High Dimensional Factor Models with an Application to Mutual Fund Characteristics

Martin Lettau

MPRA Paper from University Library of Munich, Germany

Abstract: This paper considers extensions of 2-dimensional factor models to higher-dimension data that can be represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the model using a 3-dimensional data set consisting of 25 characteristics of 1,342 mutual funds observed over 34 quarters. The tensor factor model reduces the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard 2-dimensional models and show that the components of the model have clear economic interpretations.

Keywords: Tucker decomposition; CP decomposition; tensors; PCA; SVD; factor models; mutual funds; characteristics (search for similar items in EconPapers)
JEL-codes: C38 G12 (search for similar items in EconPapers)
Date: 2021-03-02
New Economics Papers: this item is included in nep-cwa
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Related works:
Working Paper: High-Dimensional Factor Models with an Application to Mutual Fund Characteristics (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:112192

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