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.

The industrial databases market is booming, projected to reach $2.3 Billion by 2033 with a CAGR of 10.8%. Discover key trends, drivers, and restraints shaping this dynamic sector, including the rise of IoT, Industry 4.0, and cloud-based solutions. Explore leading companies and regional market shares in this comprehensive analysis.

Discover the booming financial database market! This in-depth analysis reveals key trends, growth drivers, and leading companies shaping the future of financial data, including real-time & historical databases. Explore market size, regional breakdowns, and future projections to 2033.

The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2021. We then screened and processed the initial sample data, including (a) Screening for companies with both RepRisk's ESG rating and Bloomberg's ESG rating. Specifically, the selection is based on samples with the same ISIN code and companies' English names in the Bloomberg and RepRisk lndex (RRI) databases. The ISIN code is a securities coding standard developed by the International Organization for Standardization (ISO) and is a unique code used to identify securities in each country or region around the world. We exclude samples that do not provide ISIN codes or have inconsistent English names. (b) We exclude observations with missing values for the main variables. (c) We exclude the ST, *ST and PT trading status samples during the observation period. Our final sample contains 1456 firm-year observations.The ESG disclosure score data and ESG performance score data required for the ESG-washing construction are respectively obtained from the Bloomberg database and the RepRisk Index (RRI) database of the Wharton Research Centre for Data Studies (WRDS). Positive media coverage data is sourced from the China Research Data Services Platform (CNRDS), while the instrumental variable (IV_population) is obtained from the EPS database and Juhe Data (https://www.gotohui.com/). Unless otherwise stated, all other data in this study are from the China Stock Market and Accounting Research (CSMAR) database.Data on executive company changes were collected manually by the authors back-to-back and independently. Then we compared and reconciled the data collected by each, and where there were discrepancies, we again collected and calibrated the data to maximize their reliability. We first obtained executive biographies from the CSMAR database, and the missing values were retrieved from Sina Finance ( https://finance.sina.com.cn/). Due to the unstructured nature of the resume data, we manually processed more than 30,000 resumes of executives to get the data of executives' company changes, based on which we calculated the per capita number of job hops of all executives in each company. The number of part-time jobs held by executives also reflects their pursuit of career changes and development, so in the robustness test the per capita mean of the number of part-time jobs held by executives is used as a proxy variable for careerist orientation. These data can be obtained directly from the CSMAR database.

Bloomberg Financial News Embeddings for Vector Database Benchmarking

  Dataset Description

This dataset contains pre-computed embeddings of Bloomberg financial news articles, designed for evaluating vector database performance. The embeddings are generated using Google's EmbeddingGemma-300M model.

  Purpose

Benchmark dataset for evaluating vector database performance on financial news domain, specifically designed for use with VectorDBBench.

  Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/cryptolab-playground/Bloomberg-Financial-News-embedding-gemma-300m.

Given the contradictory empirical evidence on the relationship between green R&D expenditure and corporate Green Innovation performance (GIP), The present research study is a distinctive investigation into the moderating impacts of ESG reporting on this relationship. We utilized a data collection of 3,846, firm-year observations of A-share listed firms in China from 2016 to 2022 from CSMAR and Bloomberg databases. The firm’s Corporate GIP is assessed and measured by looking at the total quantity of green patents. Lastly, models with multiple regression analyses and fixed effects were employed. The findings show that ESG reporting has a positive and significant impact on the association between corporate GIP and green R&D expenditure, implying its compensating and supportive function in the form of green signals in green outputs. This research could help executives and lawmakers, especially in developing countries to build innovative environmental strategies for business sustainability.

The dataset is updated to March 21, 2021. The data is collected from Bloomberg https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/, which gathered vaccine information from government websites, official statements and Bloomberg interviews. Local governments and the CDC sometimes report different totals for the same jurisdiction; in these cases, Bloomberg uses the higher number. It can take several days for counts to be reported to databases. ****There might be several days of data missing because the crawler was down.

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.

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

GIP and green R&D investment impacts on ESG reporting (panel data analysis with fixed effects).

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.

A proprietary IFC dataset containing data on the total value of IFC-approved green, social, sustainability and sustainability-linked bond issuance value compiled from many sources, including Bloomberg, Environmental Finance, Climate Bond Initiative (CBI), and third-party sources. Visit the 2024 Emerging Markets Green Bond Report here: https://www.ifc.org/en/insights-reports/2025/emerging-market-green-bonds-2024

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.

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Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

In the context of the ESG era, this study provides an in-depth analysis of the ESG practices of listed companies and their impact on business performance in Korea and Taiwan, two of the Four Little Dragons economies in Asia. Although these two regions are similar in terms of economic size, they show significant differences in their ESG implementation strategies and effects. Based on the Bloomberg database, this study empirically analyzes data from 113 Taiwanese and 113 Korean firms, using Tobin’s q ratio as a measure of business performance. The findings show that there is complexity in the association between ESG scores and firms’ business performance. In South Korea, government policies and large conglomerates contribute significantly to ESG practices, while in Taiwan, the economic structure dominated by SMEs has led to different characteristics of ESG practices. All of these differences reflect the influence of intra-firm factors on performance. The findings of this study not only enrich the theoretical foundation of the relationship between ESG and business performance, but the findings provide valuable regional insights and recommendations for international investors, corporate managers, and policymakers in the Asia-Pacific region to implement ESG strategies, especially when considering the specific market environment, economic structure, and internal factors of the firms they operate in order to achieve sustainable growth and competitive advantage.

Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, Bloomberg, and the European Commission’s EAFO portal.

Eco-Movement integrates data from 3000+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

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The hardware report includes full geolocation, operator/brand, and technical information for each individual station, as well as two specific hardware attributes: DC Hardware Manufacturer and DC Hardware Model. This report is available for all countries in our database (see full list of territories below). The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

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ABOUT US Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

The data comprises multiple variables from Chinese A-share listed companies between 2011 and 2021. A dual fixed-effects model was employed to examine the correlation and underlying mechanisms between corporate ESG performance and R&D innovation outcomes. The study further analyzed the impact and influence mechanisms of the three ESG dimensions—environmental, social, and governance—on R&D innovation performance. Data sources are as follows: All listed companies' financial and other data are sourced from the Wind database. Innovation and financing constraint data for listed companies are sourced from the CSMAR database. ESG data for listed companies are sourced from Bloomberg.

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website. The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file. RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined below Metadata_Unclean_full_dataset.csv RCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelled Metadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified below Metadata_Clean_selection_dataset.csv The data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors). The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included. We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors. From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process. We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following: - the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website); - the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities). For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner. The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them. A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted. To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes. Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.

The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online. (Research Data Support provided by Springer Nature)