Quantitative Investment Analysis (CFA Institute Investment Series)

Cover
Quantitative Investment Analysis
Title page
Copyright page
Contents
Preface
Acknowledgments
About the CFA Institute Investment Series
Chapter 1: The Time Value of Money
	Learning Outcomes
	1. Introduction
	2. Interest Rates: Interpretation
	3. The Future Value of a Single Cash Flow
		3.1. The Frequency of Compounding
		3.2. Continuous Compounding
		3.3. Stated and Effective Rates
	4. The Future Value of a Series of Cash Flows
		4.1. Equal Cash Flows—Ordinary Annuity
		4.2. Unequal Cash Flows
	5. The Present Value of a Single Cash Flow
		5.1. Finding the Present Value of a Single Cash Flow
		5.2. The Frequency of Compounding
	6. The Present Value of a Series of Cash Flows
		6.1. The Present Value of a Series of Equal Cash Flows
		6.2. The Present Value of an Infinite Series of Equal Cash Flows—Perpetuity
		6.3. Present Values Indexed at Times Other than t = 0
		6.4. The Present Value of a Series of Unequal Cash Flows
	7. Solving for Rates, Number of Periods, or Size of Annuity Payments
		7.1. Solving for Interest Rates and Growth Rates
		7.2. Solving for the Number of Periods
		7.3. Solving for the Size of Annuity Payments
		7.4. Review of Present and Future Value Equivalence
		7.5. The Cash Flow Additivity Principle
	8. Summary
	Practice Problems
Chapter 2: Organizing, Visualizing, and Describing Data
	Learning Outcomes
	1. Introduction
	2. Data Types
		2.1. Numerical versus Categorical Data
		2.2. Cross-Sectional versus Time-Series versus Panel Data
		2.3. Structured versus Unstructured Data
	3. Data Summarization
		3.1. Organizing Data for Quantitative Analysis
		3.2. Summarizing Data Using Frequency Distributions
		3.3. Summarizing Data Using a Contingency Table
	4. Data Visualization
		4.1. Histogram and Frequency Polygon
		4.2. Bar Chart
		4.3. Tree-Map
		4.4. Word Cloud
		4.5. Line Chart
		4.6. Scatter Plot
		4.7. Heat Map
		4.8. Guide to Selecting among Visualization Types
	5. Measures of Central Tendency
		5.1. The Arithmetic Mean
		5.2. The Median
		5.3. The Mode
		5.4. Other Concepts of Mean
	6. Other Measures of Location: Quantiles
		6.1. Quartiles, Quintiles, Deciles, and Percentiles
		6.2. Quantiles in Investment Practice
	7. Measures of Dispersion
		7.1. The Range
		7.2. The Mean Absolute Deviation
		7.3. Sample Variance and Sample Standard Deviation
		7.4. Target Downside Deviation
		7.5. Coefficient of Variation
	8. The Shape of the Distributions: Skewness
	9. The Shape of the Distributions: Kurtosis
	10. Correlation between Two Variables
		10.1. Properties of Correlation
		10.2. Limitations of Correlation Analysis
	11. Summary
	Practice Problems
Chapter 3: Probability Concepts
	Learning Outcomes
	1. Introduction
	2. Probability, Expected Value, and Variance
	3. Portfolio Expected Return and Variance of Return
	4. Topics in Probability
		4.1. Bayes’ Formula
		4.2. Principles of Counting
	5. Summary
	References
	Practice Problem
Chapter 4: Common Probability Distributions
	Learning Outcomes
	1. Introduction to Common Probability Distributions
	2. Discrete Random Variables
		2.1. The Discrete Uniform Distribution
		2.2. The Binomial Distribution
	3. Continuous Random Variables
		3.1. Continuous Uniform Distribution
		3.2. The Normal Distribution
		3.3. Applications of the Normal Distribution
		3.4. The Lognormal Distribution
	4. Introduction to Monte Carlo Simulation
	5. Summary
	References
	Practice Problems
Chapter 5: Sampling and Estimation
	Learning Outcomes
	1. Introduction
	2. Sampling
		2.1. Simple Random Sampling
		2.2. Stratified Random Sampling
		2.3. Time-Series and Cross-Sectional Data
	3. Distribution of the Sample Mean
		3.1. The Central Limit Theorem
	4. Point and Interval Estimates of the Population Mean
		4.1. Point Estimators
		4.2. Confidence Intervals for the Population Mean
		4.3. Selection of Sample Size
	5. More on Sampling
		5.1. Data-Mining Bias
		5.2. Sample Selection Bias
