## 3 Data and Preprocessing
### Data Sources and Alignment  
We constructed our dataset from multiple complementary sources to ensure comprehensive coverage of central bank communications and associated market reactions. The primary textual data comes from two key datasets that provide different, but complementary, perspectives on Federal Reserve communications.  
We utilized the \"FOMC Meeting Statements & Minutes\" dataset curated by [PERSON] on Kaggle [35], covering over 900 official FOMC statements and meeting minutes texts from 1977 to 2024. This dataset represents the most comprehensive publicly available collection of Federal Reserve communications, encompassing both formal policy statements released immediately after FOMC meetings and detailed meeting minutes that provide deeper insights into policymaker deliberations. The dataset includes critical metadata such as exact publication timestamps, document types, and meeting identifiers, enabling precise temporal alignment with market data. The documents vary significantly in length, with policy statements typically containing 1000-3000 tokens, while meeting minutes often exceed 8000 tokens, highlighting the need for our long-sequence processing capabilities. Each text entry includes accurate publication dates and authoring body information, greatly facilitating subsequent temporal alignment and multi-task annotation.  
To construct the primary nowcasting task labels, we simultaneously used the \"Federal Reserve Interest Rates, 1954-Present\" data published by the federalreserve team on Kaggle, which provides monthly records of the federal funds' target rate and effective rate. We further sourced daily data from the \"Effective Federal Funds Rate (FEDFUNDS)\" from the St. Louis Fed website [36] to achieve high-resolution measurement of interest rate change \(\Delta r\) within 7 days following the statement release date. The interest rate change labels were categorized as \"Up\" if \(\Delta r>5\) bp, \"Down\" if \(\Delta r<-5\) bp, and \"Stable\" otherwise. The selection of this 5 bp threshold aligns with industry practices for measuring macroeconomic policy volatility.  
To evaluate cross-institutional model generalization, we batch-downloaded speech texts from the European Central Bank (ECB) website, covering official speeches by the President and Executive Board members with complete metadata from 1999 to the 2025. We also incorporated the \"Euro Area Communication Event-Study Database (EA-CED)\" toolkit published by the Banque de France [37], which records immediate Euro Area market reactions triggered by 304 major policy announcements and over 4400 speeches, serving for subsequent inter-source performance comparison and domain adaptation validation.  
Additionally, we integrated the \"Trillion Dollar Words\" dataset open-sourced by the Georgia Tech Financial Services Innovation Lab [38]. This dataset provides granular paragraph-level sentiment annotations for FOMC communications, with expert-labeled classifications of policy language as \"hawkish\", \"dovish\", or \"neutral\". The dataset covers approximately 30,000 paragraphs spanning 1994-2023, offering rich sentiment supervision that complements our primary interest rate prediction task. The annotation methodology employed professional financial analysts who labeled content based on its implied policy stance regarding future monetary tightening or easing. This high-quality sentiment labeling enables our multi-task learning approach, where the auxiliary hawkish/dovish classification task provides additional supervisory signals to improve overall model performance. This dataset provides paragraph-level annotations of hawkish/dovish sentiment for FOMC texts, serving as auxiliary sentiment signal input for multi-task learning to enhance the model's ability to capture fine-grained policy tones.