As part of the CFA Institute Investment Series, the Second Edition of Quantitative Investment Analysis has been designed for a wide range of individuals, from graduate-level students focused on finance to practicing investment professionals. This globally relevant guide will help you understand quantitative methods and apply them to today's investment process.
In this latest edition, the distinguished team of Richard DeFusco, Dennis McLeavey, Jerald Pinto, and David Runkle update information associated with this discipline; improve the presentation and coverage of several major areas, including regression, time series, and multifactor models; and introduce an even greater variety of investment-oriented examples—which reflect the changes currently taking place in the investment community. Throughout the text, special attention is paid to ensuring the even treatment of subject matter, consistency of mathematical notation, and continuity of topic coverage that is so critical to the learning process.
Valuable for self-study and general reference, this book provides clear, example-driven coverage of a wide range of quantitative methods. Topics discussed include:
- The time value of money
- Discounted cash flow applications
- Common probability distributions
- Sampling and estimation
- Hypothesis testing
- Correlation and regression
- Multiple regression and issues in regression analysis
- Time-series analysis
- Portfolio concepts
And to further enhance your understanding of the tools and techniques presented here,don't forget to pick up the Quantitative Investment Analysis Workbook, Second Edition—an essential guide containing learning outcomes and summary overview sections along with challenging problems and solutions.
With each author bringing his own unique experiences and perspectives to the table, the Second Edition of Quantitative Investment Analysis distills the knowledge, skills, and abilities you need to succeed in today's fast-paced financial environment. Filled with in-depth insights and practical advice, Quantitative Investment Analysis, Second Edition offers a comprehensive treatment of quantitative methods that combines best practices with solid theory.
Table of Contents
Foreword xiii
Acknowledgments xvii
Introduction xix
The Time Value of Money 1
Introduction 1
Interest Rates: Interpretation 1
The Future Value of a Single Cash Flow 3
The Frequency of Compounding 8
Continuous Compounding 10
Stated and Effective Rates 12
The Future Value of a Series of Cash Flows 13
Equal Cash Flows-Ordinary Annuity 13
Unequal Cash Flows 15
The Present Value of a Single Cash Flow 15
Finding the Present Value of a Single Cash Flow 15
The Frequency of Compounding 17
The Present Value of a Series of Cash Flows 19
The Present Value of a Series of Equal Cash Flows 19
The Present Value of an Infinite Series of Equal Cash Flows-Perpetuity 23
Present Values Indexed at Times Other Than t = 0 24
The Present Value of a Series of Unequal Cash Flows 26
Solving for Rates, Number of Periods, or Size of Annuity Payments 27
Solving for Interest Rates and Growth Rates 27
Solving for the Number of Periods 30
Solving for the Size ofAnnuity Payments 30
Review of Present and Future Value Equivalence 35
The Cash Flow Additivity Principle 36
Discounted Cash Flow Applications 39
Introduction 39
Net Present Value and Internal Rate of Return 39
Net Present Value and the Net Present Value Rule 40
The Internal Rate of Return and the Internal Rate of Return Rule 42
Problems with the IRR Rule 45
Portfolio Return Measurement 47
Money-Weighted Rate of Return 47
Time-Weighted Rate of Return 49
Money Market Yields 54
Statistical Concepts and Market Returns 61
Introduction 61
Some Fundamental Concepts 61
The Nature of Statistics 62
Populations and Samples 62
Measurement Scales 63
Summarizing Data Using Frequency Distributions 65
The Graphic Presentation of Data 72
The Histogram 73
The Frequency Polygon and the Cumulative Frequency Distribution 74
Measures of Central Tendency 76
The Arithmetic Mean 77
The Median 81
The Mode 84
Other Concepts of Mean 85
Other Measures of Location: Quantiles 94
Quartiles, Quintiles, Deciles, and Percentiles 94
Quantiles in Investment Practice 98
Measures of Dispersion 100
The Range 100
The Mean Absolute Deviation 101
Population Variance and Population Standard Deviation 103
Sample Variance and Sample Standard Deviation 106
Semivariance, Semideviation, and Related Concepts 110
Chebyshev's Inequality 111
Coefficient of Variation 113
The Sharpe Ratio 115
Symmetry and Skewness in Return Distributions 118
Kurtosis in Return Distributions 123
Using Geometric and Arithmetic Means 127
Probability Concepts 129
Introduction 129
Probability, Expected Value, and Variance 129
Portfolio Expected Return and Variance of Return 152
Topics in Probability 161
Bayes' Formula 161
