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.

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 September 13 of 2025.

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

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

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

A daily data ranging from January 2014 until December 2018 is employed. The period between January, 1, 2014 until November 7, 2016 refers to the pre-election period. The period ranging from November 8, 2016, until December, 31 2018 defines the post-election period. Four U.S stock price indices are retrieved from DataStream: The standard and Poor’s 500 index (S&P 500) covers the performance of 500 largest capitalization stocks. The Dow Jones Industrial Average (DJIA) index tracks the prices of the top 30 US companies. The NASDAQ 100 measures the performance of the 100 largest non-financial stocks traded on NASDAQ. The Russell 2000 index covers the performance of 2.000 lowest capitalization stocks. A daily political risk index is calculated for each period using Google trends and the principal component analysis.

Net-Income-Applicable-To-Common-Shares Time Series for Herald Investment Trust. Herald Investment Trust PLC is a closed-ended equity mutual fund launched and managed by Herald Investment Management Limited. It invests in public equity markets across the globe. The fund seeks to invest in stocks of companies operating in the technology, telecommunications, and multi-media sectors. It primarily invests in stocks of small-cap companies having market capitalizations lower than £750 million. The fund benchmarks the performance of its portfolio against a composite benchmark comprised of 33.33% Russell 2000 Technology Index and 66.66% Numis Smaller Companies Index. Herald Investment Trust PLC was formed on February 16, 1994 and is domiciled in the United Kingdom.