Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

The Comprehensive Annual Financial Reports are presented in three main sections; the Introductory Section, the Financial Section, and the Statistical Section. The Introductory Section includes a financial overview, discussion of Iowa's economy and an organizational chart for State government. The Financial Section includes the state auditor's report, management's discussion and analysis, audited basic financial statements and notes thereto, and the underlying combining and individual fund financial statements and supporting schedules. The Statistical Section sets forth selected unaudited economic, financial trend and demographic information for the state on a multi-year basis. Reports for multiple fiscal years are available.

Market Size statistics on the Financial Data Service Providers industry in the US

Financial Data Feeds Market Outlook

According to our latest research, the global Financial Data Feeds market size reached USD 8.3 billion in 2024, driven by rapid digital transformation and increasing reliance on real-time analytics across financial sectors. The market is projected to expand at a robust CAGR of 8.7% from 2025 to 2033, culminating in a forecasted value of USD 17.3 billion by 2033. This strong growth trajectory is underpinned by the surging demand for accurate, timely, and comprehensive data to support trading, risk management, and regulatory compliance activities worldwide. As per our latest research, the proliferation of algorithmic trading, increasing regulatory requirements, and the rise of cloud-based solutions are significant growth drivers for the Financial Data Feeds market.

One of the primary growth factors fueling the Financial Data Feeds market is the exponential rise in algorithmic and high-frequency trading. Financial institutions, asset managers, and hedge funds are increasingly leveraging sophisticated trading algorithms that require ultra-low latency, real-time data feeds to make split-second decisions. The competitive edge in today’s trading landscape is often determined by the speed and accuracy of data acquisition, analysis, and execution. This has led to a greater reliance on real-time and historical data feeds, compelling vendors to innovate and offer solutions that can handle vast data volumes with minimal latency. Additionally, the growing adoption of machine learning and artificial intelligence in trading strategies further amplifies the need for high-quality, granular data feeds, making this segment a cornerstone of market expansion.

Another significant growth driver is the tightening regulatory environment across global financial markets. Regulatory bodies such as the SEC, ESMA, and MAS are enforcing stringent compliance and reporting standards, necessitating robust data management and transparency. Financial institutions must now source, process, and report vast amounts of reference and transactional data accurately and promptly to meet these mandates. This has led to increased investment in advanced financial data feed solutions that support compliance and regulatory reporting. Furthermore, the complexity of cross-border transactions and the emergence of new asset classes, including cryptocurrencies, have escalated the demand for diverse and comprehensive data feeds, propelling market growth.

The ongoing digital transformation within the financial sector is also a pivotal factor driving the Financial Data Feeds market. As financial institutions migrate to cloud-based infrastructures and embrace digital-first strategies, the demand for flexible, scalable, and cost-efficient data delivery models has surged. Cloud-based data feeds offer significant advantages in terms of scalability, accessibility, and integration with other digital tools and platforms, enabling organizations to respond quickly to market shifts and customer needs. The integration of data feeds with advanced analytics, portfolio management, and risk assessment platforms is enabling financial firms to derive actionable insights, optimize decision-making, and enhance overall operational efficiency. This digital evolution is expected to further accelerate market growth in the coming years.

From a regional perspective, North America continues to dominate the Financial Data Feeds market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The region’s leadership is attributed to the presence of major financial hubs, advanced technological infrastructure, and a high concentration of market participants. However, Asia Pacific is emerging as the fastest-growing region, with a notable CAGR driven by rapid financial sector development, regulatory modernization, and increasing adoption of digital trading platforms. Meanwhile, Europe is witnessing steady growth due to evolving regulatory frameworks and the rising importance of sustainable finance and ESG reporting. Latin America and the Middle East & Africa are also experiencing gradual growth, spurred by ongoing financial sector reforms and increased foreign investment.

Extract of data from financial transaction system to enable detailed financial reporting across all business units in the Civil Service

Graph and download economic data for Domestic Financial Sectors; Total Liabilities and Equity, Level (FBLIEQQ027S) from Q4 1945 to Q2 2025 about finance companies, IMA, companies, equity, finance, liabilities, financial, and USA.

The statistic presents the leading financial data service companies in the United States in 2015, by revenue. In that year, Visa was ranked second with the revenue of approximately 13.88 billion U.S. dollars.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

Landline random-digit-dial sample excludes 12 municipalities near the nuclear power plant in Fukushima, representing less than 1% of the population.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1005.

Mode of data collection

Landline and Cellular Telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

CN: Industrial Enterprise: Private: Sales Tax & Surcharge: ytd data was reported at 258.990 RMB bn in Dec 2015. This records an increase from the previous number of 226.420 RMB bn for Nov 2015. CN: Industrial Enterprise: Private: Sales Tax & Surcharge: ytd data is updated monthly, averaging 24.843 RMB bn from Jan 2001 (Median) to Dec 2015, with 156 observations. The data reached an all-time high of 258.990 RMB bn in Dec 2015 and a record low of 0.693 RMB bn in Feb 2001. CN: Industrial Enterprise: Private: Sales Tax & Surcharge: ytd data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Private Enterprise.

