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E-raamat: Quantitative Investment Analysis

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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 today’s 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 to 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 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 today’s investment world
  • A wealth of practice problems, charts, tables, and graphs to clarify and reinforce the concepts and tools of quantitative investment management

Sharpen your skills by furthering your hands-on experience in the Quantitative Investment Analysis Workbook, 4th Edition—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(44)
Learning Outcomes
1(1)
1 Introduction
1(1)
2 Interest Rates: Interpretation
2(2)
3 The Future Value of a Single Cash Flow
4(9)
3.1 The Frequency of Compounding
9(2)
3.2 Continuous Compounding
11(1)
3.3 Stated and Effective Rates
12(1)
4 The Future Value of a Series of Cash Flows
13(3)
4.1 Equal Cash Flows---Ordinary Annuity
14(1)
4.2 Unequal Cash Flows
15(1)
5 The Present Value of a Single Cash Flow
16(4)
5.1 Finding the Present Value of a Single Cash Flow
16(2)
5.2 The Frequency of Compounding
18(2)
6 The Present Value of a Series of Cash Flows
20(7)
6.1 The Present Value of a Series of Equal Cash Flows
20(4)
6.2 The Present Value of an Infinite Series of Equal Cash Flows---Perpetuity
24(1)
6.3 Present Values Indexed at Times Other than t = 0
25(2)
6.4 The Present Value of a Series of Unequal Cash Flows
27(1)
7 Solving for Rates, Number of Periods, or Size of Annuity Payments
27(11)
7.1 Solving for Interest Rates and Growth Rates
28(2)
7.2 Solving for the Number of Periods
30(1)
7.3 Solving for the Size of Annuity Payments
31(4)
7.4 Review of Present and Future Value Equivalence
35(2)
7.5 The Cash Flow Additivity Principle
37(1)
8 Summary
38(1)
Practice Problems
39(6)
Chapter 2 Organizing, Visualizing, and Describing Data
45(102)
Learning Outcomes
45(1)
1 Introduction
45(1)
2 Data Types
46(8)
2.1 Numerical versus Categorical Data
46(3)
2.2 Cross-Sectional versus Time-Series versus Panel Data
49(1)
2.3 Structured versus Unstructured Data
50(4)
3 Data Summarization
54(14)
3.1 Organizing Data for Quantitative Analysis
54(3)
3.2 Summarizing Data Using Frequency Distributions
57(6)
3.3 Summarizing Data Using a Contingency Table
63(5)
4 Data Visualization
68(17)
4.1 Histogram and Frequency Polygon
68(1)
4.2 Bar Chan
69(4)
4.3 Tree-Map
73(1)
4.4 Word Cloud
73(2)
4.5 Line Chart
75(2)
4.6 Scatter Plot
77(4)
4.7 Heat Map
81(1)
4.8 Guide to Selecting among Visualization Types
82(3)
5 Measures of Central Tendency
85(17)
5.1 The Arithmetic Mean
85(5)
5.2 The Median
90(2)
5.3 The Mode
92(1)
5.4 Other Concepts of Mean
92(10)
6 Other Measures of Location: Quantiles
102(7)
6.1 Quartiles, Quintiles, Deciles, and Percentiles
103(5)
6.2 Quantiles in Investment Practice
108(1)
7 Measures of Dispersion
109(10)
7.1 The Range
109(1)
7.2 The Mean Absolute Deviation
109(2)
7.3 Sample Variance and Sample Standard Deviation
111(3)
7.4 Target Downside Deviation
114(3)
7.5 Coefficient of Variation
117(2)
8 The Shape of the Distributions: Skewness
119(2)
9 The Shape of the Distributions: Kurtosis
121(4)
10 Correlation between Two Variables
125(7)
10.1 Properties of Correlation
126(3)
10.2 Limitations of Correlation Analysis
129(3)
11 Summary
132(3)
Practice Problems
135(12)
Chapter 3 Probability Concepts
147(48)
Learning Outcomes
147(1)
1 Introduction
