The global financial database market is experiencing robust growth, driven by increasing demand for real-time data and advanced analytics across various sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors: the proliferation of algorithmic trading and quantitative analysis necessitating high-frequency data feeds; the growing adoption of cloud-based solutions enhancing accessibility and scalability; and the increasing regulatory scrutiny demanding robust and reliable financial data for compliance purposes. The market segmentation reveals a strong preference for real-time databases across both personal and commercial applications, reflecting the time-sensitive nature of financial decisions. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and FactSet maintain significant market share due to their established brand reputation and comprehensive data offerings. However, the emergence of innovative fintech companies and the increasing availability of open-source data platforms are expected to intensify competition and foster market disruption. The geographical distribution of the market reveals North America as the dominant region, followed by Europe and Asia-Pacific. However, the Asia-Pacific region is poised for significant growth, driven by expanding financial markets in countries like China and India. While the market faces restraints such as data security concerns, increasing data costs, and complexities in data integration, the overall trend points toward sustained expansion. The continuous development of sophisticated analytical tools and the growing need for data-driven decision-making will continue to drive the adoption of financial databases across various user segments and geographies, shaping the competitive landscape in the coming years.

Explore the "Bloomberg Quint News Dataset," a comprehensive collection of news articles from Bloomberg Quint, a leading source of financial, business, and economic news in India and around the world.

This dataset includes thousands of articles covering a wide range of topics, such as financial markets, economic policies, corporate news, technology, politics, and more. Each article in the dataset comes with detailed information, including headlines, publication dates, authors, article content, and categories, offering valuable insights for researchers, data analysts, and media professionals.

Key Features:

• Extensive Coverage: Thousands of news articles from Bloomberg Quint, covering diverse topics including business, finance, economics, technology, and global news.
• Detailed Metadata: Each article includes key details such as headline, publication date, author, content, and category, making it ideal for in-depth research and analysis.
• Ideal for Analysis: Perfect for researchers, data scientists, and content strategists looking to analyze trends in news reporting, study media coverage, or develop content strategies.
• Rich Source of Information: Provides up-to-date information on financial markets, economic policies, and global events, helping professionals stay informed and make data-driven decisions.

Whether you're researching financial trends, analyzing media coverage, or developing new content, the "Bloomberg Quint News Dataset" is an invaluable resource that offers detailed insights and extensive coverage of the latest news.

This dataset contains full-text Bloomberg News articles published between 2006 and 2013 (source: Hugging Face mirror of Bloomberg Financial News). A few articles were annotated via a weak-supervision workflow using Gemini labelling, and then those were used to fine-tune DeBERTAv3-base model. The fine-tuned model was then used to score all the remaining articles. The primary motivation for assembling this dataset was to explore whether aggregated news sentiment can help predict movements in the S&P 500 (event studies, signal construction and backtesting).

As of 2024, Sustainalytics was the third most popular source for Environmental, Social, and Governance (ESG) data among institutional investors. Bloomberg ranked second, with ** percent of survey respondents stating they used this source for ESG data. MSCI was the leading source among institutional investors surveyed, with ** percent of investors having a preference for this source.

Large-scale green grabbing for wind and solar PV development in Brazil This repository contains the R code and parts of the data used for the analysis in the paper "Large-scale green grabbing for wind and solar PV development in Brazil" by Michael Klingler, Nadia Amelie, Jamie Rickman, and Johannes Schmidt, available as pre-print. Due to data sharing limitations, we cannot provide all data in the repository. Partly this data is not available publically at all (i.e. Bloomberg data, data by the instituto socio ambiental), partly the data has to be downloaded manually (CAR). We still provide a repository which at least allows to understand the procedures we used during the analysis. Land tenure data set The procedures used to form our final land tenure data set can be found in land-tenure-data/processing.txt It is a mix of analyses in Python and in QGis. Analysis of land tenure data and park ownership/investment information The R-code to analyze the owernship relationships between windpark areas and investors/owners can be found in src/. All required libraries will install automatically. The first two scripts cannot be executed due to data limitations. They create the sankey diagrams linking park areas to onwers and investors: - 1.1-figures-results-1-wind.R - 1.2-figures-results-1-solar.R These three scripts are used to analyze the land tenure types prevailing on parks and comparing them to random areas. They should run with the provided data sets: - 2-random-sampling-areas.R - 3-intersection-parks-land-tenure.R - 4-figures-land-tenure.R This script validates our data against an independent data source. However, it cannot be run as it needs the proprietary Bloomberg database: - 5-validation.R

