Estadísticas › Backtesting y evidencia › Machine learning
The best machine learning forecast explains 0.40% of monthly return variance
Monthly out of sample R squared for individual stock return forecasts, all stocks, out of sample window 1987 to 2016 with annual refitting. The benchmark is a forecast of zero rather than the historical mean, which mechanically raises every figure. Unregularised least squares on all 920 predictors is omitted here and scores minus 3.46%.
Neural network forecasts produced a monthly out of sample R squared of 0.40% and a long short Sharpe ratio of 1.35 value weighted and 2.45 equal weighted. Neither of the two most cited studies reports any transaction cost adjustment at all.
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