OVERVIEW OF MACHINE LEARNING TECHNIQUES
The machine learning paradigm
- Features selection
- Training vs test sets
- Cross validation
- Data snooping bias
Unsupervised Learning/Supervised Learning
- Overview of main classification algorithms
- Structured vs Unstructured Data
- Issues with both types of data
- Setting up the problem with multiple linear regression as the learning model
- Exercise: predict 1-day SPY return using simple technical indicators
- Stepwise linear regression
- Classification and regression trees (CART)
- Stopping criteria for tree growing
- Using the whole tree or selecting certain nodes for prediction?
- Reducing overfitting by cross-validation
- Increasing training sample size by bootstrapping/bagging
- Learning from past errors: boosting
- Which technique gives the most accurate predictions?
Support vector machine (SVM)
- Predicting the sign of returns
Neural networks (NN)
- Neural network as nonlinear function fitting
- What network architecture to pick?
- Drawbacks of using NN for financial predictions
- Avoiding overfitting: dropout technique
Extended exercise: predicting SPY returns using various learning algorithms
EXTENDED EXERCISE ON FEATURES SELECTION
- Building a multifactor stock selection model using fundamental factors
- Techniques: multiple regression, stepwise regression, and CART
- What fundamental factors are most useful for predicting stock portfolio returns?