Optimizing an NN for a long-short strategy

In practice, we need to explore variations for the design options for the NN architecture and how we train it from those we outlined previously because we can never be sure from the outset which configuration best suits the data. In this section, we will explore various architectures for a simple feedforward NN to predict daily stock returns using the dataset developed in Chapter 12 (see the notebook preparing_the_model_data in the GitHub directory for that chapter).

To this end, we will define a function that returns a TensorFlow model based on several architectural input parameters and cross-validate alternative designs using the MultipleTimeSeriesCV we introduced in Chapter 7, Linear Models – From Risk Factors to Return Forecasts. To assess the signal quality of the model predictions, we build a simple ranking-based long-short strategy based on an ensemble of the models that perform best during the in-sample cross-validation...