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Estimating nonlinear effects of management styles in the US equity market using a classifier neural network

The authors use a classifier neural network to predict stock factor returns. Training files with four months of fundamental factor data classify stocks as "buy" "hold" or "sell" based on the next month's return. Out-of-sample testing indicates that this technique is effective. The classifier's probability predictions for each state are used to estimate expected return and variance for the following month.