Traffic analytics, rankings, and competitive metrics for bloomberg.com as of September 2025

Title

EntSUM: A Data Set for Entity-Centric Extractive Summarization

  Author list

Mounica Maddela*, Mayank Kulkarni*, Daniel Preotiuc-Pietro

  Description

Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM… See the full description on the dataset page: https://huggingface.co/datasets/bloomberg/entsum.

Bloomberg Billionaires Index

As of October 31, 2021

The Bloomberg Billionaires Index is a daily ranking of the world’s richest people. Details about the calculations are provided in the net worth analysis on each billionaire’s profile page. The figures are updated at the close of every trading day in New York.

Acknowledgements

https://www.bloomberg.com/

Federal Reserve data on emergency lending to banks covering the period August 2007 to April 2010 released in batches in Dec 2010, March 2011 and July 2011 as a result of the Dodd-Frank Act and FOIA requests by Bloomberg news and others.

From the Bloomberg page about the data (Aug 2011):

The data were extracted from 29,000 pages of documents and 18 Fed-prepared Microsoft Excel spreadsheets listing more than 21,000 transactions. The records were made public in batches on Dec. 1, 2010, and March 31 and July 6 of this year. The Fed released some of them under the 2010 Dodd-Frank Act and the rest in responses to Freedom of Information Act requests by media outlets including Bloomberg News and related federal court orders. The data covered money borrowed from the central bank from August 2007 through April 2010.

• Bloomberg News visualization using consolidated data (NB: as far as one can tell Bloomberg have not released their consolidated database. Details of data processing).

Data Source

From Bloomberg Story:

The Federal Reserve released thousands of pages of secret loan documents under court order, almost three years after Bloomberg LP first requested details of the central bank’s unprecedented support to banks during the financial crisis.

The records reveal for the first time the names of financial institutions that borrowed directly from the central bank through the so-called discount window. The Fed provided the documents after the U.S. Supreme Court this month rejected a banking industry group’s attempt to shield them from public view.

...

The central bank has never revealed identities of borrowers since the discount window began lending in 1914. The Dodd-Frank law exempted the facility last year when it required the Fed to release details of emergency programs that extended $3.3 trillion to financial institutions to stem the credit crisis. While Congress mandated disclosure of discount-window loans made after July 21, 2010 with a two-year delay, the records released today represent the only public source of details on discount- window lending during the crisis.

Openness

License: presuming public domain as data released from a federal agency.

This dataset contains the predicted prices of the asset Bloomberg Galaxy Crypto Index over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Parrillo’s Article “Administrative Law as a Choice of Business Strategy” documents variation across industries in how frequently companies and their trade associations sue their federal health-and-safety regulators. This dataset page contains the Article’s Methodological Appendix (in PDF), which explains how the author and research team searched for relevant lawsuits using the Bloomberg Law dockets database and how they identified industry challengers, agency actions under challenge, and challenger companies’ parent companies—as well as how the author conducted interviews. This dataset page also contains Excel files with the data on which the Article relies. Most of the Excel files consist of the results of Bloomberg Law dockets database searches for lawsuits, plus information about individual lawsuits and challengers gathered by the author and research team; each of these files includes a tab titled “Lawsuits” that includes a row for each lawsuit, plus a tab titled “Sources and Ordering” that explains how the lawsuit results were obtained from Bloomberg and ordered. The remaining Excel files consist of other relevant data on which the Article relies, especially information about companies or agency operations in certain of the areas studied. Citations in the Article are to the Dataset by File number and then (often) by Row number; each Excel file’s filename begins with the File number referenced in the Article.

CNXT / CHaTx likes x # Adapter Transformers OpenAssistant/oasst1 fka/awesome-chatgpt-prompts togethercomputer/RedPajama-Data-1T anon8231489123/ShareGPT_Vicuna_unfiltered gsdf/EasyNegative bloomberg/entsum openai/summarize_from_feedback billsum AmazonScience/massive amazon_us_reviews amazon_reviews_multi openwebtext microsoft/CLUES Norod78/microsoft-fluentui-emoji-512-whitebg Norod78/microsoft-fluentui-emoji-768 MicPie/unpredictable_msdn-microsoft-com microsoft/codexglue_method_generation… See the full description on the dataset page: https://huggingface.co/datasets/CNXT/autotrain-data-chatx.

