Autorentext

GIUSEPPE A. PALEOLOGO, PhD, is the Head of Quantitative Research at Balyasny Asset Management. Previously, he held senior positions in quantitative research and risk at Citadel, Millennium, and Hudson River Trading. He has extensive experience in equities quantitative risk management, portfolio construction, and alpha signal research. He holds a doctorate in Management Science and Engineering from Stanford University.

Klappentext

Practical and relevant insights into the intricacies of quantitative trading at every stage of the investing process The Elements of Quantitative Investing is an in insightful and practical roadmap to every part of the quantitative investing process, from strategy formulation to post-trade analysis. Written by Dr. Giuseppe Paleologo, the author of the widely read Advanced Portfolio Management: A Quant's Guide for Fundamental Investors, the book walks you through every step of quantitative modeling. You'll learn about the statistical properties of returns, factor models, and portfolio management as you discover critical quantitative investing concepts grounded in key financial context. Everything that's been included in the book is highly relevant to quantitative investing in contemporary markets, and the author has focused exclusively on those subjects that can advance a quantitative investor's success in an increasingly competitive financial marketplace. Perfect for financial practitioners looking for applicable insights from one of the industry's leading lights, The Elements of Quantitative Investing makes accessible information, techniques, strategies, and knowledge typically available only to a select few. It's an essential and hands-on guide to quantitative investing.

