Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative in the Mexican stock exchange

Autor:	Enrique Monte, Rogelio Ladron de Guevara Cortes, Salvador Torra
Rok vydání:	2021
Předmět:	Principal Component Analysis Economics and Econometrics Economics as a science Mexican Stock Exchange HG1-9999 Independent Component Analysis Factor Analysis HB71-74 Finance Neural Networks Principal Component Analysis
Zdroj:	Revista Finanzas y Política Económica, Vol 13, Iss 2 (2021)
ISSN:	2011-7663 2248-6046
DOI:	10.14718/revfinanzpolitecon.3740
Popis:	This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component 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 Pricing Theory. We carry out our research according to two different perspectives. 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. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f6b8334ca9965ff88924f24a5dda8c5 https://doi.org/10.14718/revfinanzpolitecon.3740 Zobrazit plný text záznamu