Hedge funds are significantly boosting their bullish positions on US crude oil amidst global supply concerns and economic policies from China, affecting market dynamics.

Explore Getting started in hedge funds through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

High-Flyer hedge fund boldly redirects its $13.79 billion assets towards AGI, emblematic of its technological foresight and ambition.

Explore The investor's guide to hedge funds through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

Discover how hedge funds exiting the cocoa futures market have led to price surges and increased volatility, reshaping global cocoa trade dynamics.

Explore Market risk management for hedge funds : foundations of the style and implicit value-at-risks through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

The largest local newspaper owner in the United States in 2024 was Gannett, with a total of 310 papers. Whilst larger companies - Gannett, News Media Group (owned by hedge fund Alden Global Capital), and Lee Enterprises all owned fewer papers in 2024 than in 2023, some regional chains saw increases. Cherry Road Media, founded in 2020, owned a total of 81 papers by 2024.

Explore The hibiscus hedge through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

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.

Currency Hedge: An Investment Safe Haven for the Global Market? (iShares Currency Hedged MSCI ACWI ex U.S. ETF)

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

In September 2022, the hedge shear price amounted to $8,090 per ton (CIF, Turkey), rising by 46% against the previous month.

Explore Money mavericks : confessions of a hedge fund manager through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

Hedge funds reduce net-bullish crude positions, reflecting a cooling market sentiment influenced by trade concerns, the Ukraine conflict, and potential OPEC+ output changes.

Explore The complete Brambly Hedge through data • Key facts: author, publication date, book publisher, book series, book subjects • Real-time news, visualizations and datasets

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.

Landcadia's Warrant: A Hedge Against Market Volatility? (LCAHW)

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

Explore Untitled from Two-Way Mirror / Hedge Projects through data • Key facts: creation period, creation period details, width, height, art medium, museum, acquisition year, artists, countries • Real-time news, visualizations and datasets

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.

The Best Way to Manage Your Risk: Diversification or Hedging?

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

Explore Loophead Summer Hedge School through data • Key facts: number of books published, authors, publication period covered • Real-time news, visualizations and datasets

The newspaper company with the most daily newspapers in the United States was Gannett as of 2024, with 215 daily titles. Tribune/News Media Group, also known as Digital First Media and owned by hedge fund Alden Global Capital, ranked second with 77 daily newspapers.

In March 2023, the hedge shear price amounted to $14,282 per ton (CIF, Spain), jumping by 25% against the previous month.