ABSTRACT
It is common to estimate equity betas for private firms or non-traded assets through a comparable company analysis, and we test if the Random Forest algorithm can provide superior forecasts. In out-of-sample tests from 1992 to 2018, we find that Random Forest forecasts produce substantially lower average errors and mean absolute errors every year.
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Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Our results do not change if we allow bigger lags (up to 6 months) between market and accounting information or if we do not lag it at all.
2 We estimate the Random Forest with the ranger package in the R software. We tune three hyperparameters: the number of trees in the forest, the maximum number of predictors each tree can test and the depth or level of the tree.