Financial News Events Dataset - Comprehensive Description

Overview

This synthetic dataset contains 3,024 records of financial news headlines centered around major market events from February 2025 to August 2025. The dataset captures real-time market dynamics, sentiment analysis, and trading patterns across global financial markets, making it ideal for financial analysis, sentiment modeling, and market prediction tasks.

Dataset Specifications

• Total Records: 3,024 rows
• Total Features: 12 columns
• Date Range: February 1, 2025 - August 14, 2025
• File Formats: CSV, JSON, XLSX
• Data Quality: ~5% null values strategically distributed for realistic data cleaning scenarios

Column Descriptions

Key Features & Statistics

Market Events Coverage (20 Categories)

• Stock Market Crashes & Rallies
• Interest Rate Changes & Central Bank Meetings
• Corporate Earnings Reports & IPO Launches
• Government Policy Announcements
• Trade Tariffs & Geopolitical Events
• Cryptocurrency Regulations
• Supply Chain Disruptions
• Economic Data Releases

Global Market Indices (18 Major Indices)

• US Markets: S&P 500, Dow Jones, Nasdaq Composite, Russell 2000
• Indian Markets: NSE Nifty, BSE Sensex
• European Markets: FTSE 100, DAX, Euro Stoxx 50, CAC 40
• Asian Markets: Nikkei 225, Hang Seng, Shanghai Composite, KOSPI
• Others: TSX, ASX 200, IBOVESPA, S&P/TSX Composite

News Sources (18 Reputable Publications)

Major financial news outlets including Reuters, Bloomberg, CNBC, Financial Times, Wall Street Journal, Economic Times, Forbes, and specialized financial publications.

Sector Distribution (18 Business Sectors)

Technology, Finance, Healthcare, Energy, Consumer Goods, Utilities, Industrials, Materials, Real Estate, Telecommunications, Automotive, Retail, Pharmaceuticals, Aerospace & Defense, Agriculture, Transportation, Media & Entertainment, Construction.

Data Quality & Preprocessing Notes

• Realistic Null Distribution: Approximately 5% null values in key columns (Headline, Sentiment, Index_Change_Percent, News_Url) to simulate real-world data collection challenges
• Balanced Sentiment Distribution: Mix of positive, neutral, and negative sentiment classifications
• Diverse Market Conditions: Index changes ranging from -5% to +5% reflecting various market scenarios
• Volume Variability: Trading volumes span 1M to 500M to represent different market liquidity conditions

Potential Use Cases

📈 Financial Analysis

• Market sentiment analysis and trend prediction
• Correlation studies between news events and market movements
• Trading volume pattern analysis

🤖 Machine Learning Applications

• Sentiment classification model training
• Market movement prediction algorithms
• News headline generation models
• Event-driven trading strategy development

📊 Data Visualization Projects

• Interactive market sentiment dashboards
• Time-series analysis of market events
• Geographic distribution of financial news impact
• Sector-wise performance visualization

🔍 Research Applications

• Academic research on market efficiency
• News impact analysis on different sectors
• Cross-market correlation studies
• Event study methodologies

Technical Specifications

• Memory Usage: Approximately 1.5MB across all formats
• **Proces...

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.

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-earnings ratio (PE series), and (vii) industry (SECTOR series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations. Accordingly, our sample comprises a total number of 5,212 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

