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Whether you are a novice investor or an experienced practitioner, Quantitative Investment Analysis, 4th Edition has something for you. Part of the CFA Institute Investment Series, this authoritative guide is relevant the world over and will facilitate your mastery of quantitative methods and their application in todays investment process. This updated edition provides all the statistical tools and latest information you need to be a confident and knowledgeable investor. This edition expands coverage of Machine Learning algorithms and the role of Big Data in an investment context along with capstone chapters in applying these techniques to factor modeling, risk management and backtesting and simulation in investment strategies. 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. Well suited for motivated individuals who learn on their own, as well as a general reference, this complete resource delivers clear, example-driven coverage of a wide range of quantitative methods. Inside you'll find: Learning outcome statements (LOS) specifying the objective of each chapter A diverse variety of investment-oriented examples both aligned with the LOS and reflecting the realities of todays investment world A wealth of practice problems, charts, tables, and graphs to clarify and reinforce the concepts and tools of quantitative investment management You can choose to sharpen your skills by furthering your hands-on experience in the Quantitative Investment Analysis Workbook, 4th Edition (sold separately)—an essential guide containing learning outcomes and summary overview sections, along with challenging problems and solutions.

TABLE OF CONTENTS 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 3.1. The Frequency of Compounding 9 3.2. Continuous Compounding 11 3.3. Stated and Effective Rates 12 4. The Future Value of a Series of Cash Flows 13 4.1. Equal Cash Flows—Ordinary Annuity 14 4.2. Unequal Cash Flows 15 5. The Present Value of a Single Cash Flow 16 5.1. Finding the Present Value of a Single Cash Flow 16 5.2. The Frequency of Compounding 18 6. The Present Value of a Series of Cash Flows 20 6.1. The Present Value of a Series of Equal Cash Flows 20 6.2. The Present Value of an Infinite Series of Equal Cash Flows—Perpetuity 24 6.3. Present Values Indexed at Times Other than t = 0 25 6.4. The Present Value of a Series of Unequal Cash Flows 27 7. Solving for Rates, Number of Periods, or Size of Annuity Payments 27 7.1. Solving for Interest Rates and Growth Rates 28 7.2. Solving for the Number of Periods 30 7.3. Solving for the Size of Annuity Payments 31 7.4. Review of Present and Future Value Equivalence 35 7.5. The Cash Flow Additivity Principle 37 8. Summary 38 Practice Problems 39 Chapter 2 Organizing, Visualizing, and Describing Data 45 Learning Outcomes 45 1. Introduction 45 2. Data Types 46 2.1. Numerical versus Categorical Data 46 2.2. Cross-Sectional versus Time-Series versus Panel Data 49 2.3. Structured versus Unstructured Data 50 3. Data Summarization 54 3.1. Organizing Data for Quantitative Analysis 54 3.2. Summarizing Data Using Frequency Distributions 57 3.3. Summarizing Data Using a Contingency Table 63 4. Data Visualization 68 4.1. Histogram and Frequency Polygon 68 4.2. Bar Chart 69 4.3. Tree-Map 73 4.4. Word Cloud 73 4.5. Line Chart 75 4.6. Scatter Plot 77 4.7. Heat Map 81 4.8. Guide to Selecting among Visualization Types 82 5. Measures of Central Tendency 85 5.1. The Arithmetic Mean 85 5.2. The Median 90 5.3. The Mode 92 5.4. Other Concepts of Mean 92 6. Other Measures of Location: Quantiles 102 6.1. Quartiles, Quintiles, Deciles, and Percentiles 103 6.2. Quantiles in Investment Practice 108 7. Measures of Dispersion 109 7.1. The Range 109 7.2. The Mean Absolute Deviation 109 7.3. Sample Variance and Sample Standard Deviation 111 7.4. Target Downside Deviation 114 7.5. Coefficient of Variation 117 8. The Shape of the Distributions: Skewness 119 9. The Shape of the Distributions: Kurtosis 121 10. Correlation between Two Variables 125 10.1. Properties of Correlation 126 10.2. Limitations of Correlation Analysis 129 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 4.1. Bayes’ Formula 180 4.2. Principles of Counting 184 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 2.1. The Discrete Uniform Distribution 198 2.2. The Binomial Distribution 200 3. Continuous Random Variables 210 3.1. Continuous Uniform Distribution 210 3.2. The Normal Distribution 214 3.3. Applications of the Normal Distribution 220 3.4. The Lognormal Distribution 222 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 2.1. Simple Random Sampling 242 2.2. Stratified Random Sampling 244 2.3. Time-Series and Cross-Sectional Data 245 3. Distribution of the Sample Mean 248 3.1. The Central Limit Theorem 248 4. Point and Interval Estimates of the Population Mean 251 4.1. Point Estimators 252 4.2. Confidence Intervals for the Population Mean 253 4.3. Selection of Sample Size 259 5. More on Sampling 261 5.1. Data-Mining Bias 261 5.2. Sample Selection Bias 264 5.3. Look-Ahead Bias 265 5.4. Time-Period Bias 266 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 3.1. Tests Concerning a Single Mean 287 3.2. Tests Concerning Differences between Means 294 3.3. Tests Concerning Mean Differences 299 4. Hypothesis Tests Concerning Variance and Correlation 303 4.1. Tests Concerning a Single Variance 303 4.2. Tests Concerning the Equality (Inequality) of Two Variances 305 4.3. Tests Concerning Correlation 308 5. Other Issues: Nonparametric Inference 310 5.1. Nonparametric Tests Concerning Correlation: The Spearman Rank Correlation Coefficient 312 5.2. Nonparametric Inference: Summary 313 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 2.1. Linear Regression with One Independent Variable 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 2.1. Assumptions of the Multiple Linear Regression Model 372 2.2. Predicting the Dependent Variable in a Multiple Regression Model 376 2.3. Testing Whether All Population Regression Coefficients Equal Zero 378 2.4. Adjusted R2 380 3. Using Dummy Variables in Regressions 381 3.1. Defining a Dummy Variable 381 3.2. Visualizing and Interpreting Dummy Variables 382 3.3. Testing for Statistical Significance 384 4. Violations of Regression Assumptions 387 4.1. Heteroskedasticity 388 4.2. Serial Correlation 394 4.3. Multicollinearity 398 4.4. Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues 401 5. Model Specification and Errors in Specification 401 5.1. Principles of Model Specification 402 5.2. Misspecified Functional Form 402 5.3. Time-Series Misspecification (Independent Variables Correlated with Errors) 410 5.4. Other Types of Time-Series Misspecification 414 6. Models with Qualitative Dependent Variables 414 6.1. 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 3.1. Linear Trend Models 455 3.2. Log-Linear Trend Models 458 3.3. Trend Models and Testing for Correlated Errors 463 4. Autoregressive (AR) Time-Series Models 464 4.1. Covariance-Stationary Series 465 4.2. Detecting Serially Correlated Errors in an Autoregressive Model 466 4.3. Mean Reversion 469 4.4. Multiperiod Forecasts and the Chain Rule of Forecasting 470 4.5. Comparing Forecast Model Performance 473 4.6. Instability of Regression Coefficients 475 5. Random Walks and Unit Roots 478 5.1. Random Walks 478 5.2. The Unit Root Test of Nonstationarity 482 6. Moving-Average Time-Series Models 486 6.1. Smoothing Past Values with an n-Period Moving Average 486 6.2. Moving-Average Time-Series Models for Forecasting 489 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 3.1. Defining Machine Learning 529 3.2. Supervised Learning 529 3.3. Unsupervised Learning 531 3.4. Deep Learning and Reinforcement Learning 531 3.5. Summary of ML Algorithms and How to Choose among Them 532 4. Overview of Evaluating ML Algorithm Performance 533 4.1. Generalization and Overfitting 534 4.2. Errors and Overfitting 534 4.3. Preventing Overfitting in Supervised Machine Learning 537 5. Supervised Machine Learning Algorithms 539 5.1. Penalized Regression 539 5.2. Support Vector Machine 541 5.3. K-Nearest Neighbor 542 5.4. Classification and Regression Tree 544 5.5. Ensemble Learning and Random Forest 547 6. Unsupervised Machine Learning Algorithms 559 6.1. Principal Components Analysis 560 6.2. Clustering 563 7. Neural Networks, Deep Learning Nets, and Reinforcement Learning 575 7.1. Neural Networks 575 7.2. Deep Learning Neural Networks 578 7.3. Reinforcement Learning 579 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 4.1. Structured Data 604 4.2. Unstructured (Text) Data 610 5. Data Exploration Objectives and Methods 617 5.1. Structured Data 618 5.2. Unstructured Data: Text Exploration 622 6. Model Training 629 6.1. Structured and Unstructured Data 630 7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks 639 7.1. Text Curation, Preparation, and Wrangling 640 7.2. Data Exploration 644 7.3. Model Training 654 7.4. Results and Interpretation 658 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 4.1. Factors and Types of Multifactor Models 683 4.2. The Structure of Macroeconomic Factor Models 684 4.3. The Structure of Fundamental Factor Models 687 4.4. Fixed-Income Multifactor Models 691 5. Multifactor Models: Selected Applications 695 5.1. Factor Models in Return Attribution 696 5.2. Factor Models in Risk Attribution 698 5.3. Factor Models in Portfolio Construction 703 5.4. How Factor Considerations Can Be Useful in Strategic Portfolio Decisions 705 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 2.1. Value at Risk: Formal Definition 715 2.2. Estimating VaR 718 2.3. Advantages and Limitations of VaR 730 2.4. Extensions of VaR 733 3. Other Key Risk Measures—Sensitivity and Scenario Measures 735 3.1. Sensitivity Risk Measures 736 3.2. Scenario Risk Measures 740 3.3. Sensitivity and Scenario Risk Measures and VaR 746 4. Using Constraints in Market Risk Management 750 4.1. Risk Budgeting 751 4.2. Position Limits 752 4.3. Scenario Limits 752 4.4. Stop-Loss Limits 753 4.5. Risk Measures and Capital Allocation 753 5. Applications of Risk Measures 755 5.1. Market Participants and the Different Risk Measures They Use 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 3.1. Strategy Design 777 3.2. Rolling Window Backtesting 778 3.3. Key Parameters in Backtesting 779 3.4. Long/Short Hedged Portfolio Approach 781 3.5. Pearson and Spearman Rank IC 785 3.6. Univariate Regression 789 3.7. Do Different Backtesting Methodologies Tell the Same Story? 789 4. Metrics and Visuals Used in Backtesting 792 4.1. Coverage 792 4.2. Distribution 794 4.3. Performance Decay, Structural Breaks, and Downside Risk 797 4.4. Factor Turnover and Decay 797 5. Common Problems in Backtesting 801 5.1. Survivorship Bias 801 5.2. Look-Ahead Bias 804 6. Backtesting Factor Allocation Strategies 807 6.1. Setting the Scene 808 6.2. Backtesting the Benchmark and Risk Parity Strategies 808 7. Comparing Methods of Modeling Randomness 813 7.1. Factor Portfolios and BM and RP Allocation Strategies 814 7.2. Factor Return Statistical Properties 815 7.3. Performance Measurement and Downside Risk 819 7.4. Methods to Account for Randomness 821 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.