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Preface xv

Acknowledgments xvii

About the CFA Institute Investment Series xix

Chapter 1 The Time Value of Money 1

Learning Outcomes 1

1. Introduction 1

2. Interest Rates: Interpretation 2

3. The Future Value of a Single Cash Flow 4

4. The Future Value of a Series of Cash Flows 13

5. The Present Value of a Single Cash Flow 16

6. The Present Value of a Series of Cash Flows 20

7. Solving for Rates, Number of Periods, or Size of Annuity Payments 27

8. Summary 38

Practice Problems 39

Chapter 2 Organizing, Visualizing, and Describing Data 45

Learning Outcomes 45

1. Introduction 45

2. Data Types 46

3. Data Summarization 54

4. Data Visualization 68

5. Measures of Central Tendency 85

6. Other Measures of Location: Quantiles 102

7. Measures of Dispersion 109

8. The Shape of the Distributions: Skewness 119

9. The Shape of the Distributions: Kurtosis 121

10. Correlation between Two Variables 125

11. Summary 132

Practice Problems 135

Chapter 3 Probability Concepts 147

Learning Outcomes 147

1. Introduction 148

2. Probability, Expected Value, and Variance 148

3. Portfolio Expected Return and Variance of Return 171

4. Topics in Probability 180

5. Summary 188

References 190

Practice Problem 190

Chapter 4 Common Probability Distributions 195

Learning Outcomes 195

1. Introduction to Common Probability Distributions 196

2. Discrete Random Variables 196

3. Continuous Random Variables 210

4. Introduction to Monte Carlo Simulation 228

5. Summary 231

References 233

Practice Problems 234

Chapter 5 Sampling and Estimation 241

Learning Outcomes 241

1. Introduction 242

2. Sampling 242

3. Distribution of the Sample Mean 248

4. Point and Interval Estimates of the Population Mean 251

5. More on Sampling 261

6. Summary 267

References 269

Practice Problems 270

Chapter 6 Hypothesis Testing 275

Learning Outcomes 275

1. Introduction 276

2. Hypothesis Testing 277

3. Hypothesis Tests Concerning the Mean 287

4. Hypothesis Tests Concerning Variance and Correlation 303

5. Other Issues: Nonparametric Inference 310

6. Summary 314

References 317

Practice Problems 317

Chapter 7 Introduction to Linear Regression 327

Learning Outcomes 327

1. Introduction 328

2. Linear Regression 328

3. Assumptions of the Linear Regression Model 332

4. The Standard Error of Estimate 335

5. The Coefficient of Determination 337

6. Hypothesis Testing 339

7. Analysis of Variance in a Regression with One Independent Variable 347

8. Prediction Intervals 350

9. Summary 353

References 354

Practice Problems 354

Chapter 8 Multiple Regression 365

Learning Outcomes 365

1. Introduction 366

2. Multiple Linear Regression 366

3. Using Dummy Variables in Regressions 381

4. Violations of Regression Assumptions 387

5. Model Specification and Errors in Specification 401

6. Models with Qualitative Dependent Variables 414

7. Summary 422

References 425

Practice Problems 426

Chapter 9 Time-Series Analysis 451

Learning Outcomes 451

1. Introduction to Time-Series Analysis 452

2. Challenges of Working with Time Series 454

3. Trend Models 454

4. Autoregressive (AR) Time-Series Models 464

5. Random Walks and Unit Roots 478

6. Moving-Average Time-Series Models 486

7. Seasonality in Time-Series Models 491

8. Autoregressive Moving-Average Models 496

9. Autoregressive Conditional Heteroskedasticity Models 497

10. Regressions with More than One Time Series 500

11. Other Issues in Time Series 504

12. Suggested Steps in Time-Series Forecasting 505

13. Summary 507

References 508

Practice Problems 509

Chapter 10 Machine Learning 527

Learning Outcomes 527

1. Introduction 527

2. Machine Learning and Investment Management 528

3. What is Machine Learning? 529

4. Overview of Evaluating ML Algorithm Performance 533

5. Supervised Machine Learning Algorithms 539

6. Unsupervised Machine Learning Algorithms 559

7. Neural Networks, Deep Learning Nets, and Reinforcement Learning 575

8. Choosing an Appropriate ML Algorithm 589

9. Summary 590

References 593

Practice Problems 593

Chapter 11 Big Data Projects 597

Learning Outcomes 597

1. Introduction 597

2. Big Data in Investment Management 598

3. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data 599

4. Data Preparation and Wrangling 603

5. Data Exploration Objectives and Methods 617

6. Model Training 629

7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks 639

8. Summary 664

Practice Problems 665

Chapter 12 Using Multifactor Models 675

Learning Outcomes 675

1. Introduction 675

2. Multifactor Models and Modern Portfolio Theory 676

3. Arbitrage Pricing Theory 677

4. Multifactor Models: Types 683

5. Multifactor Models: Selected Applications 695

6. Summary 706

References 707

Practice Problems 708

Chapter 13 Measuring and Managing Market Risk 713

Learning Outcomes 713

1. Introduction 714

2. Understanding Value at Risk 714

3. Other Key Risk Measures-Sensitivity and Scenario Measures 735

4. Using Constraints in Market Risk Management 750

5. Applications of Risk Measures 755

6. Summary 764

References 766

Practice Problems 766

Chapter 14 Backtesting and Simulation 775

Learning Outcomes 775

1. Introduction 775

2. The Objectives of Backtesting 776

3. The Backtesting Process 776

4. Metrics and Visuals Used in Backtesting 792

5. Common Problems in Backtesting 801

6. Backtesting Factor Allocation Strategies 807

7. Comparing Methods of Modeling Randomness 813

8. Scenario Analysis 824

9. Historical Simulation versus Monte Carlo Simulation 828

10. Historical Simulation 830

11. Monte Carlo Simulation 835

12. Sensitivity Analysis 840

13. Summary 848

References 849

Practice Problems 849

Appendices 855

Glossary 865

About the Authors 883

About the CFA Program 885

Index 887