Forecasting retained earnings of privately held companies with PCA and L1 regression

Authors

  • Harish S. Bhat,

    Corresponding author
    1. School of Natural Sciences, University of California, Merced, CA
    • Correspondence to: Harish S. Bhat, School of Natural Sciences, University of California, Merced, 5200 N. Lake Rd., Merced, CA 95343.

      E-mail: hbhat@ucmerced.edu

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    • The first author gratefully acknowledges Dave Krimm and Tony Yeh of SVB Analytics for supporting this collaboration, both by providing access to data and through discussions of this and future work.

  • Dan Zaelit

    1. SVB Analytics, San Francisco, CA
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Abstract

We use proprietary data collected by SVB Analytics, an affiliate of Silicon Valley Bank, to forecast the retained earnings of privately held companies. Combining methods of principal component analysis (PCA) and L1/quantile regression, we build multivariate linear models that feature excellent in-sample fit and strong out-of-sample predictive accuracy. The combined PCA and L1 technique effectively deals with multicollinearity and non-normality of the data, and also performs favorably when compared against a variety of other models. Additionally, we propose a variable ranking procedure that explains which variables from the current quarter are most predictive of the next quarter's retained earnings. We fit models to the top five variables identified by the ranking procedure and thereby, discover interpretable models with excellent out-of-sample performance. Copyright © 2013 John Wiley & Sons, Ltd.

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