The pioneer of value investing, Benjamin Graham, believed in a philosophy that continues to be followed by some of today's most successful investors, such as Warren Buffett. Part of this philosophy includes adhering to your stock selection process come "hell or high water" which, in his view, was one of the most important aspects of investing.

So, if a quant designs and implements mathematical models for predicting stock or market movements, what better way to remain objective, then to invest using algorithms or the quantitative method? This is exactly what Ben Graham Was a Quant will show you how to do. Opening with a brief history of quantitative investing, this book quickly moves on to focus on the fundamental and financial factors used in selecting "Graham" stocks, demonstrate how to test these factors, and discuss how to combine them into a quantitative model.

• Reveals how to create custom screens based on Ben Graham's methods for security selection
• Addresses what it takes to find those factors most influential in forecasting stock returns
• Explores how to design models based on other styles and international strategies

If you want to become a better investor, you need solid insights and the proper guidance. With Ben Graham Was a Quant, you'll receive this and much more, as you learn how to create quantitative models that follow in the footsteps of Graham's value philosophy.

Preface xi

Introduction: The Birth of the Quant 1

Characterizing the Quant 3

Active versus Passive Investing 6

Chapter 1 Desperately Seeking Alpha 11

The Beginnings of the Modern Alpha Era 16

Important History of Investment Management 18

Methods of Alpha Searching 20

Chapter 2 Risky Business 27

Experienced versus Exposed Risk 28

The Black Swan: A Minor ELE Event—Are Quants to Blame? 34

Active versus Passive Risk 38

Other Risk Measures: VAR, C-VAR, and ETL 49

Summary 52

Chapter 3 Beta is Not “Sharpe” Enough 55

Back to Beta 64

Beta and Volatility 65

The Way to a Better Beta: Introducing the g-Factor 67

Tracking Error: The Deviant Differential Measurer 75

Summary 77

Chapter 4 Mr. Graham, I Give You Intelligence 79

Fama-French Equation 81

The Graham Formula 89

Factors for Use in Quant Models 90

Momentum: Increasing Investor Interest 96

Volatility as a Factor in Alpha Models 113

Chapter 5 Modeling Pitfalls and Perils 123

Data Availability, Look-Ahead, and Survivorship Biases 124

Building Models You Can Trust 127

Scenario, Out-of-Sample, and Shock Testing 131

Data Snooping and Mining 139

Statistical Significance and Other Fascinations 140

Choosing an Investment Philosophy 148

Growth, Value, Quality 149

Investment Consultant as Dutch Uncle 152

Where Are the Relative Growth Managers? 154

Chapter 6 Testing the Graham Crackers . . . er, Factors 159

The First Tests: Sorting 160

Time-Series Plots 173

The Next Tests: Scenario Analysis 182

Chapter 7 Building Models from Factors 193

Surviving Factors 194

Weighting the Factors 197

The Art versus Science of Modeling 200

Time Series of Returns 210

Other Conditional Information 215

The Final Model 217

Other Methods of Measuring Performance: Attribution Analysis via Brinson and Risk Decomposition 220

Regression of the Graham Factors with Forward Returns 228

Chapter 8 Building Portfolios from Models 233

The Deming Way: Benchmarking Your Portfolio 235

Portfolio Construction Issues 247

Using an Online Broker: Fidelity, E*Trade, TD Ameritrade, Schwab, Interactive Brokers, and TradeStation 249

Working with a Professional Investment Management System: Bloomberg, Clarifi, and FactSet 251

Chapter 9 Barguments: The Antidementia Bacterium 255

The Colossal Nonfailure of Asset Allocation 256

The Stock Market as a Class of Systems 258

Stochastic Portfolio Theory: An Introduction 266

Portfolio Optimization: The Layman’s Perspective 276

Tax-Efficient Optimization 282

Summary 282

Chapter 10 Past and Future View 285

Why Did Global Contagion and Meltdown Occur? 292

Fallout of Crises 297

The Rise of the Multinational State-Owned Enterprises 301

The Emerged Markets 310

The Future Quant 311

Notes 317

Acknowledgments 325

About the Author 327

Index 329

STEVEN P. GREINER, Ph.D., has served as the senior quantitative strategist and portfolio manager for Allegiant Asset Management (now wholly owned by PNC Capital Advisors) and was a member of its Investment Committee. Prior to this, he was a senior quantitative strategist for large capitalization investments at Harris Investment Management. He has more than twenty years of quantitative and modeling experience. Currently, Dr. Greiner is the head of Risk Research for FactSet Research Systems. He received a BS in mathematics and chemistry from the University of Buffalo, an MS and PhD in physical chemistry from the University of Rochester, and attained postdoctoral experience from the Free University Berlin, Department of Physics.