This dataset contains the text from Federal Reserve FOMC (Federal Open Market Committee) meeting minutes and statements, collected by scraping the Federal Reserve's website. The data spans a specific period of time, providing insights into the central bank's monetary policy decisions and discussions.

Content

The dataset consists of the following columns:

• Date: The date of the FOMC meeting or statement release in the format YYYYMMDD.
• Type: Indicator for the type of document. 0 represents a statement, while 1 represents meeting minutes.
• Text: The text content of each paragraph in the meeting minutes or statements.

Acknowledgements

The data is collected from the official Federal Reserve website (https://www.federalreserve.gov) using a custom Python scraper built with BeautifulSoup.

Inspiration

This dataset can be used for various purposes, such as:

1. Analyzing the sentiment and tone of FOMC meeting minutes and statements over time.
2. Identifying key phrases and words that indicate changes in monetary policy.
3. Developing natural language processing models to predict future policy decisions based on historical data.
4. Investigating the relationship between FOMC meeting minutes/statements and financial market reactions.

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Label Interpretation

LABEL_2: NeutralLABEL_1: HawkishLABEL_0: Dovish

  Citation and Contact Information





  Cite

Please cite our paper if you use any code, data, or models. @inproceedings{shah-etal-2023-trillion, title = "Trillion Dollar Words: A New Financial Dataset, Task {&} Market Analysis", author = "Shah, Agam and Paturi, Suvan and Chava, Sudheer", booktitle = "Proceedings of the 61st Annual Meeting of the Association for… See the full description on the dataset page: https://huggingface.co/datasets/gtfintechlab/fomc_communication.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Future Computing (FC) Community of Interest (CoI) meeting on August 5–6, 2019, explored the evolving computing landscape to inform agencies about potential opportunities as well as gaps in the Nation's future computing objectives. The meeting focused on where computing will be in the next decade and beyond while also looking at emerging and future applications. It considered the need for new software concepts and approaches to effectively capitalize on new hardware architectures and paradigms. The long period of sustained growth in computing power over the last five decades, characterized by Moore's Law and Dennard Scaling, is expected to end over the next decade. The continued improvement in computing performance will now require moving to new modalities and new means of cooperation and partnership for the benefit of the Nation. The FC-CoI meeting was held at the offices of the Federal Networking and Information Technology Research and Development Program in Washington, D.C. The meeting brought together key members of industry, academia, and the Federal Government over a two-day period to discuss the future of computing. The meeting had been advertised in the Federal Register to encourage broad participation from the advanced computing community. More detail about the meeting is available at https://www.nitrd.gov/nitrdgroups/index.php?title=FC-COI-2019.

The benchmark interest rate in Indonesia was last recorded at 5 percent. This dataset provides - Indonesia Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The purpose of these files is to extend the Romer-Romer (2004) monetary policy shock series. Program and data are provided without any warranty. Please email jfwieland@ucsd.edu if you find any discrepancies.When using this data in your work, cite Wieland-Yang (2020) along with Romer-Romer (2004) as a reference.Thanks to:1. Yeji Sung for noting a missing match in an earlier version of the code and for alerting me to Philadelphia Fed’s dataset, which includes data for August-October 1972 that was missing in the original Romer-Romer dataset. The values I use differ slightly from the Philadelphia Fed’s. This is because I do not compound annualized growth rates in order to be consistent with the original Greenbook forecasts. 2. Pavel Kapinos for noting that the old target variable was shifted by one meeting from 2004-7.3. Michael McMahon for noting that the May 15th 2001 meeting was incorrectly coded as May 18.Contents:1. RR_monetary_shock_update.doThis code file generates three Stata datasets, RR_monetary_shock_monthly.dta, RR_monetary_shock_quarterly.dta, and RR_monetary_shock_annual.dta. These correspond to the monetary shock series at monthly, quarterly, and annual frequency. Each Stata file contains four variables. The date variable "date", “resid” are the original Romer-Romer (2004) shocks, "resid_romer" are the monetary policy shocks based on the original Romer-Romer (2004) regression, and "resid_full" are the monetary policy shocks based on running the Romer-Romer (2004) regression on the full 1969-2007 sample.2. RRimport.xlsThis is the original Romer-Romer (2004) dataset of Greenbook forecasts updated to 2007. Also includes recently-published data for August-October 1972 that was missing in the original Romer-Romer dataset (these are marked in yellow).3. RRshock_Quarterly_1.txt and RRshock_Quarterly_2.txtThese are Greenbook forecasts downloaded from the FRED database. They are used to check the entries in RRimport.xls.4. ForecastRelease.xlsxContains forecast release dates for meetings where FRED does not have the data.5. RRshock_xls folderThese are the digitized Greenbook forecast from the Philadelphia Fed website. These are used to cross-check the data from FRED and the entries in RRimport.xls.

