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A First Application of Independent Component Analysis to Extracting Structure from Stock Returns

Andrew D. Back, Andreas S. Weigend
1997 International Journal of Neural Systems  
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

Rogelio Ladrón de Guevara Cortés, Salvador Torra Porras, Enric Monte Moreno
2018 Journal of Computacion y Sistemas  
-, 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

Rogelio Ladrón de Guevara Cortés, Salvador Torra Porras, Enric Monte Moreno
2019 Journal of Computacion y Sistemas  
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

Andrzej Cichocki, Stanley R Stansell, Zbigniew Leonowicz, James Buck
2005 Journal of Asset Management  
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

Rogelio, Salvador Torra Porras, Enric Monte Moreno
2021 Revista Finanzas y Política Económica  
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

Rogelio Ladrón de Guevara Cortés, Salvador Torra Porras, Enric Monte Moreno
2021 Revista Mexicana de Economía y Finanzas  
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

Patrick Kouontchou, Bertrand Maillet
2007 The European Symposium on Artificial Neural Networks  
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]

Siu-Ming Cha, Lai-Wan Chan
2000 Lecture Notes in Computer Science  
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

Zhiliang Wang, Yalin Sun, Peng Li
2014 Discrete Dynamics in Nature and Society  
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

Rogelio Ladrón de Guevara Cortés, Salvador Torra Porras
2014 Contaduría y Administración  
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

Dilip B. Madan
2006 Quantitative finance (Print)  
-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

Wataru Souma
2021 Frontiers in Physics  
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

Pooya Sabetfar
2012 African Journal of Business Management  
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

Yu Ling Zhao
2013 Advanced Materials Research  
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/ fatcat:ftiar7jzhjhavnegmcuadin5we

Homogeneity and Sub-homogeneity Pursuit: Iterative Complement Clustering PCA [article]

Daning Bi, Le Chang, Yanrong Yang
2022 arXiv   pre-print
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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