This analysis dives deep into a comprehensive collection of financial and macroeconomic data, armed with diverse machine learning features to unlock actionable insights in stock market modeling. Researchers, analysts, and enthusiasts will find it an invaluable resource for exploring the potential of this powerful technology in predicting market behavior.

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

  • Download Dataset
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