf/esann/KouontchouM07
fatcat:f4bfhwhlkvgc3e6lqa5xd6hn7y
Applying Independent Component Analysis to Factor Model in Finance
[chapter]
2000
Lecture Notes in Computer Science
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In this paper, we show the relation between factor model and blind source separation, and we propose to use Independent Component Analysis (ICA) as a data mining tool to construct the underlying factors ...
Factor model is a very useful and popular model in finance. ...
Acknowledgement The authors would like to thank The Research Grants Council, HK for support. ...
doi:10.1007/3-540-44491-2_78
fatcat:gxefgu6lxjgoxjwocxckdm5ame
Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index
2014
Discrete Dynamics in Nature and Society
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The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). ...
Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. ...
Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. ...
doi:10.1155/2014/365204
fatcat:hodwo25bwbd3dlvs2hbsjmppl4
Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis
2014
Contaduría y Administración
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We present an improved methodology to estimate the underlying structure of systematic risk in the Mexican Stock Exchange with the use of Principal Component Analysis and Factor Analysis. ...
First, we extract the underlying systematic risk factors by way of both, the standard linear version of the Principal Component Analysis and the Maximum Likelihood Factor Analysis estimation. ...
Consequently, the data from 2000 to 2006 were used to extract the generative underlying structure of returns, which explains the behavior of the returns of the training period. ...
doi:10.1016/s0186-1042(14)71270-7
fatcat:4wjwug6hgrf57n4ijsslzgwk2q
Equilibrium asset pricing: with non-Gaussian factors and exponential utilities
2006
Quantitative finance (Print)
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-Independent Components Analysis (ICA) to identify factor structure. ...
finds significant kurtosis both statistically and risk neutrally in index returns. • Independent Components Analysis seeks to recover signal components of data by performing a PCA (principal components ...
doi:10.1080/14697680600804437
fatcat:mopvxpywtfbc5brrbf2t5tllby
Characteristics of Principal Components in Stock Price Correlation
2021
Frontiers in Physics
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of assets returns. 2) Null-model randomness is implemented via rotational random shuffling. 3) Principal component analysis and Helmholtz-Hodge decomposition are used to extract leading and lagging relationships ...
The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix ...
Principal component analysis (PCA), independent component analysis, machine learning, and other techniques have been applied to extract the meaningful components of various datasets. ...
doi:10.3389/fphy.2021.602944
fatcat:ruykgwxtifcrxpb4tybpxfzb4m
Common risk factors in the returns of non oil based stocks in Tehran Stock Exchange: The case of close economy
2012
African Journal of Business Management
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Overall, the findings document is a weak applicability of APT in this market. ...
The evidences point to at least one factor but probably about two factors explained the cross-section of expected returns on Tehran Stock Exchange (TSE). ...
principal component of macroeconomic variables that extracted from factor structure of Iranian economy. ...
doi:10.5897/ajbm11.1922
fatcat:suuymymvw5e6bl4bgzjlvqc4gu
Multiscale Event Study of Private Placement Announcement Effect
2013
Advanced Materials Research
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The case study results show that this approach is a promising method from the multi-scale point of view to analyze the impact of Announcement Date Effect in stock market. ...
., appears to be a promising data analysis method for nonlinear and non-stationary time series. ...
Variances of each IMF's percent of the stock series are used to explain the contribution of each IMF observation, because the IMF components are mutually independent. ...
doi:10.4028/www.scientific.net/amr.756-759.3011
fatcat:ftiar7jzhjhavnegmcuadin5we
Homogeneity and Sub-homogeneity Pursuit: Iterative Complement Clustering PCA
[article]
2022
arXiv
pre-print
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The simulation study and real analysis of the stock return data confirm the superior performance of our proposed methods. ...
However, in the presence of a group structure of the data, PCA often fails to identify the group-specific pattern, which is known as sub-homogeneity in this study. ...
The applications of CPCA are not limited to producing principal components in PCR and revealing the industry structure from the stock return data. ...
arXiv:2203.06573v1
fatcat:jptpyxqpnvcntci6gcn5svkdr4
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