The effects of the content of FOMC communications on US treasury rates

Christopher Rohlfs, Sunandan Chakraborty, Lakshminarayanan Subramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study measures the effects of Federal Open Market Committee text content on the direction of short- and medium-term interest rate movements. Because the words relevant to short- and medium-term interest rates differ, we apply a supervised approach to learn distinct sets of topics for each dependent variable being examined. We generate predictions with and without controlling for factors relevant to interest rate movements, and our prediction results average across multiple training-test splits. Using data from 1999-2016, we achieve 93% and 64% accuracy in predicting Target and Effective Federal Funds Rate movements and 38%-40% accuracy in predicting longer term Treasury Rate movements. We obtain lower but comparable accuracies after controlling for other macroeconomic and market factors.

Original languageEnglish (US)
Title of host publicationEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages2096-2102
Number of pages7
ISBN (Electronic)9781945626258
StatePublished - Jan 1 2016
Event2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States
Duration: Nov 1 2016Nov 5 2016

Publication series

NameEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
CountryUnited States
CityAustin
Period11/1/1611/5/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

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    Rohlfs, C., Chakraborty, S., & Subramanian, L. (2016). The effects of the content of FOMC communications on US treasury rates. In EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 2096-2102). (EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings). Association for Computational Linguistics (ACL).