Adapter‐Regularised Continual Learning for Dynamic Financial Sentiment Encoding in Multi‐Modal Market Fusion

ABSTRACT We propose an adapter‐regularised continual learning framework for dynamic financial sentiment encoding, addressing the dual challenge of retaining long‐term domain knowledge while adapting to transient market sentiment patterns. The proposed method augments a pre‐trained RoBERTa model with lightweight adapter modules and elastic knowledge consolidation, enabling parameter‐efficient updates without catastrophic forgetting. Each transformer layer incorporates parallel adapters with gating mechanisms, selectively blending original and adapted features to maintain stability during incremental training. Moreover, the framework dynamically distills emerging sentiment lexicon terms into the encoder through an auxiliary distillation loss, ensuring sensitivity to evolving financial language. The resulting sentiment embeddings are fused with market data via cross‐modal attention, where adapter gating outputs further modulate the contribution of textual features. Realised with a RoBERTa‐large backbone and online Fisher matrix approximation, our approach demonstrates compatibility with high‐frequency trading scenarios while outperforming static domain‐adapted models. The key innovation lies in the tight integration of adapter‐based continual learning with financial lexicon dynamics and modality‐specific gating, a combination not explored in prior work. Experimental validation across multi‐modal market datasets confirms the model's ability to simultaneously preserve financial domain knowledge and capture temporal sentiment shifts, outperforming both generic continual learning methods and specialised financial encoders. This advancement bridges the gap between stable representation learning and real‐time market adaptation, offering a principled solution for dynamic sentiment analysis in algorithmic trading systems.