Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model

1. 1Integrate aggregate analyst consensus into a deep learning model as a structural bottleneck
2. 2Use the bottleneck constraint as an endogenous regularizer to improve out-of-sample predictions
3. 3Evaluate the model's performance using relevant metrics, such as mean absolute error or R-squared
4. 4Refine the model by incorporating additional features or tweaking hyperparameters

This research benefits data scientists and quantitative analysts on a team, as it provides a novel approach to predicting stock returns using deep learning and analyst consensus, enabling them to make more informed investment decisions

💡 Incorporating analyst consensus as a bottleneck in a deep learning model can improve interpretability and out-of-sample predictions

💡 New deep learning model uses analyst consensus to predict stock returns!