{"id":7050,"date":"2023-12-05T19:45:31","date_gmt":"2023-12-05T19:45:31","guid":{"rendered":"https:\/\/aaep.org.ar\/?p=7050"},"modified":"2023-12-05T19:45:33","modified_gmt":"2023-12-05T19:45:33","slug":"algorithmic-examination-of-monetary-policy-deliberations-an-analysis-of-the-informational-content-of-fomc-meetings","status":"publish","type":"post","link":"https:\/\/aaep.org.ar\/?p=7050","title":{"rendered":"Algorithmic Examination of Monetary Policy Deliberations: an Analysis of the Informational Content of FOMC Meetings"},"content":{"rendered":"<iframe loading=\"lazy\" class=\"wonderplugin-pdf-iframe\" src=\"https:\/\/aaep.org.ar\/wp-content\/plugins\/wonderplugin-pdf-embed\/pdfjslight\/web\/viewer.html?v=2&file=https:\/\/aaep.org.ar\/works\/works2023\/2.pdf\" width=\"100%\" height=\"600px\" style=\"border:0;\"><\/iframe>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We apply natural language processing techniques to infer sentiment expressed in FOMC meetings. The sample period covers the Great Recession and its aftermath (2003-2012). We infer meetings\u2019 tone implementing large language models (BERT, NLI, ChatGPT) and traditional dictionary methods (Loughran &amp; MacDonald 2011, Arom\u00ed 2020). Suggesting policymakers are advantageously informed, we find that tone in FOMC meetings anticipates media sentiment,<br \/>\nconsumers\u2019 confidence, and financial market dynamics. Furthermore, meetings\u2019 tone also anticipates growth forecast errors from Fed staff and private sector analysts. The findings are robust to changes in text processing methodologies and show a persistent anticipatory ability that extends over multiple quarters. We observe that, despite some discrepancies and evidence of underreaction, the tone of FOMC is closely replicated in meetings\u2019 minutes. Our analysis shows that, despite its availability, analysts fail to incorporate the information on policymakers\u2019 deliberations in an adequate manner.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[29],"tags":[31],"class_list":["post-7050","post","type-post","status-publish","format-standard","hentry","category-anales","tag-aaep-anales-2023"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Algorithmic Examination of Monetary Policy Deliberations: an Analysis of the Informational Content of FOMC Meetings - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica<\/title>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Algorithmic Examination of Monetary Policy Deliberations: an Analysis of the Informational Content of FOMC Meetings - Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"og:description\" content=\"We apply natural language processing techniques to infer sentiment expressed in FOMC meetings. The sample period covers the Great Recession and its aftermath (2003-2012). We infer meetings\u2019 tone implementing large language models (BERT, NLI, ChatGPT) and traditional dictionary methods (Loughran &amp; MacDonald 2011, Arom\u00ed 2020). Suggesting policymakers are advantageously informed, we find that tone in FOMC meetings anticipates media sentiment, consumers\u2019 confidence, and financial market dynamics. Furthermore, meetings\u2019 tone also anticipates growth forecast errors from Fed staff and private sector analysts. The findings are robust to changes in text processing methodologies and show a persistent anticipatory ability that extends over multiple quarters. We observe that, despite some discrepancies and evidence of underreaction, the tone of FOMC is closely replicated in meetings\u2019 minutes. Our analysis shows that, despite its availability, analysts fail to incorporate the information on policymakers\u2019 deliberations in an adequate manner.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/aaep.org.ar\/?p=7050\" \/>\n<meta property=\"og:site_name\" content=\"Asociaci\u00f3n Argentina de Econom\u00eda Pol\u00edtica\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-05T19:45:31+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-05T19:45:33+00:00\" \/>\n<meta name=\"author\" content=\"aaepolitica\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@aaepoficial\" \/>\n<meta name=\"twitter:site\" content=\"@aaepoficial\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"aaepolitica\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=7050#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=7050\"},\"author\":{\"name\":\"aaepolitica\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/#\\\/schema\\\/person\\\/7150b747ba20875259c74b2cd177316c\"},\"headline\":\"Algorithmic Examination of Monetary Policy Deliberations: an Analysis of the Informational Content of FOMC Meetings\",\"datePublished\":\"2023-12-05T19:45:31+00:00\",\"dateModified\":\"2023-12-05T19:45:33+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=7050\"},\"wordCount\":30,\"publisher\":{\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/#organization\"},\"keywords\":[\"AAEP Anales 2023\"],\"articleSection\":[\"Anales\"],\"inLanguage\":\"es\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/aaep.org.ar\\\/?p=7050\",\"url\":\"https:\\\/\\\/aaep.org.ar\\\/?p=7050\",\"name\":\"Algorithmic Examination of Monetary Policy Deliberations: an Analysis of the Informational Content of FOMC Meetings - 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