Principles of Counting 166
Common Probability Distributions 171
Introduction 171
Discrete Random Variables 171
The Discrete Uniform Distribution 173
The Binomial Distribution 175
Continuous Random Variables 185
Continuous Uniform Distribution 186
The Normal Distribution 189
Applications of the Normal Distribution 197
The Lognormal Distribution 200
Monte Carlo Simulation 206
Sampling and Estimation 215
Introduction 215
Sampling 215
Simple Random Sampling 216
Stratified Random Sampling 217
Time-Series and Cross-Sectional Data 219
Distribution of the Sample Mean 221
The Central Limit Theorem 222
Point and Interval Estimates of the Population Mean 225
Point Estimators 225
Confidence Intervals for the Population Mean 227
Selection of Sample Size 233
More on Sampling 235
Data-Mining Bias 236
Sample Selection Bias 238
Look-Ahead Bias 240
Time-Period Bias 240
Hypothesis Testing 243
Introduction 243
Hypothesis Testing 244
Hypothesis Tests Concerning the Mean 253
Tests Concerning a Single Mean 254
Tests Concerning Differences between Means 261
Tests Concerning Mean Differences 265
Hypothesis Tests Concerning Variance 269
Tests Concerning a Single Variance 269
Tests Concerning the Equality (Inequality) of Two Variances 271
Other Issues: Nonparametric Inference 275
Tests Concerning Correlation: The Spearman Rank Correlation Coefficient 276
Nonparametric Inference: Summary 279
Correlation and Regression 281
Introduction 281
Correlation Analysis 281
Scatter Plots 281
Correlation Analysis 282
Calculating and Interpreting the Correlation Coefficient 283
Limitations of Correlation Analysis 287
Uses of Correlation Analysis 289
Testing the Significance of the Correlation Coefficient 297
Linear Regression 300
Linear Regression with One Independent Variable 300
Assumptions of the Linear Regression Model 303
The Standard Error of Estimate 306
The Coefficient of Determination 309
Hypothesis Testing 310
Analysis of Variance in a Regression with One Independent Variable 318
Prediction Intervals 321
Limitations of Regression Analysis 324
Multiple Regression and Issues in Regression Analysis 325
Introduction 325
Multiple Linear Regression 325
Assumptions of the Multiple Linear Regression Model 331
Predicting the Dependent Variable in a Multiple Regression Model 336
Testing Whether All Population Regression Coefficients Equal Zero 338
Adjusted R[superscript 2] 340
Using Dummy Variables in Regressions 341
Violations of Regression Assumptions 345
Heteroskedasticity 345
Serial Correlation 351
Multicollinearity 356
Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues 359
Model Specification and Errors in Specification 359
Principles of Model Specification 359
Misspecified Functional Form 360
Time-Series Misspecification (Independent Variables Correlated with Errors) 368
Other Types of Time-Series Misspecification 372
Models with Qualitative Dependent Variables 372
Time-Series Analysis 375
Introduction 375
Challenges of Working with Time Series 375
Trend Models 377
Linear Trend Models 377
Log-Linear Trend Models 380
Trend Models and Testing for Correlated Errors 385
Autoregressive (AR) Time-Series Models 386
Covariance-Stationary Series 386
Detecting Serially Correlated Errors in an Autoregressive Model 387
Mean Reversion 391
Multiperiod Forecasts and the Chain Rule of Forecasting 391
Comparing Forecast Model Performance 394
Instability of Regression Coefficients 397
Random Walks and Unit Roots 399
Random Walks 400
The Unit Root Test of Nonstationarity 403
Moving-Average Time-Series Models 407
Smoothing Past Values with an n-Period Moving Average 407
Moving-Average Time-Series Models for Forecasting 409
Seasonality in Time-Series Models 412
Autoregressive Moving-Average Models 416
Autoregressive Conditional Heteroskedasticity Models 417
Regressions with More than One Time Series 420
Other Issues in Time Series 424
Suggested Steps in Time-Series Forecasting 425
Portfolio Concepts 429
Introduction 429
Mean-Variance Analysis 429
The Minimum-Variance Frontier and Related Concepts 430
Extension to the Three-Asset Case 439
Determining the Minimum-Variance Frontier for Many Assets 442
Diversification and Portfolio Size 445
Portfolio Choice with a Risk-Free Asset 449
The Capital Asset Pricing Model 458
Mean-Variance Portfolio Choice Rules: An Introduction 460
Practical Issues in Mean-Variance Analysis 464
Estimating Inputs for Mean-Variance Optimization 464
Instability in the Minimum-Variance Frontier 470
Multifactor Models 473
Factors and Types of Multifactor Models 474
The Structure of Macroeconomic Factor Models 475
Arbitrage Pricing Theory and the Factor Model 478
The Structure of Fundamental Factor Models 484
Multifactor Models in Current Practice 485
Applications 493
Concluding Remarks 509
Appendices 511
References 521
Glossary 527
About the CFA Program 541
About the Authors 543
Index 545