Quantitative Investment Analysis

In the Second Edition of Quantitative Investment Analysis, financial experts Richard DeFusco, Dennis McLeavey, Jerald Pinto, and David Runkle outline the tools and techniques needed to understand and apply quantitative methods to today's investment process. Now, in Quantitative Investment Analysis Workbook, Second Edition, they offer you a wealth of practical information and exercises that will further enhance your understanding of this discipline. This essential study guide--which parallels the main book chapter by chapter--contains challenging problems and a complete set of solutions as well as concise learning outcome statements and summary overviews. If you're looking to successfully navigate today's dynamic investment environment, the lessons found within these pages can show you how. Topics reviewed include: The time value of money Discounted cash flow Probability distributions Sampling and estimation Hypothesis testing Multiple regression Time-series analysis And much more

RICHARD A. DEFUSCO, CFA, is an Associate Professor of Finance at the University of Nebraska-Lincoln (UNL). He earned his CFA charter in 1999. DeFusco is a member of the Omaha-Lincoln Society of Financial Analysts, and completed his bachelor's degree in management science at the University of Rhode Island and doctoral degree in finance at the University of Tennessee-Knoxville.

DENNIS W. MCLEAVEY, CFA, is Head of Professional Development Products at CFA Institute. During his twenty-five year academic career, he has taught at The University of Western Ontario, the University of Connecticut, the University of Rhode Island (where he founded a student-managed fund), and Babson College. McLeavey completed a doctorate in production management and industrial engineering at Indiana University in 1972, and earned his CFA charter in 1990.

JERALD E. PINTO, CFA, is Director in the CFA and CIPM Programs Division at CFA Institute. Before coming to CFA Institute in 2002, he consulted to corporations, foundations, and partnerships in investment planning, portfolio analysis, and quantitative analysis. He has also worked in the investment and banking industries in New York City and taught finance at New York University's Stern School of Business. He holds an MBA from Baruch College, a PhD in finance from the Stern School, and earned his CFA charter in 1992.

DAVID E. RUNKLE, CFA, is Vice President and Research Manager at U.S. Bancorp Piper Jaffray. He has been an adjunct professor of finance in the Carlson School of Management at the University of Minnesota since 1989. Runkle received a BA in economics from Carleton College and a PhD in economics from MIT.