		5.3. Look-Ahead Bias
		5.4. Time-Period Bias
	6. Summary
	References
	Practice Problems
Chapter 6: Hypothesis Testing
	Learning Outcomes
	1. Introduction
	2. Hypothesis Testing
	3. Hypothesis Tests Concerning the Mean
		3.1. Tests Concerning a Single Mean
		3.2. Tests Concerning Differences between Means
		3.3. Tests Concerning Mean Differences
	4. Hypothesis Tests Concerning Variance and Correlation
		4.1. Tests Concerning a Single Variance
		4.2. Tests Concerning the Equality (Inequality) of Two Variances
		4.3. Tests Concerning Correlation
	5. Other Issues: Nonparametric Inference
		5.1. Nonparametric Tests Concerning Correlation: The Spearman Rank Correlation Coefficient
		5.2. Nonparametric Inference: Summary
	6. Summary
	References
	Practice Problems
Chapter 7: Introduction to Linear Regression
	Learning Outcomes
	1. Introduction
	2. Linear Regression
		2.1. Linear Regression with One Independent Variable
	3. Assumptions of the Linear Regression Model
	4. The Standard Error of Estimate
	5. The Coefficient of Determination
	6. Hypothesis Testing
	7. Analysis of Variance in a Regression with One Independent Variable
	8. Prediction Intervals
	9. Summary
	References
	Practice Problems
Chapter 8: Multiple Regression
	Learning Outcomes
	1. Introduction
	2. Multiple Linear Regression
		2.1. Assumptions of the Multiple Linear Regression Model
		2.2. Predicting the Dependent Variable in a Multiple Regression Model
		2.3. Testing Whether All Population Regression Coefficients Equal Zero
		2.4. Adjusted R2
	3. Using Dummy Variables in Regressions
		3.1. Defining a Dummy Variable
		3.2. Visualizing and Interpreting Dummy Variables
		3.3. Testing for Statistical Significance
	4. Violations of Regression Assumptions
		4.1. Heteroskedasticity
		4.2. Serial Correlation
		4.3. Multicollinearity
		4.4. Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues
	5. Model Specification and Errors in Specification
		5.1. Principles of Model Specification
		5.2. Misspecified Functional Form
		5.3. Time-Series Misspecification (Independent Variables Correlated with Errors
		5.4. Other Types of Time-Series Misspecification
	6. Models with Qualitative Dependent Variables
		6.1. Models with Qualitative Dependent Variables
	7. Summary
	References
	Practice Problems
Chapter 9: Time-Series Analysis
	Learning Outcomes
	1. Introduction to Time-Series Analysis
	2. Challenges of Working with Time Series
	3. Trend Models
		3.1. Linear Trend Models
		3.2. Log-Linear Trend Models
		3.3. Trend Models and Testing for Correlated Errors
	4. Autoregressive (AR) Time-Series Models
		4.1. Covariance-Stationary Series
		4.2. Detecting Serially Correlated Errors in an Autoregressive Model
		4.3. Mean Reversion
		4.4. Multiperiod Forecasts and the Chain Rule of Forecasting
		4.5. Comparing Forecast Model Performance
		4.6. Instability of Regression Coefficients
	5. Random Walks and Unit Roots
		5.1. Random Walks
		5.2. The Unit Root Test of Nonstationarity
	6. Moving-Average Time-Series Models
		6.1. Smoothing Past Values with an n-Period Moving Average
		6.2. Moving-Average Time-Series Models for Forecasting
	7. Seasonality in Time-Series Models
	8. Autoregressive Moving-Average Models
	9. Autoregressive Conditional Heteroskedasticity Models
	10. Regressions with More than One Time Series
	11. Other Issues in Time Series
	12. Suggested Steps in Time-Series Forecasting
	13. Summary
	References
	Practice Problems
Chapter 10: Machine Learning
	Learning Outcomes
	1. Introduction
	2. Machine Learning and Investment Management
	3. What is Machine Learning?
		3.1. Defining Machine Learning
		3.2. Supervised Learning
		3.3. Unsupervised Learning
		3.4. Deep Learning and Reinforcement Learning
		3.5. Summary of ML Algorithms and How to Choose among Them
	4. Overview of Evaluating ML Algorithm Performance
		4.1. Generalization and Overfitting
		4.2. Errors and Overfitting
		4.3. Preventing Overfitting in Supervised Machine Learning
