Your Essential Guide to Quantitative Hedge Fund Investing

Cover
Endorsements
Half Title
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Acknowledgments
About the Authors
CHAPTER 1: Introduction to Hedge Funds
	1.1. AN INTRODUCTION TO HEDGE FUNDS
	1.2. MYTHS ABOUT HEDGE FUNDS
		1.2.1. Hedge Fund Investing Is Easy
		1.2.2. It Is Easy to Select Top-performing Hedge Funds
		1.2.3. Hedge Funds Hedge
		1.2.4. Active Hedge Funds Outperform
		1.2.5. Socially Responsible Hedge Funds Outperform
		1.2.6. Investors Benefit from Hedge Funds Identifying Undervalued Stocks
	1.3. KEY TAKEAWAYS
CHAPTER 2: Hedge Fund Research and Data
	2.1. RIGOROUS AND PRACTICAL HEDGE FUND RESEARCH
		2.1.1. Challenges
	2.2. HEDGE FUND DATA: GARBAGE IN, GARBAGE OUT?
		2.2.1. Public Databases and Biases
		2.2.2. Other Frictions and Considerations
	2.3. KEY TAKEAWAYS
CHAPTER 3: Manager Selection and Hedge Fund Factors
	3.1. ACADEMIC RESEARCH AND FRAMEWORK FOR MANAGER SELECTION
		3.1.1. Hedge Fund Performance Persistence
		3.1.2. General Framework of Molyboga, Bilson, and Baek
		3.1.3. “ALL” Funds
		3.1.4. “SKILLED” Funds
		3.1.5. Evaluation of Out-of-sample Results
	3.2. FACTORS AND FACTOR SELECTION
		3.2.1. Issues with Standard 7-Factor Fung-Hsieh Model
		3.2.2. Volatility Premium
		3.2.3. Short-term Momentum
		3.2.4. Term Premium in Commodities
		3.2.5. Factor Selection
		3.2.6. Quantitative and Qualitative Factors
		3.2.7. Operational Due Diligence of Digital Asset Funds
	3.3. KEY TAKEAWAYS
CHAPTER 4: Performance Persistence
	4.1. PERFORMANCE PERSISTENCE
		4.1.1. Does Skill Exist?
			4.1.1.1. A Bootstrap Approach
			4.1.1.2. A False Discovery Rates Approach
		4.1.2. Performance Evaluation with a Noise Reduced Alpha Approach
		4.1.3. Performance Evaluation with Seemingly Unrelated Assets
		4.1.4. Performance Evaluation with Decreasing Returns to Scale
		4.1.5. Identifying Hedge Fund Skill with Peer Cohorts
	4.2. INTERESTING NUGGETS: COMBINING QUANTITATIVE AND QUALITATIVE FACTORS WITHIN A BAYESIAN FRAMEWORK
	4.3. KEY TAKEAWAYS
CHAPTER 5: From Mean-Variance to Risk Parity
	5.1. FRAMEWORK FOR PORTFOLIO MANAGEMENT
		5.1.1. A General Framework of Molyboga and L’Ahelec
		5.1.2. Mean-Variance Optimization: A Beautiful Theory with Ugly Results
		5.1.3. Extensions of Mean-variance Optimization
			5.1.3.1. Mean-variance with Shrinkage
			5.1.3.2. Black-Litterman Optimization
			5.1.3.3. Minimum-variance Portfolio
	5.2. FROM MEAN-VARIANCE TO RISK PARITY
		5.2.1. How Inefficient Is 1/N?
		5.2.2. Naive 1/N, Minimum-variance, or Equal Risk?
		5.2.3. Standard Risk Parity: Equal Risk Contribution
		5.2.4. Adaptive Optimal Risk Budgeting
		5.2.5. Diversification Across Time with Volatility Targeting
	5.3. KEY TAKEAWAYS
CHAPTER 6: Advanced Portfolio Construction
