Gazing into the Future: Using Ensemble Techniques to Forecast Company Fundamentals

Quantitative factor portfolios generally use historical company fundamental data in portfolio construction. What if we could forecast, with a small margin of error, the forward-looking company fundamentals? Using best practices from the science of forecasting and machine learning techniques, namely Random Forests and Gradient Boosting, I try to build a value composite model to sort portfolios based on forecasted fundamentals. I use the in-sample data to train the models to predict forward looking earnings, free cash flow, EBITDA, and Net Operating Profit After Taxes. The combined value portfolio out of sample did not produce statistically significant outperformance verses the equal weight portfolio (as a comparison to the long only value composite) or versus cash (for the long/short portfolio).