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Quantitative Investment Analysis de Cfa Institute

Quantitative Investment Analysis: CFA Institute Investment Series

Autor Cfa Institute
en Limba Engleză Hardback – 26 noi 2020

Structura acestei ediții este riguros organizată în jurul obiectivelor de învățare (Learning Outcome Statements), oferind un parcurs pedagogic care transformă conceptele statistice complexe în instrumente de lucru aplicabile. Quantitative Investment Analysis nu este doar un manual teoretic, ci un ghid metodologic care facilitează trecerea de la analiza de bază la tehnici avansate de modelare. Remarcăm efortul autorilor de a menține o consistență strictă a notațiilor matematice pe parcursul celor peste 900 de pagini, element esențial pentru un proces de studiu coerent. Această a patra ediție se distinge prin integrarea tehnologiilor moderne, extinzând aria de acoperire către algoritmii de Machine Learning și rolul Big Data în contextul investițional actual. Cititorul va parcurge etape critice, de la fundamentarea statistică la capitole de sinteză dedicate modelării factoriale, managementului riscului și simulărilor de tip backtesting. Pe linia practică a lucrării Investments de Michael McMillan, dar cu un focus mult mai pronunțat pe metodele cantitative și rigoarea matematică necesară testării strategiilor, volumul de față devine o referință autoritară pentru oricine activează în piețele financiare. Recomandăm acest volum pentru modul în care reușește să echilibreze teoria econometrică cu realitățile pieței. Spre deosebire de alte cursuri academice, abordarea CFA Institute pune accent pe aplicabilitate, oferind o multitudine de probleme practice, grafice și tabele care clarifică utilizarea metodelor cantitative în procesul de luare a deciziilor de investiții.

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Specificații

ISBN-13: 9781119743620
ISBN-10: 1119743621
Pagini: 944
Dimensiuni: 192 x 257 x 52 mm
Greutate: 1.75 kg
Ediția:4. Auflage
Editura: John Wiley & Sons, Inc.
Colecția CFA Institute Investment Series
Seria CFA Institute Investment Series

Locul publicării:Hoboken, United States

De ce să citești această carte

Această carte este indispensabilă pentru analiștii financiari și investitorii care doresc să stăpânească instrumentele statistice moderne. Cititorul câștigă o înțelegere profundă a tehnicilor de Machine Learning și Big Data aplicate în finanțe. Este resursa ideală pentru cei care se pregătesc pentru examenul CFA sau pentru profesioniștii care au nevoie de un cadru riguros de analiză a riscului și testare a strategiilor de portofoliu.

Despre autor

CFA Institute este asociația globală a profesioniștilor în investiții care stabilește standardele pentru excelență tehnică și etică în industria financiară. Organizația administrează programele de certificare CFA (Chartered Financial Analyst) și CIPM la nivel mondial. Prin contribuțiile membrilor săi și ale experților din domeniu, institutul publică resurse educaționale de referință, precum seria CFA Institute Investment Series, care sintetizează cunoștințele colective ale practicienilor de top pentru a promova integritatea și competența în managementul investițiilor.

Notă biografică

CFA Institute is the global association of investment professionals that sets the standard for professional excellence and credentials. The organization is a champion for ethical behavior in investment markets and a respected source of knowledge in the global financial community. The end goal: to create an environment where investors' interests come first, markets function at their best, and economies grow. CFA Institute has more than 155,000 members in 165 countries and territories, including 150,000 CFA(r) charterholders, and 148 member societies. For more information, visit www.cfainstitute.org.

Descriere

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.

Cuprins

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