Analyzing FOMC Minutes: Accuracy and Constraints of Language Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866912393371582464 |
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| author | Kim, Wonseong Spörer, Jan Frederic Handschuh, Siegfried |
| author_facet | Kim, Wonseong Spörer, Jan Frederic Handschuh, Siegfried |
| contents | This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting. The study reveals that the FOMC is careful to avoid expressing emotion in their sentences and follows a set of templates to cover economic situations. The analysis employs advanced language modeling techniques such as VADER and FinBERT, and a trial test with GPT-4. The results show that FinBERT outperforms other techniques in predicting negative sentiment accurately. However, the study also highlights the challenges and limitations of using current NLP techniques to analyze FOMC texts and suggests the potential for enhancing language models and exploring alternative approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_10164 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Analyzing FOMC Minutes: Accuracy and Constraints of Language Models Kim, Wonseong Spörer, Jan Frederic Handschuh, Siegfried Computation and Language This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting. The study reveals that the FOMC is careful to avoid expressing emotion in their sentences and follows a set of templates to cover economic situations. The analysis employs advanced language modeling techniques such as VADER and FinBERT, and a trial test with GPT-4. The results show that FinBERT outperforms other techniques in predicting negative sentiment accurately. However, the study also highlights the challenges and limitations of using current NLP techniques to analyze FOMC texts and suggests the potential for enhancing language models and exploring alternative approaches. |
| title | Analyzing FOMC Minutes: Accuracy and Constraints of Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2304.10164 |