Quantitative Investment Analysis -

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Editeur : Wiley

Langue : Anglais

Parution : 01/09/2020

Format : Moyen, de 350g à 1kg

Nombre de pages : 944

Expédition : 1748

Dimensions : 25.7 x 19.2 x 5.2

ISBN : 9781119743620

Résumé :

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

You can choose to sharpen your skills by furthering your hands-on experience in the Quantitative Investment Analysis Workbook, 4th Edition (sold separately)-an essential guide containing learning outcomes and summary overview sections, along with challenging problems and solutions.

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Biographie:

Preface xv

Acknowledgments xvii

About the CFA Institute Investment Series xix

Chapter 1 The Time Value of Money 1

Learning Outcomes 1

1. Introduction 1

2. Interest Rates: Interpretation 2

3. The Future Value of a Single Cash Flow 4

3.1. The Frequency of Compounding 9

3.2. Continuous Compounding 11

3.3. Stated and Effective Rates 12

4. The Future Value of a Series of Cash Flows 13

4.1. Equal Cash Flows-Ordinary Annuity 14

4.2. Unequal Cash Flows 15

5. The Present Value of a Single Cash Flow 16

5.1. Finding the Present Value of a Single Cash Flow 16

5.2. The Frequency of Compounding 18

6. The Present Value of a Series of Cash Flows 20

6.1. The Present Value of a Series of Equal Cash Flows 20

6.2. The Present Value of an Infinite Series of Equal Cash Flows-Perpetuity 24

6.3. Present Values Indexed at Times Other than t = 0 25

6.4. The Present Value of a Series of Unequal Cash Flows 27

7. Solving for Rates, Number of Periods, or Size of Annuity Payments 27

7.1. Solving for Interest Rates and Growth Rates 28

7.2. Solving for the Number of Periods 30

7.3. Solving for the Size of Annuity Payments 31

7.4. Review of Present and Future Value Equivalence 35

7.5. The Cash Flow Additivity Principle 37

8. Summary 38

Practice Problems 39

Chapter 2 Organizing, Visualizing, and Describing Data 45

Learning Outcomes 45

1. Introduction 45

2. Data Types 46

2.1. Numerical versus Categorical Data 46

2.2. Cross-Sectional versus Time-Series versus Panel Data 49

2.3. Structured versus Unstructured Data 50

3. Data Summarization 54

3.1. Organizing Data for Quantitative Analysis 54

3.2. Summarizing Data Using Frequency Distributions 57

3.3. Summarizing Data Using a Contingency Table 63

4. Data Visualization 68

4.1. Histogram and Frequency Polygon 68

4.2. Bar Chart 69

4.3. Tree-Map 73

4.4. Word Cloud 73

4.5. Line Chart 75

4.6. Scatter Plot 77

4.7. Heat Map 81

4.8. Guide to Selecting among Visualization Types 82

5. Measures of Central Tendency 85

5.1. The Arithmetic Mean 85

5.2. The Median 90

5.3. The Mode 92

5.4. Other Concepts of Mean 92

6. Other Measures of Location: Quantiles 102

6.1. Quartiles, Quintiles, Deciles, and Percentiles 103

6.2. Quantiles in Investment Practice 108

7. Measures of Dispersion 109

7.1. The Range 109

7.2. The Mean Absolute Deviation 109

7.3. Sample Variance and Sample Standard Deviation 111

7.4. Target Downside Deviation 114

7.5. Coefficient of Variation 117

8. The Shape of the Distributions: Skewness 119

9. The Shape of the Distributions: Kurtosis 121

10. Correlation between Two Variables 125

10.1. Properties of Correlation 126

10.2. Limitations of Correlation Analysis 129

11. Summary 132

Practice Problems 135

Chapter 3 Probability Concepts 147

Learning Outcomes 147

1. Introduction 148

2. Probability, Expected Value, and Variance 148

3. Portfolio Expected Return and Variance of Return 171

4. Topics in Probability 180

4.1. Bayes' Formula 180

4.2. Principles of Counting 184

5. Summary 188

References 190

Practice Problem 190

Chapter 4 Common Probability Distributions 195

Learning Outcomes 195

1. Introduction to Common Probability Distributions 196

2. Discrete Random Variables 196

2.1. The Discrete Uniform Distribut...

Sommaire:

Preface xv

Acknowledgments xvii

About the CFA Institute Investment Series xix

Chapter 1 The Time Value of Money 1

Learning Outcomes 1

1. Introduction 1

2. Interest Rates: Interpretation 2

3. The Future Value of a Single Cash Flow 4

4. The Future Value of a Series of Cash Flows 13

5. The Present Value of a Single Cash Flow 16

6. The Present Value of a Series of Cash Flows 20

7. Solving for Rates, Number of Periods, or Size of Annuity Payments 27

8. Summary 38

Practice Problems 39

Chapter 2 Organizing, Visualizing, and Describing Data 45

Learning Outcomes 45

1. Introduction 45

2. Data Types 46

3. Data Summarization 54

4. Data Visualization 68

5. Measures of Central Tendency 85

6. Other Measures of Location: Quantiles 102

7. Measures of Dispersion 109

8. The Shape of the Distributions: Skewness 119

9. The Shape of the Distributions: Kurtosis 121

10. Correlation between Two Variables 125

11. Summary 132

Practice Problems 135

Chapter 3 Probability Concepts 147

Learning Outcomes 147

1. Introduction 148

2. Probability, Expected Value, and Variance 148

3. Portfolio Expected Return and Variance of Return 171

4. Topics in Probability 180

5. Summary 188

References 190

Practice Problem 190

Chapter 4 Common Probability Distributions 195

Learning Outcomes 195

1. Introduction to Common Probability Distributions 196

2. Discrete Random Variables 196

3. Continuous Random Variables 210

4. Introduction to Monte Carlo Simulation 228

5. Summary 231

References 233

Practice Problems 234

Chapter 5 Sampling and Estimation 241

Learning Outcomes 241

1. Introduction 242

2. Sampling 242

3. Distribution of the Sample Mean 248

4. Point and Interval Estimates of the Population Mean 251

5. More on Sampling 261

6. Summary 267

References 269

Practice Problems 270

Chapter 6 Hypothesis Testing 275

Learning Outcomes 275

1. Introduction 276

2. Hypothesis Testing 277

3. Hypothesis Tests Concerning the Mean 287

4. Hypothesis Tests Concerning Variance and Correlation 303

5. Other Issues: Nonparametric Inference 310

6. Summary 314

References 317

Practice Problems 317

Chapter 7 Introduction to Linear Regression 327

Learning Outcomes 327

1. Introduction 328

2. Linear Regression 328

3. Assumptions of the Linear Regression Model 332

4. The Standard Error of Estimate 335

5. The Coefficient of Determination 337

6. Hypothesis Testing 339

7. Analysis of Variance in a Regression with One Independent Variable 347

8. Prediction Intervals 350

9. Summary 353

References 354

Practice Problems 354

Chapter 8 Multiple Regression 365

Learning Outcomes 365

1. Introduction 366

2. Multiple Linear Regression 366

3. Using Dummy Variables in Regressions 381

4. Violations of Regression Assumptions 387

5. Model Specification and Errors in Specification 401

6. Models with Qualitative Dependent Variables 414

7. Summary 422

References 425

Practice Problems 426

Chapter 9 Time-Series Analysis 451

Learning Outcomes 451

1. Introduction to Time-Series Analysis 452

2. Challenges of Working with Time Series 454

3. Trend Models 454

4. Autoregressive (AR) Time-Series Models 464

5. Random ...