In the newly revised third edition of Inside the Black Box: A Simple Guide to Systematic Investing, veteran practitioner and investor Rishi K Narang delivers another insightful discussion of how quantitative and algorithmic trading strategies work in non-mathematical terms. As with prior editions, this third edition is full of timeless concepts and timely updates. Supplemented by compelling anecdotes and real-world stories, the book explains the most relevant developments in the discipline since the publication of the second edition in 2013.
You'll find out about the explosion in machine learning for alphas, signal mixing, data extraction, and execution, as well as the proliferation of alt data and a discussion of how to use it appropriately. You'll also discover: - Updated discussions of approaches to research - Newer and more effective approaches to portfolio optimization - The frontiers of quantitative investing
An essential and accessible treatment of a complicated and of-the-moment topic, Inside the Black Box remains the gold standard for non-mathematicians seeking to understand the ins and outs of one of the most fascinating and lucrative trading strategies, as well as quants from disciplines outside of finance looking for a conceptual framework on which to build profitable systematic trading strategies.
Table of Contents
Foreword xiPreface to the Third Edition xiii
Acknowledgments xv
PART ONE The Quant Universe
CHAPTER 1 Why Does Quant Trading Matter? 3
1.1 The Benefit of Deep Thought 8
1.2 The Measurement and Mismeasurement of Risk 9
1.3 Disciplined Implementation 10
1.4 Summary 11
Notes 11
CHAPTER 2 An Introduction to Quantitative Trading 13
2.1 What Is a Quant? 14
2.2 What Is the Typical Structure of a Quantitative Trading System? 17
2.3 Summary 20
Notes 20
PART TWO Inside the Black Box
CHAPTER 3 Alpha Models: How Quants Make Money 23
3.1 Types of Alpha Models: Theory-Driven and Data-Driven 25
3.2 Theory-Driven Alpha Models 28
3.3 Data-Driven Alpha Models 47
3.4 Implementing the Strategies 52
3.5 Blending Alpha Models 64
3.6 Summary 68
Notes 69
CHAPTER 4 Risk Models 71
4.1 Limiting the Amount of Risk 73
4.2 Limiting the Types of Risk 76
4.3 Risk Management, Outside of Risk Models 81
4.4 Summary 82
Notes 84
CHAPTER 5 Transaction Cost Models 85
5.1 Defining Transaction Costs 86
5.2 Types of Transaction Cost Models 91
5.3 Summary 96
Notes 97
CHAPTER 6 Portfolio Construction Models 99
6.1 Rule-Based Portfolio Construction Models 100
6.2 Portfolio Optimizers 104
6.3 Output of Portfolio Construction Models 121
6.4 How Quants Choose a Portfolio Construction Model 123
6.5 Summary 123
Notes 125
CHAPTER 7 Execution 127
7.1 Order Execution Algorithms 129
7.2 Trading Infrastructure 138
7.3 Summary 140
Notes 141
CHAPTER 8 Data 143
8.1 The Importance of Data 144
8.2 Types of Data 146
8.3 Sources of Data 149
8.4 Cleaning Data 152
8.5 Storing Data 158
8.6 Summary 159
Notes 160
CHAPTER 9 Research 161
9.1 Blueprint for Research: The Scientific Method 161
9.2 Idea Generation 163
9.3 Testing 166
9.4 Summary 186
Note 187
PART THREE A Practical Guide for Investors in Quantitative Strategies
CHAPTER 10 Risks Inherent to Quant Strategies 191
10.1 Model Risk 191
10.2 Regime Change Risk 196
10.3 Exogenous Shock Risk 200
10.4 Contagion, or Common Investor, Risk 202
10.5 How Quants Monitor Risk 209
10.6 Summary 211
Notes 211
CHAPTER 11 Criticisms of Quant Trading 213
11.1 Trading Is an Art, Not a Science 214
11.2 Quants Cause More Market Volatility by Underestimating Risk 215
11.3 Quants Cannot Handle Unusual Events or Rapid Changes in Market Conditions 221
11.4 Quants Are All the Same 223
11.5 Only a Few Large Quants Can Thrive in the Long Run 224
11.6 Quants Are Guilty of Data Mining 228
11.7 Summary 231
Notes 231
CHAPTER 12 Evaluating Quants and Quant Strategies 233
12.1 Gathering Information 234
12.2 Evaluating a Quantitative Trading Strategy 236
12.3 Evaluating the Acumen of Quantitative Traders 239
12.4 The Edge 241
12.5 Evaluating Integrity 244
12.6 How Quants Fit into a Portfolio 246
12.7 Summary 249
Notes 251
PART FOUR High-Speed and High-Frequency Trading
CHAPTER 13 An Introduction to High-Speed and High-Frequency Trading 255
Notes 259
CHAPTER 14 High-Speed Trading 261
14.1 Why Speed Matters 262
14.2 Sources of Latency 270
14.3 Summary 280
Notes 281
CHAPTER 15 High-Frequency Trading 283
15.1 Contractual Market Making 283
15.2 Non-Contractual Market Making 288
15.3 Arbitrage 289
15.4 Fast Alpha 291
15.5 HFT Risk Management and Portfolio Construction 293
15.6 Summary 295
Note 296
CHAPTER 16 Looking to the Future of Quant Trading 297
16.1 Business Models 297
16.2 Machine Learning and Artificial Intelligence 301
16.3 Expansion into More Asset Classes and Markets 302
16.4 Digitalization and Datasets 303
16.5 Man and Machine 304
16.6 Conclusion 305
Appendix: Controversy Regarding High-Frequency Trading 307
A.1 Does HFT Create Unfair Competition? 308
A.2 Does HFT Lead to Front-Running or Market Manipulation? 311
A.3 Does HFT Lead to Greater Volatility or Structural Instability? 317
A.4 Does HFT Lack Social Value? 324
A.5 Regulatory Considerations 325
A.6 Summary 327
Notes 328
About the Author 329
Index 331
