High-Dimensional Factor Models with an Application to Mutual Fund Characteristics

Martin Lettau

No 29833, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper considers extensions of two-dimensional factor models to higher-dimensional data 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 three-dimensional data set consisting of 25 characteristics of 1,342 mutual funds observed over 34 quarters. The tensor factor models reduce the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard two-dimensional models and show that the components of the model have clear economic interpretations.

JEL-codes: C38 G12 (search for similar items in EconPapers)
Date: 2022-03
New Economics Papers: this item is included in nep-ecm
Note: AP
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.nber.org/papers/w29833.pdf (application/pdf)

Related works:
Working Paper: High Dimensional Factor Models with an Application to Mutual Fund Characteristics (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:29833

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w29833

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().