Data Science for Finance
This course covers the fundamental statistical tools used by quantitative analysts with a focus on forecasting financial series. Several key methods used in modern data science (also known as “statistical inference”, “machine learning”, or “artificial intelligence”) are presented and applied to financial data.
- Introduction to statistical learning. Features of financial data.
- K-nearest neighbour. The bias-variance trade-off.
- Least squares and linear projections. Weighted least squares.
- Modelling nonlinearities with interaction terms and local regressions.
- Ridge regression.
- Modelling nonlinearities with regression splines.
- Introduction to time-varying models.
- Maximum likelihood (ML).
- Penalized ML. Weighted ML. Local ML. Splines and ML.
- Bootstrap and bagging.
- Introduction to Bayesian inference.
- Approximate Bayesian inference. Markov Chain Monte Carlo.
- State space models. Kalman filter. Introduction to the particle filter.
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