# Midterm Study Guide

## Contents

## In class exam instructions

- We will be starting a bit late (about 1:45PM)
- Desks must be clear
- No laptops, books, calculators, laptops
- Write your name and GT User ID on the exam
- Do not open booklet until instructed to do so
- You will have 35 minutes to complete the exam
- At end of the exam, bring your booklet to the front with ID
- Please be silent after the exam and do not discuss with others
- One person has extra time due to a disability
- Circle ONLY ONE answer per question

## Overview For Fall 2017

The test is closed book, closed notes, closed internet. You are allowed to use scratch paper and a pencil. Calculators are NOT allowed.

Scope of midterm exam for Fall 2018

The midterm covers all material up to and including the lessons listed in the schedule before the midterm.

Topics:

MC1 Lesson 1 Reading, slicing and plotting stock data

MC1 Lesson 2 Working with many stocks at once

MC1 Lesson 3 The power of NumPy

MC1 Lesson 4 Statistical analysis of time series

MC1 Lesson 5 Incomplete data

MC1 Lesson 6 Histograms and scatter plots

MC1 Lesson 7 Sharpe ratio & other portfolio statistics

MC1 Lesson 8 Optimizers: Building a parameterized model

MC1 Lesson 9 Optimizers: How to optimize a portfolio

MC2 Lesson 1 So you want to be a hedge fund manager?

MC2 Lesson 2 Market mechanics

MC2 Lesson 3 What is a company worth?

MC2 Lesson 4 The Capital Assets Pricing Model (CAPM)

MC2 Lesson 5 How hedge funds use the CAPM

MC2 Lesson 7 Dealing with data

MC3 Lesson 1 How Machine Learning is used at a Hedge Fund

MC3 Lesson 2 Regression

MC3 Lesson 3 Assessing a Machine Learning algorithm

MC3 Lesson 4 Ensemble learners, bagging and boosting

Is the stock market rigged? 60 minutes documentary

Readings

For a specific list of readings, see the course schedule (at left).

Specific topics for Machine Learning:

- Types of learning problems: Regression versus Classification
- Supervised versus Unsupervised.
- Compare properties of kNN versus, decision trees, linear regression (training cost, query cost, prediction accuracy)
- Compare different methods of building a decision tree
- Parameterized models versus instance-based models
- Overfitting
- Measuring the quality of predictions: RMSE, correlation, other?
- Bagging
- Boosting

Topics not covered (to be covered on final):

- MC2 Lesson 6 Technical analysis
- MC2 Lesson 8 The Efficient Markets Hypothesis

## Resources

- Exam 1 Answer Key: Media:2017Fall-Exam1-answer-key.pdf
- Exam 1 Version A: Media:2017Fall-Exam1-version-A.pdf
- Exam 1 Version B: Media:2017Fall-Exam1-version-B.pdf
- Exam 1 Version D: Media:2017Fall-Exam1-version-D.pdf

- Python Question Pool
- Finance Question Pool
- ML Question Pool
- Media:ML_questions.xls
- Media:2015-midterm.pdf
- Media:2016Spring-midterm.pdf

## Example Questions

1. Fill in section A to complete code that will cause the following output:

import numpy as np j = np.random.random([2,2]) print j print _A_

Output:

[[ 0.1624438 , 0.14157016], [ 0.07818402, 0.85854546]] [[ 1. , 1. ], [ 0.48129886, 6.06445229]])

2. Why (or why not) is Sharpe Ratio a better measure of portfolio performance than cumulative return?

3. Suppose we have a group of N assets in our portfolio with allocation w_i to each asset i, each with a specific Beta_i and alpha_i. Write an equation that describes the expected return of the entire portfolio in terms of the market return r_m.

4. Assume two stocks, A and B, that you’d like to combine into a portfolio with weights w_a, w_b respectively. You have calculated Beta values and estimated alphas for each. Your objective is to gain positive return while minimizing market risk. Utilize CAPM to find the weights that achieve this goal. Assuming your estimates of alpha are correct, what is your expected return?

- Beta_A = 0.5, Beta_B = 1.0
- alpha_A = 5%, alpha_B = -10%

5. Write the equation for Sharpe Ratio using 6 months of data assuming returns are calculated monthly or weekly.

6. What is the output of the following code snippet?

import numpy as np A = np.ones((3,3)) w = np.array([0.0, 0.1, 0.2]) print (A*w).sum() print (A*w).sum(axis=0) print (A*w).sum(axis=1)

7. A company promises to pay a $30 dividend every year starting one year from now. Based on the riskiness of the company, you believe the discount rate should be 7% per year. Assume 0% inflation. Based on these factors only, what is the present value of this company?

8. Define each of these company valuation methods (one or two sentences or a formula):

- Intrinsic value
- Market capitalization
- Book value

9. Define

- Weak EMH
- Semi-strong EMH
- Strong EMH

10. Which form(s) of the EMH prohibit profiting from Fundamental Analysis?

11. Given an order book be able to estimate the average price that a BUY or SELL order will be filled at.

12. Consider a $100 bill and a $100 US bond payable in one year. Which statement is true: a. The $100 is worth more than the bond b. The $100 bond is worth more than the bill. c. They are both worth the same.

12. Write the equation that describes the Fundamental Law of Active Management. Define each term.

13. Consider two portfolio managers A and B. They both have the same Information Ratio and the same Information Coefficient. Suppose B wants to improve his Information Ratio to be double that of A. How can he accomplish his goal?

14. Define "survivor bias"