Inhalt

Introduction xvii

Prerequisites xxi

Organization xxii

Acknowledgments xxv

1 The Map and the Territory 5

1.1 The Securities 7

1.2 Modes of Exchange 9

1.3 Who Are the Market Participants? 11

1.3.1 The Sell Side 11

1.3.2 The Buy Side 15

1.4 Where Do Excess Returns Come From? 19

1.5 The Elements of Quantitative Investing 24

2 Univariate Returns 29

2.1 Returns 30

2.1.1 Definitions 30

2.1.2 Excess Returns 32

2.1.3 Log Returns 33

2.1.4 Estimating Prices and Returns 34

2.1.5 Stylized Facts 37

2.2 Conditional Heteroscedastic Models (CHM) 42

2.2.1 GARCH(1, 1) and Return Stylized Facts 44

2.2.2 GARCH as Random Recursive Equations 47

2.2.3 ?GARCH(1, 1) Estimation 49

2.2.4 Realized Volatility 50

2.3 State-Space Estimation of Variance 55

2.3.1 Muth's Original Model: EWMA 55

2.3.2 The Harvey-Shephard Model 60

2.4 Appendix 62

2.4.1 The Kalman Filter 62

2.4.2 Kalman Filter Examples 66

2.5 Exercises 70

3 Interlude: What is Performance? 73

3.1 Expected Return 74

3.2 Volatility 74

3.3 Sharpe Ratio 76

3.4 Capacity 78

4 Linear Models of Returns 83

4.1 Factor Models 84

4.2 Interpretations of Factor Models 87

4.2.1 Graphical Model 88

4.2.2 Superposition of E_ects 89

4.2.3 Single-Asset Product 90

4.3 Alpha Spanned and Alpha Orthogonal 91

4.4 Transformations 95

4.4.1 Rotations 95

4.4.2 Projections 98

4.4.3 Push-Outs 99

4.5 Applications 101

4.5.1 Performance Attribution 101

4.5.2 Risk Management: Forecast and Decomposition 102

4.5.3 Portfolio Management 105

4.5.4 Alpha Research 107

4.6 Factor Models Types 108

4.7 Appendix 109

4.7.1 Linear Regression 109

4.7.2 Linear Regression Decomposition 116

4.7.3 The Frisch-Waugh-Lovell Theorem 116

4.7.4 The Singular Value Decomposition 120

4.8 Exercises 123

5 Evaluating Risk 127

5.1 Evaluating the Covariance Matrix 128

5.1.1 Robust Loss Functions for Volatility Estimation 128

5.1.2 Application to Multivariate Returns 130

5.2 Evaluating the Precision Matrix 134

5.2.1 Minimum-Variance Portfolios 134

5.2.2 Mahalanobis Distance 135

5.3 Ancillary Tests 137

5.3.1 Model Turnover 138

5.3.2 Testing Betas 139

5.3.3 Coefficient of Determination? 140

5.4 Appendix 143

5.4.1 Proof for Minimum-Variance Portfolios 143

6 Fundamental Factor Models 147

6.1 The Inputs and the Process 148

6.1.1 The Inputs 148

6.1.2 The Process 152

6.2 Cross-Sectional Regression 153

6.2.1 Rank-Deficient Loadings Matrices 158

6.3 Estimating The Factor Covariance Matrix 160

6.3.1 Factor Covariance Matrix Shrinkage 161

6.3.2 Dynamic Conditional Correlation 162

6.3.3 Short-Term Volatility Updating 163

6.3.4 Correcting for Autocorrelation in Factor Returns 166

6.4 Estimating the Idiosyncratic Covariance Matrix 167

6.4.1 Exponential Weighting 167

6.4.2 Visual Inspection 167

6.4.3 Short-Term Idio Update 168

6.4.4 O_-Diagonal Clustering 169

6.4.5 Idiosyncratic Covariance Matrix Shrinkage 173

6.5 Winsorization of Returns 174

6.6 ?Advanced Model Topics 176

6.6.1 Linking Models 176

6.6.2 Currency Rebasing 184

6.7 A Tour of Factors 188

7 Statistical Factor Models 195

7.1 Statistical Models: The Basics 197

7.1.1 Best Low-Rank Approximation and PCA 197

7.1.2 Maximum Likelihood Estimation and PCA 202

7.1.3 Cross-Sectional and Time-Series Regressions via SVD 205

7.2 Beyond the Basics 207

7.2.1 The Spiked Covariance Model 208

7.2.2 Spectral Limit Behavior of the Spiked Covariance

Model 210

7.2.3 Optimal Shrinkage of Eigenvalues 213

7.2.4 Eigenvalues: Experiments vs. Theory 216

7.2.5 Choosing the Number of Factors 218

7.3 Real-Life Stylized Behavior of PCA 220

7.3.1 Concentration of Eigenvalues 221

7.3.2 Controlling the Turnover of Eigenvectors 223

7.4 Interpreting Principal Components 230

7.4.1 The Clustering View 230

7.4.2 The Regression View 232

7.5 Statistical Model Estimation in Practice 234

7.5.1 Weighted and Two-Stage PCA 234

7.5.2 Implementing Statistical Models in Production 238

7.6 Appendix 241

7.6.1 Exercises and Extensions to PCA 241

7.6.2 Asymptotic Properties of PCA 246

8 Evaluating Excess Returns 249

8.1 Backtesting Best Practices 251

8.1.1 Data Sourcing 251

8.1.2 Research Process 253

8.2 The Backtesting Protocol 259

8.2.1 Cross-Validation and Walk-Forward 259

8.3 The Rademacher Anti-Serum (RAS) 265

8.3.1 Setup 265

8.3.2 Main result and Interpretation 269

8.4 Some Empirical Results 275

8.4.1 Simulations 275

8.4.2 Historical Anomalies 279

8.5 ?Appendix 282

8.5.1 Proofs for RAS 282

9 Portfolio Management: The Basics 289

9.1 Why Mean-Variance Optimization? 290

9.2 Mean-Variance Optimal Portfolios 293

9.3 Trading in Factor Space 301

9.3.1 Factor-Mimicking Portfolios 301

9.3.2 Adding, Estimating, and Trading a New Factor 304

9.3.3 Factor Portfolios from Sorts? 308

9.4 Trading in Idio Space 310

9.5 Drivers of Information Ratio: Information Coefficient and Diversification 311

9.6 Aggregation: Signals vs. Portfolios 315

9.7 Appendix 320

9.7.1 Some Useful Results from Linear Algebra 320

9.7.2 Some Portfolio Optimization Problems 320

9.7.3 Optimality of FMPs 321

9.7.4 Single-Factor Covariance Matrix Updating 324

10 Beyond Simple Mean-Variance 327

10.1 Shortcomings of Naive MVO 328

10.2 Constraints and Modified Objectives 335

10.2.1 Types of Constraints 336

10.2.2 Do Constraints Improve or Worsen Performance? 341

10.2.3 Constraints as Penalties 342

10.3 How Does Estimation Error Affect the Sharpe Ratio? 349

**10.3.1 The …