## 4 Methodology  
This chapter details the innovative deep learning framework constructed to address the task of interest rate nowcasting from central bank policy communication texts. Given that central bank policy statements often feature ultra-long sequences of thousands or even tens of thousands of tokens, and that significant domain differences exist between different central banks (e.g., Federal Reserve--FOMC and European Central Bank--ECB) as well as across different economic cycles (e.g., pre/post quantitative easing, hiking cycles), our proposed method aims to efficiently process long sequences, achieve parameter-efficient fine-tuning, and possess cross-domain adaptation capabilities. Before detailing our methodology, we formally state the testable hypotheses that drive our research design and experimental validation:  
**Hypothesis 1 (Long-Sequence Processing). **_Complete central bank documents contain more predictive information for interest rate changes than truncated or summarized versions. We hypothesize that models capable of processing entire documents without truncation will achieve superior predictive performance compared to those limited by sequence length constraints. Figure 3: Distribution of interest rate change labels (7-day window, 5 bp threshold) in the FOMC dataset. Figure 2: Distribution of FOMC statement token lengths. **Hypothesis 2** (**Architecture Efficiency**). : _Hyena Hierarchy architectures with sub-quadratic complexity will demonstrate both higher computational efficiency and superior or comparable predictive performance compared to standard Transformer-based approaches when processing ultra-long financial documents._  
**Hypothesis 3** (**Parameter Efficiency**). : _Delta-LoRA fine-tuning will achieve performance levels comparable to full parameter fine-tuning while using less than 1% of the total parameters, demonstrating superior parameter efficiency for domain adaptation in financial text analysis._  
**Hypothesis 4** (**Domain Generalization**). : _Models trained with domain-adversarial training will exhibit significantly better cross-institutional generalization (e.g., FOMC to ECB) compared to models trained without domain adaptation mechanisms._  
**Hypothesis 5** (**Multi-Task Learning**). : _Incorporating auxiliary hawkish/dovish sentiment classification as a secondary task will improve primary interest rate prediction performance through shared representation learning and implicit regularization effects._  
This chapter first defines the core research problem in Section 4.1. Subsequently, Section 4.2 details the model's backbone network, Hyena Hierarchy, explaining its efficiency in handling ultra-long text sequences. Section 4.3 then elaborates on the parameter-efficient fine-tuning strategy, Delta-LoRA, illustrating how large pretrained models can be effectively adapted to downstream tasks with minimal parameters and explaining its improvements over standard LoRA. To address the domain shift issue in multi-source data, Section 4.4 introduces a multi-source domain adaptation mechanism based on domain-adversarial training of neural networks (DANN). Furthermore, to leverage implicit sentiment signals within the text, Section 4.5 presents a multi-task learning paradigm combining the main interest rate prediction task with an auxiliary hawkish/dovish classification task. Finally, Section 4.6 provides the complete model training procedure and inference stage implementation details. It is important to emphasize that these three core technologies (Hyena, Delta-LoRA, DANN) are not simply stacked but deeply integrated through a unified loss function and a backpropagation path.