Goodwill Time Series for Morningstar Inc. Morningstar, Inc. provides independent investment insights in the United States, Asia, Australia, Canada, Continental Europe, the United Kingdom, and internationally. The company operates in five segments: Morningstar Data and Analytics; PitchBook; Morningstar Wealth; Morningstar Credit; and Morningstar Retirement. The company offers managed investments, including mutual funds, ETFs, separate accounts, collective investment trusts, model portfolios, equities, and fixed income securities; Morningstar Direct is an investment-analysis and reporting application; Morningstar Advisor Workstation, a suite of tools to provide advice to clients. It also provides PitchBook that offers data and research covering the private capital markets comprising venture capital, private equity, private credit and bank loans, and merger and acquisition activities through the PitchBook platform; model portfolios and wealth platforms; Morningstar Model Portfolios, an advisor service with model portfolios for fee-based independent financial advisors; and Morningstar.com that discovers, evaluates, and monitors stocks, ETFs, and mutual funds, as well as build and monitor portfolios and markets. In addition, the company offers credit ratings, research, data, and credit analytics solutions; Morningstar DBRS which offers securitizations and other structured finance instruments, such as asset-backed securities, residential and commercial mortgage-backed securities, and collateralized loan obligations. Further, it provides managed retirement accounts, fiduciary services, Morningstar Lifetime Allocation funds, and custom models; Morningstar Sustainalytics provides environmental, social and governance data, research, and ratings; and Morningstar Indexes offers market indexes used for performance benchmarks and as the basis for investment products and other portfolio strategies. Morningstar, Inc. was incorporated in 1984 and is headquartered in Chicago, Illinois.

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.

Morningstar Stock: Hold for 3 Months, But Watch for Opportunities

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.

Morningstar (MORN): Navigating a Shifting Investment Landscape

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

Index Time Series for VanEck ETF Trust - VanEck Morningstar SMID Moat ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund normally invests at least 80% of its total assets in securities that comprise the fund"s benchmark index. The index is comprised of small- and medium-capitalization companies as defined by Morningstar, that Morningstar determines have sustainable competitive advantages based on a proprietary methodology that considers quantitative and qualitative factors. It is non-diversified.

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.

Is iShares Morningstar Small-Cap ETF the Answer for Small-Cap Exposure?

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

Graph and download economic data for ICE BofA US High Yield Index Total Return Index Value (BAMLHYH0A0HYM2TRIV) from 1986-08-31 to 2025-08-01 about return, yield, interest rate, interest, rate, indexes, and USA.

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.

Morningstar (MORN) Stock Forecast: Navigating the Financial Landscape

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

Index Time Series for Invesco Senior Loan ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund generally will invest at least 80% of its total assets in the components of the index. Strictly in accordance with its guidelines and mandated procedures Morningstar, Inc. ("Morningstar" or the "index provider") compiles, maintains and calculates the underlying index, which tracks the market value weighted performance of the largest institutional leveraged loans based on market weightings, spreads and interest payments.

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.

Morningstar: Sharpening Market Insight? (MORN)

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

Net-Receivables Time Series for AVI Global Trust PLC. AVI Global Trust plc is a closed-ended equity mutual fund launched and managed by Asset Value Investors Limited. The fund invests in public equity markets of countries across the globe. It seeks to invest in stocks of companies operating across diversified sectors. The fund primarily invests in value stocks of companies across all market capitalizations. It employs fundamental analysis with bottom-up stock picking approach with a focus on factors like companies trading on discounts to net asset value, good quality underlying assets with appreciation potential at compelling valuations, and balance sheet strength to create its portfolio. The fund benchmarks the performance of its portfolio against the Morgan Stanley Capital International All Country World ex-US Index, Morningstar Investment Trust Global Index and Morgan Stanley Capital International All Country World Index. It was formerly known as British Empire Trust Plc. AVI Global Trust plc was formed in July 1, 1889 and is domiciled in the United Kingdom.

Tax-Provision Time Series for AVI Global Trust PLC. AVI Global Trust plc is a closed-ended equity mutual fund launched and managed by Asset Value Investors Limited. The fund invests in public equity markets of countries across the globe. It seeks to invest in stocks of companies operating across diversified sectors. The fund primarily invests in value stocks of companies across all market capitalizations. It employs fundamental analysis with bottom-up stock picking approach with a focus on factors like companies trading on discounts to net asset value, good quality underlying assets with appreciation potential at compelling valuations, and balance sheet strength to create its portfolio. The fund benchmarks the performance of its portfolio against the Morgan Stanley Capital International All Country World ex-US Index, Morningstar Investment Trust Global Index and Morgan Stanley Capital International All Country World Index. It was formerly known as British Empire Trust Plc. AVI Global Trust plc was formed in July 1, 1889 and is domiciled in the United Kingdom.