	6.1. PORTFOLIO MANAGEMENT WITH MACHINE LEARNING
		6.1.1. HRP: Hierarchical Risk Parity
		6.1.2. MHRP: Modified Hierarchical Risk Parity
		6.1.3. Beyond MHRP—Denoising Correlation Matrices
	6.2. CUTTING EDGE PORTFOLIO MANAGEMENT APPROACHES
		6.2.1. Mean-variance Efficiency for Large Portfolios
		6.2.2. Robust Portfolio Choice
	6.3. INTERESTING NUGGETS
		6.3.1. Bayesian Risk Parity
		6.3.2. Empirical Bayesian Approach of Michaud
		6.3.3. Portfolio Contribution with Funding Ratios
		6.3.4. Investing in a Low-yield Environment
	6.4. KEY TAKEAWAYS
CHAPTER 7: Expert Hedge Fund Managers
	7.1. TREND FOLLOWING WITH KATY KAMINSKI
	7.2. MACHINE LEARNING WITH KAI WU
	7.3. EMERGING MARKETS AND SUSTAINABILITY INVESTING WITH ASHA MEHTA
	7.4. KEY TAKEAWAYS
CHAPTER 8: Expert Hedge Fund Investors
	8.1. QUANTITATIVE AND QUALITATIVE MANAGER SELECTION WITH ADAM DUNCAN
	8.2. QUANTITATIVE PORTFOLIO MANAGEMENT WITH CHRISTOPHE L’AHELEC
	8.3. KEY TAKEAWAYS
CHAPTER 9: Inclusion and Diversity
	9.1. PROMINENCE OF FEMALE HEDGE FUND MANAGERS WITH MEREDITH JONES
	9.2. GENDER GAP IN FINANCIAL ACADEMIA WITH MILA GETMANSKY SHERMAN
	9.3. KEY TAKEAWAYS
Conclusion
APPENDIX A: Manager Selection and Hedge Fund Factors
	A.1. FRAMEWORK OF MOLYBOGA, BILSON, AND BAEK
	A.2. FACTOR SELECTION
APPENDIX B: Performance Persistence
	B.1. BOOTSTRAPPING
	B.2. FALSE DISCOVERY
	B.3. PERFORMANCE EVALUATION WITH NOISE REDUCED ALPHA
	B.4. PERFORMANCE EVALUATION WITH SEEMINGLY UNRELATED ASSETS
	B.5. IDENTIFYING HEDGE FUND SKILL WITH PEER COHORTS
	B.6. COMBINING QUANTITATIVE AND QUALITATIVE FACTORS WITHIN A BAYESIAN FRAMEWORK
APPENDIX C: From Mean Variance to Risk Parity
	C.1. SHRINKAGE ESTIMATORS
	C.2. BLACK-LITTERMAN OPTIMIZATION
	C.3. HOW INEFFICIENT IS 1/N?
	C.4. NAIVE 1/N, MINIMUM-VARIANCE, OR EQUAL RISK?
	C.5. RISK PARITY
		C.5.1. Standard Risk Parity: Equal Risk Contribution
		C.5.2. A General Equal Risk Contribution Approach
		C.5.3. Risk Parity with Drawdowns
	C.6. ADAPTIVE OPTIMAL RISK BUDGETING
	C.7. DIVERSIFICATION ACROSS TIME WITH VOLATILITY TARGETING
APPENDIX D: Advanced Portfolio Construction
	D.1. DENOISING CORRELATION MATRICES
	D.2. MEAN-VARIANCE EFFICIENCY FOR LARGE PORTFOLIOS
	D.3. ROBUST PORTFOLIO CHOICE
	D.4. BAYESIAN RISK PARITY
Glossary
Notes
	Chapter 1. Introduction to Hedge Funds
	Chapter 2. Hedge Fund Research and Data
	Chapter 3. Manager Selection and Hedge Fund Factors
	Chapter 4. Performance Persistence
	Chapter 5. From Mean-Variance to Risk Parity
	Chapter 6. Advanced Portfolio Construction
	Chapter 7. Expert Hedge Fund Managers
	Chapter 9. Inclusion and Diversity
	Appendix A. Manager Selection and Hedge Fund Factors
	Appendix B. Performance Persistence
	Appendix C. From Mean Variance to Risk Parity
	Appendix D. Advanced Portfolio Construction
Index