This dataset contains cross-sections of the last observed option quote for each strike of 17 underlyings 30 minutes before and after the Federal Open Market Committee (FOMC) announcement at 13:00 Chicago time (CT) on 18 March 2015. It is extracted from the confidential bulk CBOE OPRA data provided by the Options Price Reporting Authority (OPRA) and is employed to estimate the high-frequency risk-neutral density (RND) of the selected underlyings and examine the intraday changes in these RNDs following the FOMC announcement. This dataset underlies the empirical application on RND extraction of Andersen et al. (Journal of Financial Econometrics, 19(1), 128-177, 2021).Buy and sell orders are aggregated at financial markets into limit order books (LOBs). Each asset has its own LOB. Our research will be the first project to combine the information in a stock's LOB with matching information in the LOBs for derivative option contracts. These derivative prices depend on the stock price, their variability through time (called volatility) and other contract inputs known to all traders. We will use empirical and mathematical methods to investigate the vast amount of information provided by integrated stock and derivative LOBs. This information will be processed to measure and predict risks associated with volatility, liquidity and price jumps. The results are expected to be of interest to market participants, regulators, financial exchanges, financial institutions employing research teams and data vendors. We will investigate how posted limit orders, i.e. offers to buy or to sell, contribute to volatility and how they can be used to measure current and future levels of volatility. Derivative prices explicitly provide volatility expectations (called implied volatility) and we will compare these with estimates obtained directly from changes in stock prices. We will discover how information is transmitted from option LOBs to stock LOBs (and vice versa) and thus identify the most up-to-date source of volatility expectations. Previous research has used transaction prices and the best buying and selling prices; we will innovate by using complete LOBs providing significantly more information. The liquidity of markets depends on supply and demand, which are revealed by LOBs. Each stock has many derivative contracts, some of which have relatively low liquidity. We will provide new insights into the microstructure of option markets by evaluating liquidity related to contract terms such as exercise prices and expiry dates. This will allow us to find robust ways to combine implied volatilities into representative volatility indices. We will identify those time periods when price jumps occur, these being periods when changes in prices are very large compared with normal time periods. We will then test methods for using stock and derivative LOBs to predict the occurrence of jumps. We will also model the dynamic interactions between different order types during a jump period. The success of our research depends on access to price information recorded very frequently. We will use databases which record all additions to and deletions from LOBs, matched with very precise timestamps. For stocks, we will use the LOBSTER database which constructs LOBs from NASDAQ prices. For derivatives, we will use the Options Price Reporting Authority (OPRA) database. Our research is the first to combine and investigate the information in these separate sources of LOBs. Data was purchased from CBOE Datashop (https://datashop.cboe.com/) and then was extracted and analyzed to answer different research questions.

This data illustrates Federal progress in meeting the requirements outlined in Section 432 of the Energy Independence and Security Act of 2007 (EISA 432) (42 U.S.C. 8253(f)). The data is accessible through the FEMP EISA 432 Compliance Tracking System, which offers: (1) Top-tier agency aggregates, representing all reported data subject to the EISA 432 requirements; (2) Facility-level detailed data that excludes information for facilities that have requested exemption from public disclosure for national-security purposes.

ECB and FED Speeches

This data contains speeches from European Central Bank (ECB) and Federal Reserve (FED) executives, from 1996 to 2025.

  Mistral OCR

In addition to the text provided by the Bank of International Settlements (BIS), we also added a new textual column derived extracting information from the source PDF files using Mistral's OCR API. Page breaks are identified with the

---[PAGE_BREAK]---

string.

The benchmark interest rate in Mexico was last recorded at 7.75 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Russia was last recorded at 18 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Turkey was last recorded at 40.50 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Korea was last recorded at 2.50 percent. This dataset provides - South Korea Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.