The hedge fund industry boomed in the 1990s, and the value of assets managed by hedge funds worldwide grew steadily until 2007. The value fell markedly the following year because of the financial crisis and did not recover until 2013. In 2024, the value of assets under management (AUM) of hedge funds reached over **** trillion U.S. dollars. Which firms dominate the hedge fund industry? The biggest hedge funds in the market typically attain their size by combining exceptional results, a solid track record, and efficient risk management tactics. In 2023, Field Street Capital Management was the biggest hedge fund company, with nearly *** billion U.S. dollars of assets under management. Some other prominent global hedge funds by AUM include Citadel, Bridgewater Associates, Mariner Investment Group LLC, etc. These industry giants often boast a diverse range of investment strategies and maintain a global presence, which allows them to capitalize on opportunities across diverse sectors and assets. Hedge Funds: What's changing? Hedge funds constantly tweak their investment strategies to keep up with market shifts. The cryptocurrency market introduces a novel asset class that is distinct from traditional financial markets. Therefore, the primary reason behind hedge funds investing in digital assets was to diversify their portfolios. The escalating interest in cryptocurrencies and blockchain technology prompted hedge funds to explore new prospects and risks associated with digital assets. In 2021, the average assets under management of crypto hedge funds more than doubled from the previous year, rising from ** to ** million U.S. dollars.

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.

Fortress Holdings (FTRE): A Bastion of Growth or a Crumbling Citadel?

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