  Investment decisions can benefit from incorporating an accumulated knowledge
of the past to drive future decision making. We introduce Continual Learning
Augmentation (CLA) which is based on an explicit memory structure and a feed
forward neural network (FFNN) base model and used to drive long term financial
investment decisions. We demonstrate that our approach improves accuracy in
investment decision making while memory is addressed in an explainable way. Our
approach introduces novel remember cues, consisting of empirically learned
change points in the absolute error series of the FFNN. Memory recall is also
novel, with contextual similarity assessed over time by sampling distances
using dynamic time warping (DTW). We demonstrate the benefits of our approach
by using it in an expected return forecasting task to drive investment
decisions. In an investment simulation in a broad international equity universe
between 2003-2017, our approach significantly outperforms FFNN base models. We
also illustrate how CLA's memory addressing works in practice, using a worked
example to demonstrate the explainability of our approach.
