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Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to 2024.

Historical data on Russell US indexes. Data Files Cover: Sector Weights - Individual index files with complete history in each. Sector Weights - Monthly files with all indexes in each. Index Holdings Closed Positions -Periodic (M/Q) files with all indexes in each. Includes Daily Index Holdings for each closing day. Monthly Contribution to Return, an analysis of each sector and industry contributing to the overall return of the Russell Index.

Graph and download economic data for CBOE Russell 2000 Volatility Index (RVXCLS) from 2004-01-02 to 2025-07-10 about VIX, volatility, stock market, and USA.

Prices for United States Stock Market Index (US2000) including live quotes, historical charts and news. United States Stock Market Index (US2000) was last updated by Trading Economics this July 14 of 2025.

In 2015 barely a percentile of companies mentioned AI in their Russell 3000 earnings calls. This has radically changed in 2023 with over **** percent of companies mentioning the concept of AI in their calls. Only in 2022 and 2020 did the mentions drop somewhat, though the overall growth of the amount of mentions has been steady.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Fidelity Russell 2000 Index Fund

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Historical AI model predictions and analysis for Russell 2000 ETF stock across multiple timeframes and confidence levels

Key information about Italy MIB

• Italy MIB closed at 38,655.1 points in Feb 2025, compared with 36,471.8 points at the previous month end
• Italy Equity Market Index: Month End: FTSE: MIB data is updated monthly, available from Sep 2003 to Feb 2025, with an average number of 22,408.3 points
• The data reached an all-time high of 43,755.0 points in Apr 2007 and a record low of 12,873.8 points in May 2012

• In the latest reports, FTSE Italia All Share recorded a monthly P/E ratio of 9.8 in Jun 2022

Browse Russell 2000 (RUT) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Check here for 1 min data walkthrough: https://resources.clootrack.com/employee-experience-russell-1ooo-index

Data source: Employee feedback from prominent employee review sites

Key data points: 1. Employee Experience sentiment trend 2. Employee Experience drivers trend 3. Actionable for SWOT 4. Review rating trend 5. Review volume trend These data points are segmented by Industry, Industry Sectors, Job Role, Company, Ticker, Country, City for Russell 1000 companies

Use case - Save several days during: 1. Equity research 2. Strategic planning 3. Portfolio audit 4. Deal thesis analysis 5. Due diligence

Data Duration: History: 12 Months rolling (older made available based on req)

Data refresh: Monthly

Delivery Format: 1. Web dashboard with GenAI Co-pilot 2. Csv

Trusted by 150+ Global Hedge Funds, Private Equity Funds, Financial Institutions, Companies, Brands

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

iShares Russell 2000 Growth ETF: Growth in the Smallest?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Key information about United Kingdom FTSE 100

• United Kingdom FTSE 100 closed at 8,809.7 points in Feb 2025, compared with 8,674.0 points at the previous month end
• UK Equity Market Index: Month End: FTSE 100 data is updated monthly, available from Jan 1984 to Feb 2025, with an average number of 5,430.8 points
• The data reached an all-time high of 8,809.7 points in Feb 2025 and a record low of 1,010.1 points in Jul 1984

• In the latest reports, FTSE All-Share recorded a daily P/E ratio of 14.4 in Jul 2022

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

iShares Russell 2000 BuyWrite: A Smart Bet for Income?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Browse Weekly - Russell 2000 (RUTW) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Context

Time Series Analysis is an important part in Data science toolkit. This dataset was created from Yahoo Finance with the help of their official API yfinance.

Content

This dataset contains closing price of Top 4 indexes recorded over daily frame from 1994 to 2021 October (27 years).

As of June 2024, the Vanguard Mega Cap Growth Index provided the ******* one-year return rate. The Vanguard Russell 1000 Growth Index Fund ranked ****** having a one-year return rate of **** percent. As of June 2024, the Vanguard Total Stock Market Index Fund was the largest fund owned by Vanguard, with net assets under management worth approximately **** trillion U.S. dollars. What is the difference between mutual funds and exchange traded funds? Both mutual funds and exchange traded funds (ETFs) originate from the concept of pooled fund investing, which bundles securities together to offer investors a more diversified portfolio. However, mutual funds and ETFs have some key differences. For instance, ETFs offer more flexible trading as they trade during the day like stocks, while mutual funds only allow transactions at the end of the day. Moreover, ETFs are mostly passively-managed and mirror a designated index. On the other hand, mutual funds are typically actively-managed, as it can be seen by comparing the number of actively and passively-managed mutual funds in the United States. Vanguard Founded by John C. Bogle in 1975, Vanguard is a U.S. asset management company that offers both mutual funds and ETFs. Headquartered in Malvern, Pennsylvania, Vanguard was the ****** largest provider of ETFs in the United States after BlackRock Financial Management, with assets under management worth almost *** trillion U.S. dollars. Likewise, in 2024, Vanguard ranked among the largest providers of mutual funds worldwide. The total assets under management of Vanguard increased considerably since its foundation in 1975, and peaked at *** trillion U.S. dollars in 2024.

The data files contain seven low-dimensional financial research data (in .txt format) and two high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own effort. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS). The two high-dimensional data sets contain daily adjusted close prices (from Jan 1, 2013 to Dec 31, 2014) of the stocks, which are in the index components list (as of Jan 7, 2015) of S&P 500 and Russell 2000 indices, respectively.

South Africa TRI: FTSE: Equity Investment Instruments data was reported at 18,268.267 NA in Jun 2022. This records a decrease from the previous number of 18,549.318 NA for May 2022. South Africa TRI: FTSE: Equity Investment Instruments data is updated monthly, averaging 14,305.459 NA from Mar 2021 (Median) to Jun 2022, with 16 observations. The data reached an all-time high of 19,392.468 NA in Mar 2022 and a record low of 10,848.540 NA in Aug 2021. South Africa TRI: FTSE: Equity Investment Instruments data remains active status in CEIC and is reported by FTSE Russell. The data is categorized under Global Database’s South Africa – Table ZA.Z002: Financial Times Stock Exchange: Enhanced ICB Framework: Total Return Index.

Market Size and Growth: The global ESG Certification market is projected to reach a value of USD XX million by 2033, expanding at a CAGR of XX% during the forecast period of 2025-2033. This growth is attributed to factors such as increasing investor demand for sustainable investments, regulatory frameworks mandating ESG disclosures, and growing awareness of environmental and social issues among businesses. Industry Dynamics and Trends: Key drivers of the market include the rise of sustainable finance, the adoption of ESG reporting standards, and the increasing importance of environmental, social, and governance (ESG) factors in investment decisions. The market is segmented by type of certification (single, integrated, etc.), application (SMEs, large enterprises), and region (North America, Europe, Asia Pacific, etc.). Major players include S&P Dow Jones Indices, MSCI, and FTSE Russell, among others.