The Market Data Platform market is experiencing robust growth, driven by the increasing demand for real-time data analytics and the proliferation of sophisticated trading strategies across financial institutions. The market's expansion is fueled by several key factors: the rise of algorithmic trading, the need for faster and more accurate market information, the growing adoption of cloud-based solutions, and the increasing regulatory scrutiny demanding robust data management and compliance. The market is witnessing a shift towards integrated platforms offering a broader range of data sources, advanced analytics capabilities, and improved connectivity. This trend is being further accelerated by the increasing adoption of artificial intelligence (AI) and machine learning (ML) for enhanced data analysis and prediction. Companies like Bloomberg, Refinitiv, and TRDATA are major players, but the market is also witnessing increased competition from innovative technology providers offering specialized solutions and niche capabilities. The forecast period from 2025-2033 suggests substantial growth, driven by the continuous adoption of these solutions across various segments of the financial services industry. The regional distribution will likely favor North America and Europe initially, followed by a gradual increase in adoption rates across Asia-Pacific and other emerging markets. The competitive landscape is dynamic, with established players facing challenges from agile startups offering innovative solutions. The success of individual vendors depends on their ability to provide high-quality data, superior analytical capabilities, seamless integration with existing infrastructure, robust security features, and a commitment to regulatory compliance. While larger players dominate market share, smaller, specialized firms are capitalizing on the demand for specialized data sets and tailored analytical tools. The increasing focus on data security and privacy will impact vendors’ strategies, with enhanced security measures and data governance becoming crucial differentiating factors. Future growth will depend on the industry's continued embrace of technology and the further development of AI/ML-driven analytical applications within the Market Data Platform ecosystem. This growth will likely result in increased consolidation and strategic partnerships in the coming years, shaping the future competitive landscape significantly.

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 Alternative Data Vendor market is experiencing robust growth, driven by the increasing reliance of businesses across diverse sectors on non-traditional data sources for enhanced decision-making. The market's expansion is fueled by several key factors. Firstly, the rise of big data analytics and the need for sophisticated insights beyond traditional data sets are creating significant demand. Secondly, the increasing availability of alternative data sources, including web data, social media sentiment, and transactional data, is further propelling market growth. Finally, the adoption of advanced analytical techniques and AI/ML capabilities to process and interpret this complex data is allowing businesses to gain a competitive edge. We estimate the current market size (2025) at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 18% between 2025 and 2033. This robust growth is projected to continue, driven by increasing investments in data analytics and the expanding adoption of alternative data by businesses in sectors such as BFSI (Banking, Financial Services, and Insurance), and technology. The market is segmented by application (BFSI, Industrial, IT & Telecommunications, Retail & Logistics, Other) and data type (Credit Card Transactions, Consultants, Web Data & Web Traffic, Sentiment & Public Data, Other). While the BFSI sector currently dominates the market, significant growth is anticipated across all sectors as the value of alternative data becomes increasingly recognized. Geographical expansion is another key driver, with North America currently holding the largest market share, followed by Europe. However, Asia Pacific is expected to witness considerable growth due to rising technological advancements and increasing adoption rates in rapidly developing economies. While the availability of reliable and high-quality data remains a challenge, ongoing developments in data governance and regulatory frameworks are mitigating these risks. The competitive landscape includes established players like S&P Global and Bloomberg, as well as innovative startups, leading to a dynamic and ever-evolving market.

This is data and slides for an anticipated forthcoming publication.

Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market. This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment.

The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request.

No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports.

The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019).

Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.

Graph and download economic data for CBOE Emerging Markets ETF Volatility Index (VXEEMCLS) from 2011-03-16 to 2025-12-01 about ETF, VIX, emerging markets, volatility, stock market, and USA.

{"The dataset comprises the following series: 01_RI_data_series: Return index series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 02_DY_data_series: Dividend yield series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 03_MV_data_series: Market value series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 04_Exchange_rates: Exchange rates (source: OECD). 05_LCOE: Average Levelized cost of energy for the United States and Europe (source: IRENA (2022)). 06_PriceLCOE_ratio: Energy prices relative to the levelized cost of energy, where energy prices are pool prices compiled from the Nord Pool power market. 07_Risk_free_and_ERP: (i) 10-year German bond yield and 20-year U.S. bond yield, and (ii) equity risk premium for Europe and U.S. (source: Bloomberg). 08_Unlevered_Betas: Unlevered betas for 23 European firms and 11 North-American firms whose activity is focused on the renewable energy sector (source: S&P Capital IQ). REFERENCES: IRENA, 2022. Renewable Energy Statistics 2022, available at: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2022/Jul/IRENA_Renewable_energy_statistics_2022.pdf (accessed 12 May 2024)."}

This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market's short-term interest rate along with the Vasicek model and joint radiation term structure model., , , # Short-term interest rate estimates based on futures markets

Abstract: This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market short-term interest rate together with the Vasicek model and the joint radiation term structure model

Usage: The data in Table 1 and Table 2 can be read into the established interest rate estimation model code using python to estimate the short-term interest rate

Data structure: month-end time series data; The xlsx tables mainly include Table 1 and Table 2

Source: Bloomberg Data Terminal

Specific variable definition:

• The gold futures price is the futures price data from the end of January 2009 to the end of March 2023, in ounces per dollar
• Soybean futures prices are futures price data from the end of January 2009 to the end of March 2023, in tons per dollar
• Natural gas futures price...

