Continual Learning Augmented Investment Decisions
This work addresses the challenge of making more accurate and explainable investment decisions for financial analysts, though it appears incremental as it builds on existing neural network methods with specific enhancements.
The authors tackled the problem of improving long-term financial investment decisions by introducing Continual Learning Augmentation (CLA), which uses an explicit memory structure and novel cues based on change points in error series, resulting in significant outperformance over base models in an international equity simulation from 2003-2017.
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.