Summary

Inside the Black Box explains how quantitative and algorithmic trading strategies work in non-mathematical terms supplemented by anecdotes and real-world stories. There is a large chasm in the understanding of what quants do with quants often perpetuating the darkness by cloaking even trivial facts about their strategies and operations in secrecy. Most of their strategies are understandable and with that understanding investment professionals can better assess a particular fund's value. The book also explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager. How do quants capture alpha? What is the real level of discretion in quant trading? What is the difference between theory-driven systems vs. data-mining strategies? What are the typical structures of quant systems? How do quants model risk? How do you know if it works? What are execution algorithms?

Author Biography

Rishi K. Narang is the Founding Principal of Telesis Capital LLC, which invests in quantitative trading strategies. Previously, he was managing director and co–portfolio manager at Santa Barbara Alpha Strategies. Narang cofounded and was president of Tradeworx, Inc., a quantitative hedge fund manager, from 1999–2002. He has been involved in the hedge fund industry, with a focus on quantitative trading strategies, since 1996. Narang graduated from the University of California, Berkeley, with a BA in economics.

Preface to the Second Edition xiii

Acknowledgments xvii

Part ONE The Quant Universe

Chapter 1 Why Does Quant Trading Matter? 3

The Benefit of Deep Thought 8

The Measurement and Mismeasurement of Risk 9

Disciplined Implementation 10

Summary 11

Notes 11

Chapter 2 An Introduction to Quantitative Trading 13

What Is a Quant? 14

What Is the Typical Structure of a Quantitative Trading System? 16

Summary 19

Notes 20

Part two Inside the Black Box

Chapter 3 Alpha Models: How Quants Make Money 23

Types of Alpha Models: Theory-Driven and Data-Driven 24

Theory-Driven Alpha Models 26

Data-Driven Alpha Models 42

Implementing the Strategies 45

Blending Alpha Models 56

Summary 62

Notes 64

Chapter 4 Risk Models 67

Limiting the Amount of Risk 69

Limiting the Types of Risk 72

Summary 76

Notes 78

Chapter 5 Transaction Cost Models 79

Defining Transaction Costs 80

Types of Transaction Cost Models 85

Summary 90

Note 91

Chapter 6 Portfolio Construction Models 93

Rule-Based Portfolio Construction Models 94

Portfolio Optimizers 98

Output of Portfolio Construction Models 112

How Quants Choose a Portfolio Construction Model 113

Summary 113

Notes 115

Chapter 7 Execution 117

Order Execution Algorithms 119

Trading Infrastructure 128

Summary 130

Notes 131

Chapter 8 Data 133

The Importance of Data 133

Types of Data 135

Sources of Data 137

Cleaning Data 139

Storing Data 144

Summary 145

Notes 146

Chapter 9 Research 147

Blueprint for Research: The Scientific Method 147

Idea Generation 149

Testing 151

Summary 170

Note 171

Part three A Practical Guide for Investors in Quantitative Strategies

Chapter 10 Risks Inherent to Quant Strategies 175

Model Risk 176

Regime Change Risk 180

Exogenous Shock Risk 184

Contagion, or Common Investor, Risk 186

How Quants Monitor Risk 193

Summary 195

Notes 195

Chapter 11 Criticisms of Quant Trading 197

Trading Is an Art, Not a Science 197

Quants Cause More Market Volatility by Underestimating Risk 199

Quants Cannot Handle Unusual Events or Rapid Changes in Market Conditions 204

Quants Are All the Same 206

Only a Few Large Quants Can Thrive in the Long Run 207

Quants Are Guilty of Data Mining 210

Summary 213

Notes 213

Chapter 12 Evaluating Quants and Quant Strategies 215

Gathering Information 216

Evaluating a Quantitative Trading Strategy 218

Evaluating the Acumen of Quantitative Traders 221

The Edge 223

Evaluating Integrity 227

How Quants Fit into a Portfolio 229

Summary 231

Note 233

Part four High-Speed and High-Frequency Trading

Chapter 13 An Introduction to High-Speed and High-Frequency Trading* 237

Notes 241

Chapter 14 High-Speed Trading 243

Why Speed Matters 244

Sources of Latency 252

Summary 262

Notes 263

Chapter 15 High-Frequency Trading 265

Contractual Market Making 265

Noncontractual Market Making 269

Arbitrage 271

Fast Alpha 273

HFT Risk Management and Portfolio Construction 274

Summary 277

Note 277

Chapter 16 Controversy Regarding High-Frequency Trading 279

Does HFT Create Unfair Competition? 280

Does HFT Lead to Front-Running or Market Manipulation? 283

Does HFT Lead to Greater Volatility or Structural Instability? 289

Does HFT Lack Social Value? 296

Regulatory Considerations 297

Summary 299

Notes 300

Chapter 17 Looking to the Future of Quant Trading 303

About the Author 307

Index 309

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