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.
Forecasting retained earnings of privately held companies with PCA and L1 regression
Article first published online: 2 APR 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Applied Stochastic Models in Business and Industry
Volume 30, Issue 3, pages 271–293, May/June 2014
How to Cite
2014), Forecasting retained earnings of privately held companies with PCA and L1 regression, Applied Stochastic Models in Business and Industry, 30, pages 271–293. DOI: 10.1002/asmb.1972and (
- Issue published online: 13 JUN 2014
- Article first published online: 2 APR 2013
- Manuscript Accepted: 7 FEB 2013
- Manuscript Revised: 15 OCT 2012
- Manuscript Received: 22 APR 2012
- L1 regression;
- principal component analysis;
- private companies;
- quantile regression;
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.