Exam 2 Study Guide

Exam 2 will cover all material on the schedule since Exam 1. The exam is closed book, closed notes. No calculator is allowed. The topics and readings are as follows:

Topics

• MC2 Lesson 6, Technical analysis
• MC2 Lesson 7, Dealing with data
• MC2 Lesson 8, The Efficient Markets Hypothesis
• MC2 Lesson 9, The fundamental law
• MC2 Lesson 10, Portfolio optimization and the efficient frontier
• MC3 Lesson 5, Reinforcement Learning
• MC3 Lesson 6, Q-Learning (Part 1)
• MC3 Lesson 7, Q-Learning (Part 2) & Dyna
• Options
• Movie: The Big Short
• ML methods for time series data
• Technical trading

Readings

• "What Hedge Funds really do", Chapter 12: Overcoming data quirks to design trading strategies
• "What Hedge Funds really do", Chapter 8: The Efficient Market Hypothesis(EMH) - its three versions
• "What Hedge Funds really do", Chapter 9: The fundamental law of active portfolio management
• "Machine Learning", Chapter 13, Reinforcement Learning

Legacy

• Comparison of different regression learner performance characteristics: Trees, forests, KNN, linreg
• Comparison of learner types: Regression, Classification, RL
• Overfitting: Definition, how to identify, what might prevent it, what might cause it?
• Bootstrap aggregating.
• Boosting.
• Decision trees. Random versus information based construction. Advantages of one over the other.
• Reinforcement learning: How is it defined? Questions about State, Action, Transitions, Reward
• Q-Learning. The update equation, definition of Q
• Dyna-Q
• Things you should know because you did the projects. In sample versus out of sample. Istanbul problem, why did shuffling help?
• Options

Readings:

• "Machine Learning", Chapter 1, Introduction
• "Machine Learning", Chapter 8, Instance-based Learning
• "Machine Learning", Chapter 3, Decision Tree Learning
• "Machine Learning", Chapter 3, Decision Tree Learning
• Paper: "Perfect Random Tree Ensembles" by Adele Cutler
• "Machine Learning", Chapter 13, Reinforcement Learning