Foreword	p. xiii
Acknowledgments	p. xvii
Introduction	p. xix
The Time Value of Money	p. 1
Introduction	p. 1
Interest Rates: Interpretation	p. 1
The Future Value of a Single Cash Flow	p. 3
The Frequency of Compounding	p. 8
Continuous Compounding	p. 10
Stated and Effective Rates	p. 12
The Future Value of a Series of Cash Flows	p. 13
Equal Cash Flows-Ordinary Annuity	p. 13
Unequal Cash Flows	p. 15
The Present Value of a Single Cash Flow	p. 15
Finding the Present Value of a Single Cash Flow	p. 15
The Frequency of Compounding	p. 17
The Present Value of a Series of Cash Flows	p. 19
The Present Value of a Series of Equal Cash Flows	p. 19
The Present Value of an Infinite Series of Equal Cash Flows-Perpetuity	p. 23
Present Values Indexed at Times Other Than t = 0	p. 24
The Present Value of a Series of Unequal Cash Flows	p. 26
Solving for Rates, Number of Periods, or Size of Annuity Payments	p. 27
Solving for Interest Rates and Growth Rates	p. 27
Solving for the Number of Periods	p. 30
Solving for the Size of Annuity Payments	p. 30
Review of Present and Future Value Equivalence	p. 35
The Cash Flow Additivity Principle	p. 36
Discounted Cash Flow Applications	p. 39
Introduction	p. 39
Net Present Value and Internal Rate of Return	p. 39
Net Present Value and the Net Present Value Rule	p. 40
The Internal Rate of Return and the Internal Rate of Return Rule	p. 42
Problems with the IRR Rule	p. 45
Portfolio Return Measurement	p. 47
Money-Weighted Rate of Return	p. 47
Time-Weighted Rate of Return	p. 49
Money Market Yields	p. 54
Statistical Concepts and Market Returns	p. 61
Introduction	p. 61
Some Fundamental Concepts	p. 61
The Nature of Statistics	p. 62
Populations and Samples	p. 62
Measurement Scales	p. 63
Summarizing Data Using Frequency Distributions	p. 65
The Graphic Presentation of Data	p. 72
The Histogram	p. 73
The Frequency Polygon and the Cumulative Frequency Distribution	p. 74
Measures of Central Tendency	p. 76
The Arithmetic Mean	p. 77
The Median	p. 81
The Mode	p. 84
Other Concepts of Mean	p. 85
Other Measures of Location: Quantiles	p. 94
Quartiles, Quintiles, Deciles, and Percentiles	p. 94
Quantiles in Investment Practice	p. 98
Measures of Dispersion	p. 100
The Range	p. 100
The Mean Absolute Deviation	p. 101
Population Variance and Population Standard Deviation	p. 103
Sample Variance and Sample Standard Deviation	p. 106
Semivariance, Semideviation, and Related Concepts	p. 110
Chebyshev's Inequality	p. 111
Coefficient of Variation	p. 113
The Sharpe Ratio	p. 115
Symmetry and Skewness in Return Distributions	p. 118
Kurtosis in Return Distributions	p. 123
Using Geometric and Arithmetic Means	p. 127
Probability Concepts	p. 129
Introduction	p. 129
Probability, Expected Value, and Variance	p. 129
Portfolio Expected Return and Variance of Return	p. 152
Topics in Probability	p. 161
Bayes' Formula	p. 161
Principles of Counting	p. 166
Common Probability Distributions	p. 171
Introduction	p. 171
Discrete Random Variables	p. 171
The Discrete Uniform Distribution	p. 173
The Binomial Distribution	p. 175
Continuous Random Variables	p. 185
Continuous Uniform Distribution	p. 186
The Normal Distribution	p. 189
Applications of the Normal Distribution	p. 197
The Lognormal Distribution	p. 200
Monte Carlo Simulation	p. 206
Sampling and Estimation	p. 215
Introduction	p. 215
Sampling	p. 215
Simple Random Sampling	p. 216
Stratified Random Sampling	p. 217
Time-Series and Cross-Sectional Data	p. 219
Distribution of the Sample Mean	p. 221
The Central Limit Theorem	p. 222
Point and Interval Estimates of the Population Mean	p. 225
Point Estimators	p. 225
Confidence Intervals for the Population Mean	p. 227
Selection of Sample Size	p. 233
More on Sampling	p. 235
Data-Mining Bias	p. 236
Sample Selection Bias	p. 238
Look-Ahead Bias	p. 240
Time-Period Bias	p. 240
Hypothesis Testing	p. 243
Introduction	p. 243
Hypothesis Testing	p. 244
Hypothesis Tests Concerning the Mean	p. 253
Tests Concerning a Single Mean	p. 254
Tests Concerning Differences between Means	p. 261
Tests Concerning Mean Differences	p. 265
Hypothesis Tests Concerning Variance	p. 269
