Continual Learning Augmented Investment Decisions

Understand

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.