The Media Monitoring Tools Marketsize was valued at USD 4.63 USD Billion in 2023 and is projected to reach USD 12.70 USD Billion by 2032, exhibiting a CAGR of 15.5 % during the forecast period. Recent developments include: October 2023 – Cision, a consumer and media intelligence platform, launched CisionOne, a fully integrated media monitoring solution that provides real-time insights in the U.S., May 2023 – Bloomberg Government (BCOV), a prominent source of news, data, and analytics for public affairs experts, unveiled a new real-time social media monitoring and analysis feature. This addition to BGOV's renowned news and alerts streamlines the process for customers to track pertinent policy matters across platforms, such as Twitter, Reddit, and YouTube., March 2023 – Sony Corporation partnered with Digimind, a competitive intelligence software and social listening provider. This partnership will help grow Digimind’s geographic presence and generate revenue., February 2023 – Meltwater, a media and social intelligence company, secured a spot in G2's 2023 Best Software Awards. G2, the largest and most trusted software marketplace with over 80 million users annually, recognized Meltwater for the second consecutive year., September 2022 – Heyday by Hootsuite, a conversational AI platform, announced it integrated Facebook Messenger and Instagram Direct Messages within its e-commerce Shopify chatbot app. This integration creates opportunities for merchants to associate with their customers., September 2022 – Talkwalker launched an updated solution called “Brand Love Benchmarking”. The solution offers quantifiable and real-time metrics across social networks, forums, and blogs powered by the company's advanced AI capabilities. The solution is based on three key consumer factors: trust, satisfaction, and customer passion., July 2022 – Digimind, a social listening and market intelligence solutions provider, announced an integration with Onclusive. The acquisition significantly expands Onclusive's social media monitoring, insights, and analytics capabilities to clients on a global scale., May 2022 – Brandwatch joined the TikTok Marketing Partner Program to expand its analytics offerings. The partnership allows Brandwatch clients to manage, enhance, act, and understand content on TikTok while leveraging the Brandwatch platform. . Key drivers for this market are: Increasing Usage of Cloud-based Solutions among Enterprises to Aid Market Growth. Potential restraints include: High Costs Involved at the Initial Stage May Restrain Market Growth. Notable trends are: Surging Adoption of Social Listening Platforms is a Prominent Trend.

This dataset contains data on Russian public companies. Financial sector organizations were not included in the sample. It is worth describing separately the data collection process, which was divided into two parts. First of all, qualitative characteristics of corporate governance were searched and systematized. Due to the lack of access to the Bloomberg terminal, it was necessary to collect indicators "manually". Additional difficulties were caused by the fact that there is no regulated corporate governance disclosure form. Thus, companies provided data in different ways. The main sources of information were organizations' annual reports, issuer's quarterly reports, sustainability reports, IFRS financial statements, as well as relevant sections of companies' official websites. At the same time, the listed reports were not always contained on the official websites of the organizations under consideration, therefore the following information resources were used additionally: Interfax, Cbonds and LiveTrader. At the second stage of data collection we systematized financial indicators taken from IFRS statements of the companies. Corporate governance indicators: BSIZE (size of the Board of Directors), BIND (percentage of independent members of the Board of Directors), DUAL1 (combining the roles of CEO and member of the Board of Directors), DUAL2 (combining the roles of CEO and Chairman of the Management Board), YCEO (CEO irremovability - logarithm of the number of years in the position), COMT (presence of internal audit, remuneration and nomination committees), COMTIND (degree of independence of internal committees), AUDIT (dummy variable equal to 1 if audited by a Big4 company), REMUN1 (percentage of remuneration to members of the Board of Directors as a percentage of total personnel expenses), REMUN2 (percentage of remuneration to members of the Management Board as a percentage of total personnel expenses), REMUN3 (percentage of remuneration to key management personnel as a percentage of total personnel expenses). Financial indicators: ROCE (return on capital employed), SIZE (company size), QTOB (Tobin ratio), TANG (tangible fixed assets to total assets), AGE (company age), NDTS (annual depreciation to total assets), INT (interest rate). The sample includes data from 2012 through 2021. It was important to have reliable information for each indicator required for the analysis. Otherwise, the company was excluded from the sample. The final sample included 32 Russian public companies. All indicators were taken in annual terms due to the specifics of corporate governance factors (with a few exceptions, they change no more than once a year). Thus, 320 observations were available for the analysis.

