Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications
Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of t...
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MDPI AG
2025-06-01
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| author | Tao Song Shijie Yuan Rui Zhong |
| author_facet | Tao Song Shijie Yuan Rui Zhong |
| author_sort | Tao Song |
| collection | DOAJ |
| description | Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2a54d90c15ef431bb4c8ec5b2fd96ea42025-08-20T02:24:25ZengMDPI AGApplied Sciences2076-34172025-06-011512642010.3390/app15126420Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank CommunicationsTao Song0Shijie Yuan1Rui Zhong2Institute of Applied Economics, Shanghai Academy of Social Sciences, Shanghai 200020, ChinaSchool of Finance, Harbin University of Commerce, Harbin 150028, ChinaInformation Initiative Center, Hokkaido University, Sapporo 060-0808, JapanEffective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting.https://www.mdpi.com/2076-3417/15/12/6420interest rate nowcastingcentral bank communicationHyena HierarchyDelta LoRAlong sequence processingdomain adaptation |
| spellingShingle | Tao Song Shijie Yuan Rui Zhong Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications Applied Sciences interest rate nowcasting central bank communication Hyena Hierarchy Delta LoRA long sequence processing domain adaptation |
| title | Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications |
| title_full | Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications |
| title_fullStr | Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications |
| title_full_unstemmed | Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications |
| title_short | Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications |
| title_sort | advanced hyena hierarchy architectures for predictive modeling of interest rate dynamics from central bank communications |
| topic | interest rate nowcasting central bank communication Hyena Hierarchy Delta LoRA long sequence processing domain adaptation |
| url | https://www.mdpi.com/2076-3417/15/12/6420 |
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