Label Interpretation

LABEL_2: NeutralLABEL_1: HawkishLABEL_0: Dovish

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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.

This data package includes two shapefiles and their associated attribute tables. The two files, GFB_producers_2021-02-18.zip and GFB_retailers_2021-02-18.zip, contain all internet-discoverable (at the time of data collection, July-August 2020; with minor edits/additions circa June 2022) grass-fed beef producers and retailers in the Southwest and Southern Plains of the U.S. (Arizona, California, Colorado, Kansas, Nevada, New Mexico, Oklahoma, Texas, Utah), compiled through an internet search. The data were initially collected in August of 2020 using publicly available information from Google search engine and Google map searches with the intention of informing members of the Sustainable Southwest Beef Project (USDA NIFA grant #2019-69012-29853) team about existing grass-fed beef producers and retailers in the study area.

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.

Effective Federal Funds Rate in the United States remained unchanged at 4.33 percent on Wednesday June 18. This dataset includes a chart with historical data for the United States Effective Federal Funds Rate.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.