Sentiment-Aware Distillation for Bitcoin Trend Forecasting Under Partial Observability

Abstract

Deep Learning (DL) models are increasingly used for financial forecasting problems, such as price or trend prediction of a financial asset. However, most methods either rely solely on price information or require difficult to implement data harvesting pipelines, e.g., from social media, to deploy them. The main contribution of this paper is a method that exploits sentiment information as a source of additional supervision during the training process, allowing for improving the profitability of the developed strategies compared to baseline agents, while also allowing for operating the agent under partial observability, i.e., without requiring sentiment information as input during inference. As demonstrated in the conducted experiments on the Bitcoin-USD currency pair, this approach can indeed lead to significant improvements in the performance of DL agents, as well as help reduce the overfitting phenomena that often occur when training such agents.