Tests Concerning a Single Variance	p. 269
Tests Concerning the Equality (Inequality) of Two Variances	p. 271
Other Issues: Nonparametric Inference	p. 275
Tests Concerning Correlation: The Spearman Rank Correlation Coefficient	p. 276
Nonparametric Inference: Summary	p. 279
Correlation and Regression	p. 281
Introduction	p. 281
Correlation Analysis	p. 281
Scatter Plots	p. 281
Correlation Analysis	p. 282
Calculating and Interpreting the Correlation Coefficient	p. 283
Limitations of Correlation Analysis	p. 287
Uses of Correlation Analysis	p. 289
Testing the Significance of the Correlation Coefficient	p. 297
Linear Regression	p. 300
Linear Regression with One Independent Variable	p. 300
Assumptions of the Linear Regression Model	p. 303
The Standard Error of Estimate	p. 306
The Coefficient of Determination	p. 309
Hypothesis Testing	p. 310
Analysis of Variance in a Regression with One Independent Variable	p. 318
Prediction Intervals	p. 321
Limitations of Regression Analysis	p. 324
Multiple Regression and Issues in Regression Analysis	p. 325
Introduction	p. 325
Multiple Linear Regression	p. 325
Assumptions of the Multiple Linear Regression Model	p. 331
Predicting the Dependent Variable in a Multiple Regression Model	p. 336
Testing Whether All Population Regression Coefficients Equal Zero	p. 338
Adjusted R[superscript 2]	p. 340
Using Dummy Variables in Regressions	p. 341
Violations of Regression Assumptions	p. 345
Heteroskedasticity	p. 345
Serial Correlation	p. 351
Multicollinearity	p. 356
Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues	p. 359
Model Specification and Errors in Specification	p. 359
Principles of Model Specification	p. 359
Misspecified Functional Form	p. 360
Time-Series Misspecification (Independent Variables Correlated with Errors)	p. 368
Other Types of Time-Series Misspecification	p. 372
Models with Qualitative Dependent Variables	p. 372
Time-Series Analysis	p. 375
Introduction	p. 375
Challenges of Working with Time Series	p. 375
Trend Models	p. 377
Linear Trend Models	p. 377
Log-Linear Trend Models	p. 380
Trend Models and Testing for Correlated Errors	p. 385
Autoregressive (AR) Time-Series Models	p. 386
Covariance-Stationary Series	p. 386
Detecting Serially Correlated Errors in an Autoregressive Model	p. 387
Mean Reversion	p. 391
Multiperiod Forecasts and the Chain Rule of Forecasting	p. 391
Comparing Forecast Model Performance	p. 394
Instability of Regression Coefficients	p. 397
Random Walks and Unit Roots	p. 399
Random Walks	p. 400
The Unit Root Test of Nonstationarity	p. 403
Moving-Average Time-Series Models	p. 407
Smoothing Past Values with an n-Period Moving Average	p. 407
Moving-Average Time-Series Models for Forecasting	p. 409
Seasonality in Time-Series Models	p. 412
Autoregressive Moving-Average Models	p. 416
Autoregressive Conditional Heteroskedasticity Models	p. 417
Regressions with More than One Time Series	p. 420
Other Issues in Time Series	p. 424
Suggested Steps in Time-Series Forecasting	p. 425
Portfolio Concepts	p. 429
Introduction	p. 429
Mean-Variance Analysis	p. 429
The Minimum-Variance Frontier and Related Concepts	p. 430
Extension to the Three-Asset Case	p. 439
Determining the Minimum-Variance Frontier for Many Assets	p. 442
Diversification and Portfolio Size	p. 445
Portfolio Choice with a Risk-Free Asset	p. 449
The Capital Asset Pricing Model	p. 458
Mean-Variance Portfolio Choice Rules: An Introduction	p. 460
Practical Issues in Mean-Variance Analysis	p. 464
Estimating Inputs for Mean-Variance Optimization	p. 464
Instability in the Minimum-Variance Frontier	p. 470
Multifactor Models	p. 473
Factors and Types of Multifactor Models	p. 474
The Structure of Macroeconomic Factor Models	p. 475
Arbitrage Pricing Theory and the Factor Model	p. 478
The Structure of Fundamental Factor Models	p. 484
Multifactor Models in Current Practice	p. 485
Applications	p. 493
Concluding Remarks	p. 509
Appendices	p. 511
References	p. 521
Glossary	p. 527
About the CFA Program	p. 541
About the Authors	p. 543
Index	p. 545
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