Exam 2 Study Guide

From Quantitative Analysis Software Courses
Jump to: navigation, search
  • All laptops and phones must be put away before exams are distributed.
  • Do not open exam booklets until told to do so.
  • You will have 35 minutes to complete the exam.
  • Closed notes, closed book, no scratch paper, no calculators.
  • Use the answer sheet to record your answers.
  • At the end of the exam you must return your answer sheet and question booklet.
  • We will check your Georgia Tech student ID also.

Exam 2 will cover all material on the schedule since Exam 1. The exam will include 30 questions. You will have 30 minutes to complete the exam. The exam is closed book, closed notes. No calculator is allowed. The topics and readings are as follows:


  • 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
  • Black Scholes
  • Movie: The Big Short
  • ML methods for time series data
  • Technical trading


  • "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


  • 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


  • "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