The Finance sector's operating environment was previously characterised by record-low interest rates. Nonetheless, high inflation prompted the Reserve Bank of Australia (RBA) to hike the cash rate from May 2022 onwards. This shift allowed financial institutions to impose higher loan charges, propelling their revenue. Banks raised interest rates quicker than funding costs in the first half of 2022-23, boosting net interest margins. However, sophisticated competition and digital disruption have reshaped the sector and nibbled at the Big Four's dominance, weighing on ADIs' performance. In the first half of 2025, the fierce competition has forced ADIs to trim lending rates even ahead of RBA moves to protect their slice of the mortgage market. Higher cash rates initially widened net interest margins, but the expiry of cheap TFF funding and a fierce mortgage war are now compressing spreads, weighing on ADIs' profitability. Although ANZ's 2024 Suncorp Bank takeover highlights some consolidation, the real contest is unfolding in tech. Larger financial institutions are combatting intensified competition from neobanks and fintechs by upscaling their technology investments, strengthening their strategic partnerships with cloud providers and technology consulting firms and augmenting their digital offerings. Notable examples include the launch of ANZ Plus by ANZ and Commonwealth Bank's Unloan. Meanwhile, investor demand for rental properties, elevated residential housing prices and sizable state-infrastructure pipelines have continued to underpin loan growth, offsetting the drag from weaker mortgage affordability and volatile business sentiment. Overall, subdivision revenue is expected to rise at an annualised 8.3% over the five years through 2024-25, to $524.6 billion. This growth trajectory includes an estimated 4.8% decline in 2024-25 driven by rate cuts in 2025, which will weigh on income from interest-bearing assets. The Big Four banks will double down on technology investments and partnerships to counter threats from fintech startups and neobanks. As cybersecurity risks and APRA regulations evolve, financial institutions will gear up to strengthen their focus on shielding sensitive customer data and preserving trust, lifting compliance and operational costs. In the face of fierce competition, evolving regulations and shifting customer preferences, consolidation through M&As is poised to be a viable trend for survival and growth, especially among smaller financial institutions like credit unions. While rate cuts will challenge profitability within the sector, expansionary economic policies are poised to stimulate business and mortgage lending activity, presenting opportunities for strategic growth in a dynamic market. These trends are why Finance subdivision revenue is forecast to rise by an annualised 1.1% over the five years through the end of 2029-30, to $554.9 billion

United States Index: NYSE Financial data was reported at 7,713.770 31Dec2002=5000 in Nov 2018. This records an increase from the previous number of 7,543.040 31Dec2002=5000 for Oct 2018. United States Index: NYSE Financial data is updated monthly, averaging 6,396.895 31Dec2002=5000 from Dec 2002 (Median) to Nov 2018, with 192 observations. The data reached an all-time high of 9,933.900 31Dec2002=5000 in May 2007 and a record low of 2,518.780 31Dec2002=5000 in Feb 2009. United States Index: NYSE Financial data remains active status in CEIC and is reported by New York Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z001: NYSE: Indexes.

Indonesia: Financial markets development, depth: The latest value from 2021 is 0.241 index points, a decline from 0.258 index points in 2020. In comparison, the world average is 0.255 index points, based on data from 157 countries. Historically, the average for Indonesia from 1980 to 2021 is 0.189 index points. The minimum value, 0.084 index points, was reached in 1980 while the maximum of 0.367 index points was recorded in 1998.

Comprehensive dataset containing 32 verified Financial institution businesses in LI with complete contact information, ratings, reviews, and location data.

CN: CE: Foreign Funded: Current Assets: Inventories data was reported at 1.364 RMB bn in 2018. This records an increase from the previous number of 1.289 RMB bn for 2017. CN: CE: Foreign Funded: Current Assets: Inventories data is updated yearly, averaging 1.326 RMB bn from Dec 2000 (Median) to 2018, with 16 observations. The data reached an all-time high of 3.489 RMB bn in 2014 and a record low of 0.370 RMB bn in 2000. CN: CE: Foreign Funded: Current Assets: Inventories data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJB: Catering Enterprise: Financial Data: Foreign Funded.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

CE Transact is the premier alternative data set for consumer spend on credit and debit cards, available as an aggregated feed. Hedge fund investors trust CE transaction data to track quarterly performance, company-reported KPIs, and earnings predictions for stock market strategic decision-making.

Comprehensive financial and analytical metrics for Lybra Finance, including key performance indicators, market data, and ecosystem analytics.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Industrial Enterprise: Financial Expense: Year to Date: Shanghai data was reported at 2,500.000 RMB mn in Mar 2025. This records a decrease from the previous number of 2,520.000 RMB mn for Feb 2025. Industrial Enterprise: Financial Expense: Year to Date: Shanghai data is updated monthly, averaging 4,507.000 RMB mn from Jan 2001 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 14,840.000 RMB mn in Dec 2015 and a record low of -1,530.000 RMB mn in May 2024. Industrial Enterprise: Financial Expense: Year to Date: Shanghai data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Financial Expense: By Province.

The value of total assets of financial institutions worldwide increased gradually between 2002 and 2023, despite a slight drop in 2022. Bank assets attributed for the largest segment of the total assets, with ***** trillion U.S. dollars in 2023. Other financial intermediaries (including money market funds, hedge funds, other investment funds, captive financial institutions and moneylenders, central counterparties, broker-dealers, finance companies, trust companies, and structured finance vehicles) followed, with total assets exceeding *** trillion U.S. dollars.