Quantitative Investment Analysis

CFA Institute is the global association of investment professionals that sets the standard for professional excellence and credentials. The organization is a champion for ethical behavior in investment markets and a respected source of knowledge in the global financial community. The end goal: to create an environment where investors’ interests come first, markets function at their best, and economies grow. CFA Institute has more than 155,000 members in 165 countries and territories, including 150,000 CFA® charterholders, and 148 member societies. For more information, visit www.cfainstitute.org.

• Preface xvAcknowledgments xviiAbout the CFA Institute Investment Series xixChapter 1 The Time Value of Money 1Learning Outcomes 11. Introduction 12. Interest Rates: Interpretation 23. The Future Value of a Single Cash Flow 44. The Future Value of a Series of Cash Flows 135. The Present Value of a Single Cash Flow 166. The Present Value of a Series of Cash Flows 207. Solving for Rates, Number of Periods, or Size of Annuity Payments 278. Summary 38Practice Problems 39Chapter 2 Organizing, Visualizing, and Describing Data 45Learning Outcomes 451. Introduction 452. Data Types 463. Data Summarization 544. Data Visualization 685. Measures of Central Tendency 856. Other Measures of Location: Quantiles 1027. Measures of Dispersion 1098. The Shape of the Distributions: Skewness 1199. The Shape of the Distributions: Kurtosis 12110. Correlation between Two Variables 12511. Summary 132Practice Problems 135Chapter 3 Probability Concepts 147Learning Outcomes 1471. Introduction 1482. Probability, Expected Value, and Variance 1483. Portfolio Expected Return and Variance of Return 1714. Topics in Probability 1805. Summary 188References 190Practice Problem 190Chapter 4 Common Probability Distributions 195Learning Outcomes 1951. Introduction to Common Probability Distributions 1962. Discrete Random Variables 1963. Continuous Random Variables 2104. Introduction to Monte Carlo Simulation 2285. Summary 231References 233Practice Problems 234Chapter 5 Sampling and Estimation 241Learning Outcomes 2411. Introduction 2422. Sampling 2423. Distribution of the Sample Mean 2484. Point and Interval Estimates of the Population Mean 2515. More on Sampling 2616. Summary 267References 269Practice Problems 270Chapter 6 Hypothesis Testing 275Learning Outcomes 2751. Introduction 2762. Hypothesis Testing 2773. Hypothesis Tests Concerning the Mean 2874. Hypothesis Tests Concerning Variance and Correlation 3035. Other Issues: Nonparametric Inference 3106. Summary 314References 317Practice Problems 317Chapter 7 Introduction to Linear Regression 327Learning Outcomes 3271. Introduction 3282. Linear Regression 3283. Assumptions of the Linear Regression Model 3324. The Standard Error of Estimate 3355. The Coefficient of Determination 3376. Hypothesis Testing 3397. Analysis of Variance in a Regression with One Independent Variable 3478. Prediction Intervals 3509. Summary 353References 354Practice Problems 354Chapter 8 Multiple Regression 365Learning Outcomes 3651. Introduction 3662. Multiple Linear Regression 3663. Using Dummy Variables in Regressions 3814. Violations of Regression Assumptions 3875. Model Specification and Errors in Specification 4016. Models with Qualitative Dependent Variables 4147. Summary 422References 425Practice Problems 426Chapter 9 Time-Series Analysis 451Learning Outcomes 4511. Introduction to Time-Series Analysis 4522. Challenges of Working with Time Series 4543. Trend Models 4544. Autoregressive (AR) Time-Series Models 4645. Random Walks and Unit Roots 4786. Moving-Average Time-Series Models 4867. Seasonality in Time-Series Models 4918. Autoregressive Moving-Average Models 4969. Autoregressive Conditional Heteroskedasticity Models 49710. Regressions with More than One Time Series 50011. Other Issues in Time Series 50412. Suggested Steps in Time-Series Forecasting 50513. Summary 507References 508Practice Problems 509Chapter 10 Machine Learning 527Learning Outcomes 5271. Introduction 5272. Machine Learning and Investment Management 5283. What is Machine Learning? 5294. Overview of Evaluating ML Algorithm Performance 5335. Supervised Machine Learning Algorithms 5396. Unsupervised Machine Learning Algorithms 5597. Neural Networks, Deep Learning Nets, and Reinforcement Learning 5758. Choosing an Appropriate ML Algorithm 5899. Summary 590References 593Practice Problems 593Chapter 11 Big Data Projects 597Learning Outcomes 5971. Introduction 5972. Big Data in Investment Management 5983. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data 5994. Data Preparation and Wrangling 6035. Data Exploration Objectives and Methods 6176. Model Training 6297. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks 6398. Summary 664Practice Problems 665Chapter 12 Using Multifactor Models 675Learning Outcomes 6751. Introduction 6752. Multifactor Models and Modern Portfolio Theory 6763. Arbitrage Pricing Theory 6774. Multifactor Models: Types 6835. Multifactor Models: Selected Applications 6956. Summary 706References 707Practice Problems 708Chapter 13 Measuring and Managing Market Risk 713Learning Outcomes 7131. Introduction 7142. Understanding Value at Risk 7143. Other Key Risk Measures—Sensitivity and Scenario Measures 7354. Using Constraints in Market Risk Management 7505. Applications of Risk Measures 7556. Summary 764References 766Practice Problems 766Chapter 14 Backtesting and Simulation 775Learning Outcomes 7751. Introduction 7752. The Objectives of Backtesting 7763. The Backtesting Process 7764. Metrics and Visuals Used in Backtesting 7925. Common Problems in Backtesting 8016. Backtesting Factor Allocation Strategies 8077. Comparing Methods of Modeling Randomness 8138. Scenario Analysis 8249. Historical Simulation versus Monte Carlo Simulation 82810. Historical Simulation 83011. Monte Carlo Simulation 83512. Sensitivity Analysis 84013. Summary 848References 849Practice Problems 849Appendices 855Glossary 865About the Authors 883About the CFA Program 885Index 887