Discussion paper

DP17091 High Dimensional Factor Models with an Application to Mutual Fund Characteristics

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 models reduce the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard 2-dimensional model and show that the components of the model have clear economic interpretations.

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Citation

Lettau, M (2022), ‘DP17091 High Dimensional Factor Models with an Application to Mutual Fund Characteristics‘, CEPR Discussion Paper No. 17091. CEPR Press, Paris & London. https://cepr.org/publications/dp17091