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More About This Title Quantitative Investment Analysis, 2nd Edition
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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 moneyDiscounted cash flowProbability distributionsSampling and estimationHypothesis testingMultiple regressionTime-series analysisAnd much moreEnglish
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.
English
Acknowledgments xvii
Introduction xix
CHAPTER 1 The Time Value of Money 1
1 Introduction 1
2 Interest Rates: Interpretation 1
3 The Future Value of a Single Cash Flow 3
4 The Future Value of a Series of Cash Flows 13
5 The Present Value of a Single Cash Flow 15
6 The Present Value of a Series of Cash Flows 19
7 Solving for Rates, Number of Periods, or Size of Annuity Payments 27
CHAPTER 2 Discounted Cash Flow Applications 39
1 Introduction 39
2 Net Present Value and Internal Rate of Return 39
3 Portfolio Return Measurement 47
34 Money Market Yields 54
CHAPTER 3 Statistical Concepts andMarket Returns 61
1 Introduction 61
2 Some Fundamental Concepts 61
3 Summarizing Data Using Frequency Distributions 65
4 The Graphic Presentation of Data 72
5 Measures of Central Tendency 76
6 Other Measures of Location: Quantiles 94
7 Measures of Dispersion 100
8 Symmetry and Skewness in Return Distributions 118
9 Kurtosis in Return Distributions 123
10 Using Geometric and Arithmetic Means 127
CHAPTER 4 Probability Concepts 129
1 Introduction 129
2 Probability, Expected Value, and Variance 129
3 Portfolio Expected Return and Variance of Return 152
4 Topics in Probability 161
CHAPTER 5 Common Probability Distributions 171
1 Introduction 171
2 Discrete Random Variables 171
3 Continuous Random Variables 185
4 Monte Carlo Simulation 206
CHAPTER 6 Sampling and Estimation 215
1 Introduction 215
2 Sampling 215
3 Distribution of the Sample Mean 221
4 Point and Interval Estimates of the Population Mean 225
5 More on Sampling 235
CHAPTER 7 Hypothesis Testing 243
1 Introduction 243
2 Hypothesis Testing 244
3 Hypothesis Tests Concerning the Mean 253
4 Hypothesis Tests Concerning Variance 269
5 Other Issues: Nonparametric Inference 275
CHAPTER 8 Correlation and Regression 281
1 Introduction 281
2 Correlation Analysis 281
3 Linear Regression 300
CHAPTER 9 Multiple Regression and Issues in Regression Analysis 325
1 Introduction 325
2 Multiple Linear Regression 325
3 Using Dummy Variables in Regressions 341
4 Violations of Regression Assumptions 345
5 Model Specification and Errors in Specification 359
6 Models with Qualitative Dependent Variables 372
CHAPTER 10 Time-Series Analysis 375
1 Introduction 375
2 Challenges of Working with Time Series 375
3 Trend Models 377
4 Autoregressive (AR) Time-Series Models 386
5 Random Walks and Unit Roots 399
6 Moving-Average Time-Series Models 407
7 Seasonality in Time-Series Models 412
8 Autoregressive Moving-Average Models 416
9 Autoregressive Conditional Heteroskedasticity Models 417
10 Regressions with More than One Time Series 420
11 Other Issues in Time Series 424
12 Suggested Steps in Time-Series Forecasting 425
CHAPTER 11 Portfolio Concepts 429
1 Introduction 429
2 Mean–Variance Analysis 429
3 Practical Issues in Mean–Variance Analysis 464
4 Multifactor Models 473
Appendices 511
References 521
Glossary 527
About the CFA Program 541
About the Authors 543
Index 545