	5. Supervised Machine Learning Algorithms
		5.1. Penalized Regression
		5.2. Support Vector Machine
		5.3. K-Nearest Neighbor
		5.4. Classification and Regression Tree
		5.5. Ensemble Learning and Random Forest
	6. Unsupervised Machine Learning Algorithms
		6.1. Principal Components Analysis
		6.2. Clustering
	7. Neural Networks, Deep Learning Nets, and Reinforcement Learning
		7.1. Neural Networks
		7.2. Deep Learning Neural Networks
		7.3. Reinforcement Learning
	8. Choosing an Appropriate ML Algorithm
	9. Summary
	References
	Practice Problems
Chapter 11: Big Data Projects
	Learning Outcomes
	1. Introduction
	2. Big Data in Investment Management
	3. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data
	4. Data Preparation and Wrangling
		4.1. Structured Data
		4.2. Unstructured (Text) Data
	5. Data Exploration Objectives and Methods
		5.1. Structured Data
		5.2. Unstructured Data: Text Exploration
	6. Model Training
		6.1. Structured and Unstructured Data
	7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks
		7.1. Text Curation, Preparation, and Wrangling
		7.2. Data Exploration
		7.3. Model Training
		7.4. Results and Interpretation
	8. Summary
	Practice Problems
Chapter 12: Using Multifactor Models
	Learning Outcomes
	1. Introduction
	2. Multifactor Models and Modern Portfolio Theory
	3. Arbitrage Pricing Theory
	4. Multifactor Models: Types
		4.1. Factors and Types of Multifactor Models
		4.2. The Structure of Macroeconomic Factor Models
		4.3. The Structure of Fundamental Factor Models
		4.4. Fixed-Income Multifactor Models
	5. Multifactor Models: Selected Applications
		5.1. Factor Models in Return Attribution
		5.2. Factor Models in Risk Attribution
		5.3. Factor Models in Portfolio Construction
		5.4. How Factor Considerations Can Be Useful in Strategic Portfolio Decisions
	6. Summary
	References
	Practice Problems
	CHAPTER
Chapter 13: Measuring and Managing Market Risk
	Learning Outcomes
	1. Introduction
	2. Understanding Value at Risk
		2.1. Value at Risk: Formal Definition
		2.2. Estimating VaR
		2.3. Advantages and Limitations of VaR
		2.4. Extensions of VaR
	3. Other Key Risk Measures—Sensitivity and Scenario Measures
		3.1. Sensitivity Risk Measures
		3.2. Scenario Risk Measures
		3.3. Sensitivity and Scenario Risk Measures and VaR
	4. Using Constraints in Market Risk Management
		4.1. Risk Budgeting
		4.2. Position Limits
		4.3. Scenario Limits
		4.4. Stop-Loss Limits
		4.5. Risk Measures and Capital Allocation
	5. Applications of Risk Measures
		5.1. Market Participants and the Different Risk Measures They Use
	6. Summary
	References
	Practice Problems
Chapter 14: Backtesting and Simulation
	Learning Outcomes
	1. Introduction
	2. The Objectives of Backtesting
	3. The Backtesting Process
		3.1. Strategy Design
		3.2. Rolling Window Backtesting
		3.3. Key Parameters in Backtesting
		3.4. Long/Short Hedged Portfolio Approach
		3.5. Pearson and Spearman Rank IC
		3.6. Univariate Regression
		3.7. Do Different Backtesting Methodologies Tell the Same Story
	4. Metrics and Visuals Used in Backtesting
		4.1. Coverage
		4.2. Distribution
		4.3. Performance Decay, Structural Breaks, and Downside Risk
		4.4. Factor Turnover and Decay
	5. Common Problems in Backtesting
		5.1. Survivorship Bias
		5.2. Look-Ahead Bias
	6. Backtesting Factor Allocation Strategies
		6.1. Setting the Scene
		6.2. Backtesting the Benchmark and Risk Parity Strategies
	7. Comparing Methods of Modeling Randomness
		7.1. Factor Portfolios and BM and RP Allocation Strategies
		7.2. Factor Return Statistical Properties
		7.3. Performance Measurement and Downside Risk
		7.4. Methods to Account for Randomness
	8. Scenario Analysis
	9. Historical Simulation versus Monte Carlo Simulation
	10. Historical Simulation
	11. Monte Carlo Simulation
	12. Sensitivity Analysis
	13. Summary
	References
	Practice Problems
Appendices
Glossary
About the Authors
About the CFA Program
Index
EULA