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Produktbild: Quantitative Investment Analysis

Quantitative Investment Analysis

Aus der Reihe CFA Institute Investment Series

122,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.11.2020

Verlag

John Wiley & Sons Inc

Seitenzahl

944

Maße (L/B/H)

25,7/19,2/5,2 cm

Gewicht

1748 g

Auflage

4. Auflage

Sprache

Englisch

ISBN

978-1-119-74362-0

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.11.2020

Verlag

John Wiley & Sons Inc

Seitenzahl

944

Maße (L/B/H)

25,7/19,2/5,2 cm

Gewicht

1748 g

Auflage

4. Auflage

Sprache

Englisch

ISBN

978-1-119-74362-0

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Quantitative Investment Analysis

  • 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 Walks and Unit Roots 478

    6. Moving-Average Time-Series Models 486

    7. Seasonality in Time-Series Models 491

    8. Autoregressive Moving-Average Models 496

    9. Autoregressive Conditional Heteroskedasticity Models 497

    10. Regressions with More than One Time Series 500

    11. Other Issues in Time Series 504

    12. Suggested Steps in Time-Series Forecasting 505

    13. Summary 507

    References 508

    Practice Problems 509

    Chapter 10 Machine Learning 527

    Learning Outcomes 527

    1. Introduction 527

    2. Machine Learning and Investment Management 528

    3. What is Machine Learning? 529

    4. Overview of Evaluating ML Algorithm Performance 533

    5. Supervised Machine Learning Algorithms 539

    6. Unsupervised Machine Learning Algorithms 559

    7. Neural Networks, Deep Learning Nets, and Reinforcement Learning 575

    8. Choosing an Appropriate ML Algorithm 589

    9. Summary 590

    References 593

    Practice Problems 593

    Chapter 11 Big Data Projects 597

    Learning Outcomes 597

    1. Introduction 597

    2. Big Data in Investment Management 598

    3. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data 599

    4. Data Preparation and Wrangling 603

    5. Data Exploration Objectives and Methods 617

    6. Model Training 629

    7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks 639

    8. Summary 664

    Practice Problems 665

    Chapter 12 Using Multifactor Models 675

    Learning Outcomes 675

    1. Introduction 675

    2. Multifactor Models and Modern Portfolio Theory 676

    3. Arbitrage Pricing Theory 677

    4. Multifactor Models: Types 683

    5. Multifactor Models: Selected Applications 695

    6. Summary 706

    References 707

    Practice Problems 708

    Chapter 13 Measuring and Managing Market Risk 713

    Learning Outcomes 713

    1. Introduction 714

    2. Understanding Value at Risk 714

    3. Other Key Risk Measures-Sensitivity and Scenario Measures 735

    4. Using Constraints in Market Risk Management 750

    5. Applications of Risk Measures 755

    6. Summary 764

    References 766

    Practice Problems 766

    Chapter 14 Backtesting and Simulation 775

    Learning Outcomes 775

    1. Introduction 775

    2. The Objectives of Backtesting 776

    3. The Backtesting Process 776

    4. Metrics and Visuals Used in Backtesting 792

    5. Common Problems in Backtesting 801

    6. Backtesting Factor Allocation Strategies 807

    7. Comparing Methods of Modeling Randomness 813

    8. Scenario Analysis 824

    9. Historical Simulation versus Monte Carlo Simulation 828

    10. Historical Simulation 830

    11. Monte Carlo Simulation 835

    12. Sensitivity Analysis 840

    13. Summary 848

    References 849

    Practice Problems 849

    Appendices 855

    Glossary 865

    About the Authors 883

    About the CFA Program 885

    Index 887