148(1)
2 Probability, Expected Value, and Variance
148(23)
3 Portfolio Expected Return and Variance of Return
171(9)
4 Topics in Probability
180(8)
4.1 Bayes' Formula
180(4)
4.2 Principles of Counting
184(4)
5 Summary
188(2)
References
190(1)
Practice Problem
190(5)
Chapter 4 Common Probability Distributions
195(46)
Learning Outcomes
195(1)
1 Introduction to Common Probability Distributions
196(1)
2 Discrete Random Variables
196(14)
2.1 The Discrete Uniform Distribution
198(2)
2.2 The Binomial Distribution
200(10)
3 Continuous Random Variables
210(18)
3.1 Continuous Uniform Distribution
210(4)
3.2 The Normal Distribution
214(6)
3.3 Applications of the Normal Distribution
220(2)
3.4 The Lognormal Distribution
222(6)
4 Introduction to Monte Carlo Simulation
228(3)
5 Summary
231(2)
References
233(1)
Practice Problems
234(7)
Chapter 5 Sampling and Estimation
241(34)
Learning Outcomes
241(1)
1 Introduction
242(1)
2 Sampling
242(6)
2.1 Simple Random Sampling
242(2)
2.2 Stratified Random Sampling
244(1)
2.3 Time-Series and Cross-Sectional Data
245(3)
3 Distribution of the Sample Mean
248(3)
3.1 The Central Limit Theorem
248(3)
4 Point and Interval Estimates of the Population Mean
251(10)
4.1 Point Estimators
252(1)
4.2 Confidence Intervals for the Population Mean
253(6)
4.3 Selection of Sample Size
259(2)
5 More on Sampling
261(6)
5.1 Data-Mining Bias
261(3)
5.2 Sample Selection Bias
264(1)
5.3 Look-Ahead Bias
265(1)
5.4 Time-Period Bias
266(1)
6 Summary
267(2)
References
269(1)
Practice Problems
270(5)
Chapter 6 Hypothesis Testing
275(52)
Learning Outcomes
275(1)
1 Introduction
276(1)
2 Hypothesis Testing
277(10)
3 Hypothesis Tests Concerning the Mean
287(16)
3.1 Tests Concerning a Single Mean
287(7)
3.2 Tests Concerning Differences between Means
294(5)
3.3 Tests Concerning Mean Differences
299(4)
4 Hypothesis Tests Concerning Variance and Correlation
303(7)
4.1 Tests Concerning a Single Variance
303(2)
4.2 Tests Concerning the Equality (Inequality) of Two Variances
305(3)
4.3 Tests Concerning Correlation
308(2)
5 Other Issues: Nonparametric Inference
310(4)
5.1 Nonparametric Tests Concerning Correlation: The Spearman Rank Correlation Coefficient
312(1)
5.2 Nonparametric Inference: Summary
313(1)
6 Summary
314(3)
References
317(1)
Practice Problems
317(10)
Chapter 7 Introduction to Linear Regression
327(38)
Learning Outcomes
327(1)
1 Introduction
328(1)
2 Linear Regression
328(4)
2.1 Linear Regression with One Independent Variable
328(4)
3 Assumptions of the Linear Regression Model
332(3)
4 The Standard Error of Estimate
335(2)
5 The Coefficient of Determination
337(2)
6 Hypothesis Testing
339(8)
7 Analysis of Variance in a Regression with One Independent Variable
347(3)
8 Prediction Intervals
350(3)
9 Summary
353(1)
References
354(1)
Practice Problems
354(11)
Chapter 8 Multiple Regression
365(86)
Learning Outcomes
365(1)
1 Introduction
366(1)
2 Multiple Linear Regression
366(15)
2.1 Assumptions of the Multiple Linear Regression Model
372(4)
2.2 Predicting the Dependent Variable in a Multiple Regression Model
376(2)
2.3 Testing Whether All Population Regression Coefficients Equal Zero
378(2)
2.4 Adjusted R2
380(1)
3 Using Dummy Variables in Regressions
381(6)
3.1 Defining a Dummy Variable
381(1)
3.2 Visualizing and Interpreting Dummy Variables
382(2)
3.3 Testing for Statistical Significance
384(3)
4 Violations of Regression Assumptions
387(14)
4.1 Heteroskedasticity
388(6)
4.2 Serial Correlation
394(4)
4.3 Multicollinearity
398(3)
