Are hedge funds worth your money?

Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content

This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

• Convertible Arbitrage - https://risk.edhec.edu/conv-arb/
• CTA Global - https://risk.edhec.edu/cta-global/
• Distressed Securities - https://risk.edhec.edu/dist-sec/
• Emerging Markets - https://risk.edhec.edu/emg-mkts/
• Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/
• Event Driven - https://risk.edhec.edu/event-driven/
• Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/
• Global Macro - https://risk.edhec.edu/global-macro/
• Long/Short Equity - https://risk.edhec.edu/ls-equity/
• Merger Arbitrage - https://risk.edhec.edu/merger-arb/
• Relative Value - https://risk.edhec.edu/relative-value/
• Short Selling - https://risk.edhec.edu/short-selling/
• Funds of Funds - https://risk.edhec.edu/fof/

Acknowledgements

All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration

The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

Are hedge funds worth your money? Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

Convertible Arbitrage - https://risk.edhec.edu/conv-arb/ CTA Global - https://risk.edhec.edu/cta-global/ Distressed Securities - https://risk.edhec.edu/dist-sec/ Emerging Markets - https://risk.edhec.edu/emg-mkts/ Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/ Event Driven - https://risk.edhec.edu/event-driven/ Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/ Global Macro - https://risk.edhec.edu/global-macro/ Long/Short Equity - https://risk.edhec.edu/ls-equity/ Merger Arbitrage - https://risk.edhec.edu/merger-arb/ Relative Value - https://risk.edhec.edu/relative-value/ Short Selling - https://risk.edhec.edu/short-selling/ Funds of Funds - https://risk.edhec.edu/fof/ Acknowledgements All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.