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Quantitative Investment Analysis, Workbook. Edition No. 4. CFA Institute Investment Series

  • Book

  • 288 Pages
  • October 2020
  • John Wiley and Sons Ltd
  • ID: 5829343

The thoroughly revised and updated fourth edition of the companion workbook to Quantitative Investment Analysis is here.

Now in its fourth edition, the Quantitative Investment Analysis Workbook offers a range of practical information and exercises that will facilitate your mastery of quantitative methods and their application in today's investment process. Part of the reputable CFA Institute Investment Series, the workbook is designed to further your hands-on experience with a variety of learning outcomes, summary overview sections, and challenging problems and solutions.

The workbook provides all the statistical tools and latest information to help you become a confident and knowledgeable investor, including expanded problems on Machine Learning algorithms and the role of Big Data in investment contexts. Well suited for motivated individuals who learn on their own, as well as a general reference, this companion resource delivers a clear, example-driven method for practicing the tools and techniques covered in the primary Quantitative Investment Analysis, 4th Edition text.?? Inside you'll find information and exercises to help you:

  • Work real-world problems associated with the modern quantitative investment process
  • Master visualizing and summarizing data
  • Review the fundamentals of single linear and multiple linear regression
  • Use multifactor models
  • Measure and manage market risk effectively

In both the workbook and the primary Quantitative Investment Analysis, 4th Edition text, the authors go to great lengths to ensure an even treatment of subject matter, consistency of mathematical notation, and continuity of topic coverage that is critical to the learning process.

For everyone who requires a streamlined route to mastering quantitative methods in investments, Quantitative Investment Analysis Workbook, 4th Edition offers world-class practice based on actual scenarios faced by professionals every day.

Table of Contents

Part 1 Learning Objectives, Summary Overview, and Problems 1

Chapter 1 The Time Value of Money 3

Learning Outcomes 3

Summary 3

Practice Problems 4

Chapter 2 Organizing, Visualizing, and Describing Data 9

Learning Outcomes 9

Summary 9

Practice Problems 13

Chapter 3 Probability Concepts 25

Learning Outcomes 25

Summary 26

Practice Problem 28

Chapter 4 Common Probability Distributions 33

Learning Outcomes 33

Summary 34

Practice Problems 35

Chapter 5 Sampling and Estimation 43

Learning Outcomes 43

Summary 43

Practice Problems 45

Chapter 6 Hypothesis Testing 51

Learning Outcomes 51

Summary 52

Practice Problems 54

Chapter 7 Introduction to Linear Regression 63

Learning Outcomes 63

Summary 63

Practice Problems 64

Chapter 8 Multiple Regression 75

Learning Outcomes 75

Summary 76

Practice Problems 78

Chapter 9 Time-Series Analysis 103

Learning Outcomes 103

Summary 104

Practice Problems 105

Chapter 10 Machine Learning 123

Learning Outcomes 123

Summary 123

Practice Problems 125

Chapter 11 Big Data Projects 129

Learning Outcomes 129

Summary 129

Practice Problems 131

Chapter 12 Using Multifactor Models 141

Learning Outcomes 141

Summary 141

Practice Problems 143

Chapter 13 Measuring and Managing Market Risk 149

Learning Outcomes 149

Summary 149

Practice Problems 152

Chapter 14 Backtesting and Simulation 161

Learning Outcomes 161

Summary 161

Practice Problems 163

Part II Solutions 169

Chapter 1 The Time Value of Money 171

Solutions 171

Chapter 2 Organizing, Visualizing, and Describing Data 185

Solutions 185

Chapter 3 Probability Concepts 195

Solutions 195

Chapter 4 Common Probability Distributions 199

Solutions 199

Chapter 5 Sampling and Estimation 207

Solutions 207

Chapter 6 Hypothesis Testing 215

Solutions 215

Chapter 7 Introduction to Linear Regression 223

Solutions 223

Chapter 8 Multiple Regression 229

Solutions 229

Chapter 9 Time-Series Analysis 241

Solutions 241

Chapter 10 Machine Learning 249

Solutions 249

Chapter 11 Big Data Projects 251

Solutions 251

Chapter 12 Using Multifactor Models 261

Solutions 261

Chapter 13 Measuring and Managing Market Risk 267

Solutions 267

Chapter 14 Backtesting and Simulation 273

Solutions 273

About the CFA Program 279