Bloomberg Billionaires Index View profiles for each of the world’s 500 richest people, see the biggest movers, and compare fortunes or track returns. As of December 12, 2024 The Bloomberg Billionaires Index is a daily ranking of the world’s richest people. Details about the calculations are provided in the net worth analysis on each billionaire’s profile page. The figures are updated at the close of every trading day in New York. Rank Name Total net worth $ Last change $ YTD change Country / Region

Join us at LechterVentures.com to explore other interesting topics in Data Science and marketplaces.

Context

Numerous people had asked me to study the role retail trading plays in driving asset prices. Using this as my inspiration, I found a dataset with hourly tick data for ~9,000 stocks and another one with hourly Robinhood user participation data (aka how many Robinhood users own a stock in a particular time period) . Here you will not only find the data used to perform my research, but also a copy of the notebook I ended up using. Excited to see what the community does with this!

Content

2 major sources were used to acquire this data: - Stooq - While not written in English, this website hosts numerous free stock tick datasets. I was able to directionally confirm accuracy of the data vs what my personal brokerage account reported over this time period. I cannot speak to the preciseness of this data. - RobinTrack - This website collects Robinhood user participation data for stocks that trade on their platform. Per Bloomberg, it does appear Robinhood will stop providing access to this data in the near future (as of August 2020)

Additionally, you can find the notebook I used to prepare the research for my article here

The data covers the time period between September 2019 and July 2020.

Understanding the Files

I originally tried to input this information directly in the Data Explorer section but Kaggle kept bugging out.

Robinhood_Master_v1.csv

This is the master dataframe that includes hourly tick and Robinhood user participation data for ~9,000 stocks going back ~1 year - #: Index column; it can be ignored - Clean_Datetime: This column can also be ignored. - Close: Closing price for the stock noted in the Ticker column during this row's time period - High: Highest price reached for the stock noted in the Ticker column during this row's time period - Low: Lowest price reached for the stock noted in the Ticker column during this row's time period - Close: Closing price for the stock noted in the Ticker column during this row's time period - Open: Opening price for the stock noted in the Ticker column during this row's time period - OpenInt: This column can be ignored - its almost all 0 - Ticker: The stock ticker analyzed in a given row. For example, if this shows 'AAPL' then this row is reporting data on Apple stock. - users _ holding _ first: The initial amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ last: The final amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ max: The highest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ min: The lowest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period

df_apple_final.csv

This is the pre-processed dataframe that includes the cleaned predictors I used for my Apple time series modeling. All columns (except "y", "Clean _ Datetime _ PST" and "ds") were shifted back 1 day. The idea here is that all predictors need to occur on or before the target data. Otherwise, you end up using future data to predict the past. I'll only describe columns below that are not also found in the master dataframe. - users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for Apple - users _ holding _ 13D _ change: the 13 day change in Robinhood stock ownership for Apple - Open 6D_change: the 6 day change in Apple’s stock market opening price - Open 13D_change: the 13 day change in Apple’s stock market opening price - SPY users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for SPY - SPY Open 1D _ change: the day-over-day change in SPY’s stock market opening price - SPY Open 13D _ change: the 13 day change in SPY’s stock market opening price

custom_functions.py

In my notebook, I had to create a couple custom functions to run the graphs used there (this file is explicitly imported into my notebook with all the other python libraries). If you want to run my notebook, make sure it can find this file so it can run these functions.

The market for Internet Financial Information Services is a rapidly growing one, with a market size of XX million and a CAGR of XX%. This growth is being driven by a number of factors, including the increasing popularity of online investing, the need for accurate and timely financial information, and the growing use of data analytics and artificial intelligence (AI) in the financial sector. The market is segmented by type (Financial Data Information Services, Securities Market Trading System Services, Financial Website Information Services), application (Institution, Individual Investor), and region. The key players in the market include Bloomberg, Refinitiv, FactSet, S&P, Moody's Analytics, ICE Data Services, Thomson Reuters, Dow Jones, Morgan Stanley, Wind, Hithink Flush Information Network, East Money Information, Shanghai DZH, Beijing Compass Technology, Hundsun, Shenzhen Fortune Trend, Huatai Securities, CITIC Securities, Guotai Junan Securities, Haitong Securities, Sina Finance, and CBN. Some of the key trends in the market include the increasing use of cloud-based services, the development of new data analytics tools, and the adoption of AI. These trends are providing financial institutions with new opportunities to improve their operations and provide better service to their customers. The market is expected to continue to grow rapidly in the coming years, as the demand for accurate and timely financial information continues to increase.

