NFA FOR FACTOR NUMBER DETERMINATION IN APT

Kai-Chun Chiu () and Lei Xu ()
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Kai-Chun Chiu: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, P.R. China
Lei Xu: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, P.R. China

International Journal of Theoretical and Applied Finance (IJTAF), 2004, vol. 07, issue 03, 253-267

Abstract: In the context of quantitative analysis of the arbitrage pricing theory (APT) model, conventional factor analytic approaches such as maximum likelihood factor analysis (MLFA) cannot provide satisfactory answers to two important questions. The first one concerns the correct identification of factor number while the second one is related to the rotation indeterminacy of factor loadings. In the literature, MLFA followed by likelihood ratio (LR) test and the analysis of eigenvalues of sample covariance matrix were two popular approaches used to determine the appropriate number of factors. However, it was shown empirically that both of them suffered from different kinds of biases. We find the recently developed non-gaussian factor analysis (NFA) model by Xu [24] provides a new perspective for the determination of the appropriate factor numberkin APT, with promising empirical results demonstrated.

Keywords: Factor analysis; arbitrage pricing theory; BYY harmony learning; model selection; statistical tests (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1142/S021902490400244X

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