4.4 Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues
401(1)
5 Model Specification and Errors in Specification
401(13)
5.1 Principles of Model Specification
402(1)
5.2 Misspecified Functional Form
402(8)
5.3 Time-Series Misspecification (Independent Variables Correlated with Errors)
410(4)
5.4 Other Types of Time-Series Misspecification
414(1)
6 Models with Qualitative Dependent Variables
414(8)
6.1 Models with Qualitative Dependent Variables
414(8)
7 Summary
422(3)
References
425(1)
Practice Problems
426(25)
Chapter 9 Time-Series Analysis
451(76)
Learning Outcomes
451(1)
1 Introduction to Time-Series Analysis
452(2)
2 Challenges of Working with Time Series
454(1)
3 Trend Models
454(10)
3.1 Linear Trend Models
455(3)
3.2 Log-Linear Trend Models
458(5)
3.3 Trend Models and Testing for Correlated Errors
463(1)
4 Autoregressive (AR) Time-Series Models
464(14)
4.1 Covariance-Stationary Series
465(1)
4.2 Detecting Serially Correlated Errors in an Autoregressive Model
466(3)
4.3 Mean Reversion
469(1)
4.4 Multiperiod Forecasts and the Chain Rule of Forecasting
470(3)
4.5 Comparing Forecast Model Performance
473(2)
4.6 Instability of Regression Coefficients
475(3)
5 Random Walks and Unit Roots
478(8)
5.1 Random Walks
478(4)
5.2 The Unit Root Test of Nonstationarity
482(4)
6 Moving-Average Time-Series Models
486(5)
6.1 Smoothing Past Values with an n-Period Moving Average
486(3)
6.2 Moving-Average Time-Series Models for Forecasting
489(2)
7 Seasonality in Time-Series Models
491(5)
8 Autoregressive Moving-Average Models
496(1)
9 Autoregressive Conditional Heteroskedasticity Models
497(3)
10 Regressions with More than One Time Series
500(4)
11 Other Issues in Time Series
504(1)
12 Suggested Steps in Time-Series Forecasting
505(2)
13 Summary
507(1)
References
508(1)
Practice Problems
509(18)
Chapter 10 Machine Learning
527(70)
Learning Outcomes
527(1)
1 Introduction
527(1)
2 Machine Learning and Investment Management
528(1)
3 What is Machine Learning?
529(4)
3.1 Defining Machine Learning
529(1)
3.2 Supervised Learning
529(2)
3.3 Unsupervised Learning
531(1)
3.4 Deep Learning and Reinforcement Learning
531(1)
3.5 Summary of ML Algorithms and How to Choose among Them
532(1)
4 Overview of Evaluating ML Algorithm Performance
533(6)
4.1 Generalization and Overfitting
534(1)
4.2 Errors and Overfitting
534(3)
4.3 Preventing Overfitting in Supervised Machine Learning
537(2)
5 Supervised Machine Learning Algorithms
539(20)
5.1 Penalized Regression
539(2)
5.2 Support Vector Machine
541(1)
5.3 K-Nearest Neighbor
542(2)
5.4 Classification and Regression Tree
544(3)
5.5 Ensemble Learning and Random Forest
547(12)
6 Unsupervised Machine Learning Algorithms
559(16)
6.1 Principal Components Analysis
560(3)
6.2 Clustering
563(12)
7 Neural Networks, Deep Learning Nets, and Reinforcement Learning
575(14)
7.1 Neural Networks
575(3)
7.2 Deep Learning Neural Networks
578(1)
7.3 Reinforcement Learning
579(10)
8 Choosing an Appropriate ML Algorithm
589(1)
9 Summary
590(3)
References
593(1)
Practice Problems
593(4)
Chapter 11 Big Data Projects
597(78)
Learning Outcomes
597(1)
1 Introduction
597(1)
2 Big Data in Investment Management
598(1)
3 Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data
599(4)
4 Data Preparation and Wrangling
603(14)
4.1 Structured Data
604(6)
4.2 Unstructured (Text) Data
610(7)
5 Data Exploration Objectives and Methods
617(12)
5.1 Structured Data
618(4)
5.2 Unstructured Data: Text Exploration
622(7)
6 Model Training
629(10)
6.1 Structured and Unstructured Data
