Content

The Federal Reserve Board has discontinued this series as of October 11, 2016. More information, including possible alternative series, can be found at http://www.federalreserve.gov/feeds/h15.html.

Annualized using a 360-day year or bank interest. Source: Bloomberg and CTRB ICAP Fixed Income & Money Market Products.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1971-01-04

• Observation End : 2016-10-07

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Arvydas Venckus on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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...

Daily closing prices for green bond, green stocks, and carbon future market, spanning October 14, 2014 to July 30, 2024, were obtained from Bloomberg. The Euro/USD exchange rate is taken from the European Central Bank.

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.

Based on Bloomberg's Billionaires index...

The Bloomberg Billionaires Index is a daily ranking of the world's richest people. In calculating net worth, Bloomberg News strives to provide the most transparent calculations available, and each individual billionaire profile contains a detailed analysis of how that person's fortune is tallied.

The index is a dynamic measure of personal wealth based on changes in markets, the economy and Bloomberg reporting. Each net worth figure is updated every business day after the close of trading in New York. Stakes in publicly traded companies are valued using the share's most recent closing price. Valuations are converted to U.S. dollars at current exchange rates.

Closely held companies are valued in several ways, such as by comparing the enterprise value-to-Ebitda or price-to-earnings ratios of similar public companies or by using comparable transactions. Calculations of closely held company debt -- if net debt cannot be determined -- are based on the net debt-to-Ebitda ratios of comparable peers. The value of closely held companies adjusts daily based on market moves for peer companies or by applying the market movement of a relevant industry index. The criteria used to choose peer companies is based on the closely held asset's industry and size.

When ownership of closely held assets cannot be verified, they aren't included in the calculations. The specific valuation methodology for each closely held company is included in the net worth analysis section of a billionaire's profile. Additional details included in the valuation notes for each asset are available to subscribers of the Bloomberg Professional Service.

A standard liquidity discount of 5 percent is applied to most closely held companies where assets may be hard to sell. When a different percentage is used an explanation is given. No liquidity discounts are applied to the values of public stakes. In some instances, a country risk discount is also applied based on a person's concentration of assets and ease of selling them in a given geography. A country's risk is assessed based on Standard & Poor's sovereign debt ratings.

If a billionaire has pledged as collateral shares he or she holds in a public company, the value of those shares or the value of a loan taken against them is removed from the net worth calculation. If reliable information can be obtained about the ultimate use of those borrowed funds, that value is added back into the calculation.

Hedge fund businesses are valued using the average market capitalization-to-assets under management ratios of the most comparable publicly traded funds. Fee income is not considered because it cannot be uniformly verified. Personal funds invested along with outside capital are not included in the calculation. A "key man" risk discount of 25 percent is applied to funds whose performance is tied to a single individual. Assets under management are updated using ADV forms filed with the federal government and news reports, and returns are factored when sourced to reports from credible news outfits, the HFRI Index and industry analysts.

Net worth calculations include dividend income paid and proceeds from the sale of public and closely held shares. Taxes are deducted based on prevailing income, dividend and capital gains tax rates in a billionaire's country of residence. Taxes are applied at the highest rate unless there is evidence to support a lower percentage, in which case an explanation is given in the net worth summary. For calculations of cash and other investable assets, a hybrid return based on holdings in cash, government bonds, equities and commodities is applied.

No assumptions are made about personal debt. Family members often hold a portion of a billionaire's assets. Such transfers don't change the nature of who ultimately controls the fortune. As a result, Bloomberg News operates under the rule that all billionaire fortunes are inherently family fortunes and credit family fortunes to the founders or ranking family members who are determined to have direct control over the assets. When individual stakes can be verified and adult family members have an active role in a business, the value is credited to each individual.

Each billionaire -- or a representative -- is given an opportunity to respond to questions regarding the net worth calculation, including assets and liabilities.

Bloomberg News editorial policy is to not cover Bloomberg L.P. As a result, Michael Bloomberg, the founder and majority owner of Bloomberg L.P., isn't considered for this ranking.