Bloomberg's website has published overview of Astra Resources Plc, engaging in mining iron ore, coal, and other steel making commodities. Its mining operations include thermal coal in Nigeria; iron ore in India; iron sands in Cagayan; and gold in Cambodia. The company was founded in 2009 and is based in Adelaide, Australia. It has operations in Africa, Eastern Europe, India, Australia, and South East Asia. The screenshot was converted in PDF format and used as referencing documents.

Herewith attached, for the replication of results, are 5 days (2019-07-08 - 2019-07-12) of supporting test data for 10 JSE and A2X securities. The datasets provided directly by A2X comprise of messages from the market data feed while the JSE datasets comprise of trade and quote data obtained from Bloomberg Pro.The 10 equities listed on the JSE that are considered are: Absa Group Ltd (ABG), Anglo American Plc (AGL), British American Tobacco Plc (BTI), FirstRand Limited (FSR), Nedbank Group Ltd (NED), Naspers Ltd (NPN), Standard Bank Group Ltd (SBK), Shoprite Holdings Ltd (SHP), Sanlam Limited (SLM), Sasol Ltd (SOL). The data for each security comes in the form of csv files with 5 columns: times, type, value, size, condcode.The 10 equities listed on A2X that are considered are: Aspen Pharmacare (APN), African Rainbow Min Ltd (ARI), AVI Ltd (AVI), Coronation Fund Managers (CML), Growthpoint Prop Ltd (GRT), Mr Price Group Limited (MRP), Naspers Ltd (NPN), Standard Bank Group Ltd (SBK), Sanlam Limited (SLM), Santam Limited (SNT). The data comes in the form of zipped flat files separated by date which contain concactenated string messages corresponding to events in the continuous trading feed ordered in time with nanosecond precision.This data should only be used to aid the reproducibility of the paper: Comparing the market microstructure between two South African exchanges. This paper explores an empircal comparisons of markets trading similar shares, in a similar regulatory and economic environment, but with vastly different liquidity, cost and business models. We compare the distributions and auto-correlations of returns on different time scales, we compare price impact and master curves, and we compare the cost of trading on each exchange. All implemetations are done using Julia Pro.Julia script files and implementation instructions for reproducing our results can be found on our GitHub site:https://github.com/CHNPAT005/PCIJAPTG-A2XvsJSE

The data consists of transaction data for 10 equities from the Johannesburg Stock Exchange. The data consists of five trading days ranging from 2019-06-24 to 2019-06-28. The data has been processed to only contain transactions. Furthermore, transactions with the same time stamp have been aggregated using a volume weighted average so that there is only one trade per time stamp. Missing data is indicated with NaN's.The 10 equities included are: FirstRand Limited (FSR), Shoprite Holdings Ltd (SHP), Absa Group Ltd (ABG), Nedbank Group Ltd (NED), Standard Bank Group Ltd (SBK), Sasol Ltd (SOL), Mondi Plc (MNP), Anglo American Plc (AGL), Naspers Ltd (NPN) and British American Tobacco Plc (BTI).The data structure in each csv file is 10 columns which contain the trading information for the assets traded. The transaction data are in chronological order. The three files have the exact same structure with each file containing information for the transaction tuple: price, time and volume.The data should only be used to aid the reproducibility for the paper:The Epps effect under alternative sampling schemes. The steps to reproduce the results can be found in our GitHub site: https://github.com/CHNPAT005/PCRBTG-VT.The research focuses on investigating the Epps effect under different definitions of time.The work is funded by the South African Statistical Association. The original data was sourced from Bloomberg Pro. The code for the research is done using Julia Pro.

Graph and download economic data for Chicago Fed National Financial Conditions Index (NFCI) from 1971-01-08 to 2025-11-21 about financial, indexes, and USA.