630(9)
7 Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks
639(25)
7.1 Text Curation, Preparation, and Wrangling
640(4)
7.2 Data Exploration
644(10)
7.3 Model Training
654(4)
7.4 Results and Interpretation
658(6)
8 Summary
664(1)
Practice Problems
665(10)
Chapter 12 Using Multifactor Models
675(38)
Learning Outcomes
675(1)
1 Introduction
675(1)
2 Multifactor Models and Modern Portfolio Theory
676(1)
3 Arbitrage Pricing Theory
677(6)
4 Multifactor Models: Types
683(12)
4.1 Factors and Types of Multifactor Models
683(1)
4.2 The Structure of Macroeconomic Factor Models
684(3)
4.3 The Structure of Fundamental Factor Models
687(4)
4.4 Fixed-Income Multifactor Models
691(4)
5 Multifactor Models: Selected Applications
695(11)
5.1 Factor Models in Return Attribution
696(2)
5.2 Factor Models in Risk Attribution
698(5)
5.3 Factor Models in Portfolio Construction
703(2)
5.4 How Factor Considerations Can Be Useful in Strategic Portfolio Decisions
705(1)
6 Summary
706(1)
References
707(1)
Practice Problems
708(5)
Chapter 13 Measuring and Managing Market Risk
713(62)
Learning Outcomes
713(1)
1 Introduction
714(1)
2 Understanding Value at Risk
714(21)
2.1 Value at Risk: Formal Definition
715(3)
2.2 Estimating VaR
718(12)
2.3 Advantages and Limitations of VaR
730(3)
2.4 Extensions of VaR
733(2)
3 Other Key Risk Measures---Sensitivity and Scenario Measures
735(15)
3.1 Sensitivity Risk Measures
736(4)
3.2 Scenario Risk Measures
740(6)
3.3 Sensitivity and Scenario Risk Measures and VaR
746(4)
4 Using Constraints in Market Risk Management
750(5)
4.1 Risk Budgeting
751(1)
4.2 Position Limits
752(1)
4.3 Scenario Limits
752(1)
4.4 Stop-Loss Limits
753(1)
4.5 Risk Measures and Capital Allocation
753(2)
5 Applications of Risk Measures
755(9)
5.1 Market Participants and the Different Risk Measures They Use
755(9)
6 Summary
764(2)
References
766(1)
Practice Problems
766(9)
Chapter 14 Backtesting and Simulation
775(80)
Learning Outcomes
775(1)
1 Introduction
775(1)
2 The Objectives of Backtesting
776(1)
3 The Backtesting Process
776(16)
3.1 Strategy Design
777(1)
3.2 Rolling Window Backtesting
778(1)
3.3 Key Parameters in Backtesting
779(2)
3.4 Long/Short Hedged Portfolio Approach
781(4)
3.5 Pearson and Spearman Rank IC
785(4)
3.6 Univariate Regression
789(1)
3.7 Do Different Backtesting Methodologies Tell the Same Story?
789(3)
4 Metrics and Visuals Used in Backtesting
792(9)
4.1 Coverage
792(2)
4.2 Distribution
794(3)
4.3 Performance Decay, Structural Breaks, and Downside Risk
797(1)
4.4 Factor Turnover and Decay
797(4)
5 Common Problems in Backtesting
801(6)
5.1 Survivorship Bias
801(3)
5.2 Look-Ahead Bias
804(3)
6 Backtesting Factor Allocation Strategies
807(6)
6.1 Setting the Scene
808(1)
6.2 Backtesting the Benchmark and Risk Parity Strategies
808(5)
7 Comparing Methods of Modeling Randomness
813(11)
7.1 Factor Portfolios and BM and RP Allocation Strategies
814(1)
7.2 Factor Return Statistical Properties
815(4)
7.3 Performance Measurement and Downside Risk
819(2)
7.4 Methods to Account for Randomness
821(3)
8 Scenario Analysis
824(4)
9 Historical Simulation versus Monte Carlo Simulation
828(2)
10 Historical Simulation
830(5)
11 Monte Carlo Simulation
835(5)
12 Sensitivity Analysis
840(8)
13 Summary
848(1)
References
849(1)
Practice Problems
849(6)
Appendices 855(10)
Glossary 865(18)
About the Authors 883(2)
About the CFA Program 885(2)
Index 887
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.
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