Because calculating net worth requires a degree of estimation, bull and bear case scenarios that would make a person's fortune higher or lower than the Bloomberg Billionaires Index valuation are included on the Bloomberg Professional Service. A confidence rating also is included on each profile:

Index Time Series for Vanguard Total Corporate Bond ETF ETF Shares. The frequency of the observation is daily. Moving average series are also typically included. The fund is a fund of funds and employs an indexing investment approach designed to track the performance of the Bloomberg U.S. Corporate Bond Index, which measures the investment-grade, fixed-rate, taxable corporate bond market. The index includes U.S. dollar-denominated securities that are publicly issued by industrial, utility, and financial issuers.

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.

The EUR/USD exchange rate rose to 1.1619 on December 2, 2025, up 0.08% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 0.86%, and is up by 10.57% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on December of 2025.

Discover the booming financial trading software market! This in-depth analysis reveals a projected CAGR of 8% through 2033, driven by algorithmic trading, HFT, and regulatory compliance. Learn about key players, market trends, and regional growth opportunities.

The global financial data services market is on a significant growth trajectory, driven by the increasing digitization of the financial industry and the escalating demand for data-driven insights for investment and risk management. This expansion is fueled by the growing complexity of global financial markets, stringent regulatory compliance requirements, and the proliferation of advanced technologies like AI and machine learning for predictive analytics. Key market players are focusing on providing real-time, accurate, and comprehensive data solutions to cater to a diverse clientele, including banks, asset management firms, and hedge funds. The Asia Pacific region is emerging as the fastest-growing market, presenting lucrative opportunities, while North America continues to hold the largest market share due to its mature financial infrastructure and high technology adoption rate.

Key strategic insights from our comprehensive analysis reveal:

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is no longer a trend but a fundamental driver, enabling predictive analytics, algorithmic trading, and personalized financial advice, thereby creating significant value.
The Asia-Pacific region, led by China and India, is projected to witness the highest CAGR, driven by rapid economic growth, increasing foreign investment, and widespread digital transformation in its BFSI sector.
There is a surging demand for specialized data services, particularly in Environmental, Social, and Governance (ESG) criteria and alternative data (e.g., satellite imagery, social media sentiment), as investors seek a more holistic view for decision-making.

Global Market Overview & Dynamics of Financial Data Services Market Analysis The global financial data services market is experiencing robust growth, set to expand from $19,761.5 million in 2021 to an estimated $52,972.4 million by 2033, progressing at a compound annual growth rate (CAGR) of 8.564%. This growth is underpinned by the financial sector's digital revolution, where real-time, accurate data is crucial for maintaining a competitive edge, ensuring regulatory compliance, and managing complex risks. The increasing adoption of cloud computing and AI is further democratizing access to sophisticated analytical tools, broadening the market's reach. Global Financial Data Services Market Drivers

Increasing Regulatory Complexity and Compliance Demands: Stringent regulations like MiFID II, Dodd-Frank, and Basel III mandate greater transparency and robust reporting, compelling financial institutions to invest heavily in reliable data services to ensure compliance and manage risk effectively.
Growth of Algorithmic and High-Frequency Trading: The rising prevalence of automated trading strategies that rely on instantaneous access to vast amounts of market data to execute trades in microseconds is a primary driver for real-time data feed services.
Digital Transformation in the BFSI Sector: The broad shift towards digital platforms in banking, wealth management, and insurance necessitates sophisticated data services for everything from customer analytics and personalized services to fraud detection and operational efficiency.

Global Financial Data Services Market Trends

Adoption of AI and Machine Learning for Predictive Analytics: Financial firms are increasingly leveraging AI/ML to analyze market trends, forecast asset performance, and automate investment decisions, driving demand for high-quality, structured datasets.
Surge in Demand for ESG Data: A growing investor focus on sustainability and ethical investing has created a massive trend for specialized ESG (Environmental, Social, and Governance) data services to assess corporate performance beyond traditional financial metrics.
Rise of Cloud-Based Data Platforms: The shift towards cloud-based solutions offers financial institutions greater flexibility, scalability, and cost-efficiency in accessing and analyzing large datasets, moving away from legacy on-premise systems.

Global Financial Data Services Market Restraints

Data Security and Privacy Concerns: The high sensitivity of financial data makes it a prime target for cyberattacks. The risk of data breaches and the need to comply with data privacy regulations like GDPR pose significant challenges and operational costs.
High Cost of Premium Data Services: Subscriptions to premium, real-time financial data feeds and sophisticated...

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