Financial Data Analytics

• Cash Flows
  Analyze cash flows including annuities, amortization and depreciation, present and future value, and rates of return
  
• Investment Performance Metrics
  Analyze asset performance metrics, including risk-adjusted alphas, maximum drawdown, and Sharpe ratio
  
• Multivariate Normal Regression
  Regression analysis, with or without missing data, using likelihood-based methods for multivariate normal regression
  
• Data Transformation
  Transform data, including conversions for constraints, covariance, and holdings
  
• Life Tables
  Life tables compute probabilities, hazards, or survivor counts using mortality models
  
• Chart Financial Data
  Charting options for financial data
  
• Machine Learning Applications
  Apply machine learning techniques to financial applications
  

Featured Examples

Machine Learning for Statistical Arbitrage I: Data Management and Visualization

Apply techniques for managing, processing, and visualizing large amounts of financial data in MATLAB®.

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Machine Learning for Statistical Arbitrage II: Feature Engineering and Model Development

Create a continuous-time Markov model of limit order book (LOB) dynamics, and develop a strategy for algorithmic trading based on patterns observed in the data.

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Machine Learning for Statistical Arbitrage III: Training, Tuning, and Prediction

Use Bayesian optimization to tune hyperparameters in the algorithmic trading model, supervised by the end-of-day return.

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