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Stats › Backtesting en bewijs › Machine learning

The best machine learning forecast explains 0.40% of monthly return variance

Elastic net
0.11%
Least squares, 3 predictors
0.16%
Generalised linear model
0.19%
Principal components
0.26%
Partial least squares
0.27%
Random forest
0.33%
Boosted trees
0.34%
Neural network, three layers
0.40%

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.

0.40%Best monthly out of sample R squared across all methods
0.16%The same for a three predictor linear model
-3.46%The same for unregularised least squares on 920 predictors
GeenPublished cost adjusted Sharpe ratios located

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Below this point there are 6 sections and 5 named sources, roughly 900 words of it. Every figure carries the source it came from and the date the data is from.

Je kunt elke statistiek in de bibliotheek gratis blijven bekijken. De intro en de samenvatting zijn altijd open.