This dataset is a panel dataset of 241 non-financial African corporations spanning 2013-2022. This data is a sample of the attributes of executives in Africa. This was collated from a secondary source, including annual reports, financial databases (Bloomberg, Fitch Connect, and Datastream), Linkendin, and the Times Higher Education Ranking.

This is a summary report on the economic impact of COVID-19 on London’s small and medium enterprises (SMEs). It presents a uniquely granular and timely analysis of the impacts on London’s SMEs by sectoral, financial, employment, and risk indicators and includes deep dive case studies on the economic impact on the Night Time Economy, high streets and town centres, and the Culture and Creative industries. The analysis was undertaken on a pro bono basis by Bloomberg Associates, for and in close collaboration with the GLA providing guidance and direction. Partners supporting Bloomberg Associates included Slalom, Burning Glass Technologies, DueDil and CK Delta. It leverages a combination of public and private data from a range of financial, economic, behavioural, sociographic and demographic sources and complements the macro-economic scenarios for the London economy. The study was conducted between March 2020 and June 2020 and leverages the most updated data that was available at the time. It is important to note that new data and evidence constantly emerges and could be integrated in a potential future iteration of this work. The report has sought to: Illustrate the impact of the pandemic on London’s SMEs and local employment and improve understanding of the scale and scope of the economic challenges that London faces in recovery. Demonstrate the application of “bottom-up” and localised data to create a more complete, granular picture of overall economic impact Show the intersection of impact by sectors and geographies, exploring the relationship between these two factors to demonstrate the risk hot spots across Greater London. If you have any comments or questions related to the report, please email GLA Economics

ABSA-FinSent30M is a synthetic yet structurally realistic dataset designed to support aspect-based sentiment analysis (ABSA) tasks within the financial domain. It includes 3,000 records of time-stamped financial text data collected at 30-minute intervals between January 2021 and February 2025. The dataset is ideal for training and evaluating machine learning and deep learning models in financial sentiment classification, especially in federated or decentralized settings.

Each record simulates a short financial passage (e.g., news headline, social media update, or earnings call note) and contains multiple layers of annotation, including:

Key Features text: Original, randomly generated financial-style sentence.

cleaned_text: Lowercased and punctuation-free version of the text.

tokenized_text: List of individual words (tokens) extracted from the cleaned text.

pos_tags: Part-of-speech tags for each token to assist in syntactic analysis.

named_entities: Mentioned entities such as companies (Apple, Tesla), financial institutions, or key figures (e.g., Elon Musk).

aspect_terms: Financial aspects mentioned in the sentence (e.g., stock_price, earnings, regulation).

aspect_positions: Index positions of aspect terms within the token sequence.

aspect_sentiment_pairs: Aspect-term paired with sentiment polarity (positive, negative, neutral).

source_type: Type of content source (e.g., news, tweet, blog).

source_name: Example source platforms like Bloomberg, Twitter, or Reddit.

ticker_symbol: Simulated stock ticker (e.g., AAPL, TSLA).

sector_industry: Industry classification (e.g., Technology, Automotive).

date_time: Timestamp reflecting real-world trading intervals.

market_context: Simulated numerical context, including stock price and trading volume.

financial_indicators: Key metrics such as earnings per share (EPS), revenue, and debt level.

This dataset serves as a benchmark for building and testing robust NLP models in financial text analytics, including deep transformer architectures, attention-based models, and federated learning systems where privacy and domain specificity are essential.