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Quantitative Investment Analysis
CFA Institute (Author)
9781119743620, Wiley
Hardback, published 26 November 2020
944 pages
26.1 x 18.8 x 5.5 cm, 1.554 kg
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: 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.
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
Subject Areas: Finance & accounting [KF]
