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 ...

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Quantitative Investment Analysis

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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
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Product Details

  • ISBN-13: 9781118044780
  • Publisher: Wiley
  • Publication date: 1/6/2011
  • Series: CFA Institute Investment Series , #2
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Pages: 600
  • Sales rank: 1,159,721
  • File size: 16 MB
  • Note: This product may take a few minutes to download.

Meet the Author

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.

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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
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