Welcome to the secretive club of modern hedge funds, where important players in the world of investing and capital markets have invested close to $4 trillion globally. If you're intrigued by the inner workings of hedge funds, investment techniques and technologies they use to source investment alpha, this book is for you. Focusing on the author's three decades of trading experience at leading banks and hedge funds, it covers both discretionary and computer-driven strategies and perspectives on AI-based and quantamental investing using new alternative data, which includes numerous examples and insights of real trades and investment strategies. No mathematical knowledge is required, with the relevant algorithms detailed in the appendices. Discretionary investing details equity and credit investing across the corporate capital structure. Through trading equities, bonds and loans, event-driven trades can target profitable special situations and relative value opportunities. Systematic trading involves computer-driven strategies derived from a scientific and statistical analysis of liquid markets. The investment strategies of both commodity trading advisors (CTAs) and long/short equity funds are detailed, from trend-following to factor-based approaches. AI investing is fashionable but does the reality for hedge funds correspond to the AI hype present in other non-financial domains? AI using neural nets and other machine learning techniques are outlined along with their practical application in regards to investing. Quantitative Hedge Funds also discusses environmental, social and governance (ESG) investing, which has rapidly evolved as the public and institutions demand solutions to global problems such as climate change, pollution and unethical labour practices. ESG investment strategies are migrating out of the long-only space and into hedge funds. Finally, the advent of big data has led to multiple alternative datasets available for hedge fund managers. The integration of alternative data into the investment process is discussed, together with the rise of so-called quantamental investing, a hybrid of the best of human skill and computer-based technologies.
Author(s): Richard D. Bateson
Publisher: World Scientific Publishing
Year: 2022
Language: English
Pages: 287
City: London
Contents
Preface
About the Author
Guide to Acronyms
Glossary of Notations
Chapter 1 Efficient Markets
1.1 Introduction
1.2 Brownian Motion
1.3 The Efficient Markets Hypothesis
1.4 The Black–Scholes Equation
1.5 Interest Rate Exotics
1.6 The Derivatives Ideology
1.7 Credit Derivatives
1.8 The Normal Copula
1.9 Equity Correlation
1.10 Structured Credit Trading
1.11 Towards Real Markets
Chapter 2 Real Markets
2.1 The Federal Cavalry
2.2 Once “Greed was Good”
2.3 The Great Japan Crash
2.4 Black Monday
2.5 George’s Black Wednesday
2.6 Tequila and Tigers
2.7 The Russian GKO Bust
2.8 Too Big to Fail
2.9 The .Con Bubble
2.10 The Lehman Moment
2.11 Taper Tantrums
2.12 The Corona Crisis
2.13 “The Best of All Possible Worlds”
2.14 Will Chaos Theory Rule?
Chapter 3 Discretionary Adventures
3.1 Catalyst Trading
3.2 Classifying Event Driven Trades
3.3 Corporate Capital Structure
3.4 The Instruments of Torture
3.4.1 Equities
3.4.2 Corporate bonds
3.4.3 Credit default swaps
3.4.4 Leveraged loans
3.5 Leverage and Financing
3.5.1 Prime brokers
3.5.2 Repo market
3.5.3 Total return swaps
3.6 Trading Across the Capital Structure
3.7 Capital Structure Trades
3.7.1 Execution, carry and narrative
3.7.2 The GM debacle
3.7.3 The Maytag short
3.8 Merger and LBO Trades
3.9 Credit Curve Trades
3.9.1 Notional weighted curve trades
3.9.2 Duration weighted curve trades
3.9.3 Practical curve trades
3.9.4 HCA curve steepener
3.9.5 UPC curve flattener
3.10 Loan Arbitrage
3.10.1 INEOS loan arbitrage
3.11 Basis Trades
3.11.1 Supervalue basis trade
3.12 Portfolio Construction
3.12.1 Liquidity considerations
3.12.2 Funding constraints
3.12.3 Profit targets
3.12.4 Mind the skew
3.13 Risk Management
3.13.1 VaR models
3.13.2 Stress testing and scenario analysis
3.13.3 Real portfolio risk
3.14 The Old School Approach
Chapter 4 Systematic Profits
4.1 Systematic Investing
4.2 The Main Types of Systematic Hedge Funds
4.2.1 Commodity trading advisors
4.2.2 L/S equity
4.3 Overview of a Typical CTA Trading System
4.4 Systematic Strategy Types
4.4.1 Momentum
4.4.2 Reversion
4.4.3 Carry
4.4.4 Value
4.4.5 Cross-sectional
4.4.6 Timing
4.4.7 Fundamental
4.4.8 Technical analysis
4.4.9 Volatility
4.4.10 Spread trading
4.5 Multi-Market Diversification
4.6 Multi-Strategy Diversification
4.7 Practical Aspects of Systematic Trading
4.8 Strategy Development
4.8.1 Strategy conception
4.8.2 System development
4.8.3 Backtesting
4.8.4 Performance evaluation
4.8.5 Internal review
4.8.6 Live-testing
4.9 Key Issues with Developing Trading Strategies
4.9.1 Over-fitting and feedback
4.9.2 Regime change
4.9.3 Transaction cost modelling
4.9.4 Risk-scaling
4.9.5 Human discipline
4.10 Good versus Bad Strategies
Chapter 5 The Factor Game
5.1 Equity Factor Investing
5.1.1 The capital asset pricing model
5.1.2 The Fama–French three-factor model
5.1.3 The Carhart four-factor model
5.1.4 A flood of factors
5.2 Review of the Big 5 Factors
5.2.1 The market beta factor
5.2.2 The size factor
5.2.3 The value factor
5.2.4 The momentum factor
5.2.5 The quality factor
5.3 Investing with Factors
5.4 A Reversion Factor?
5.5 The Blunderbuss Approach
5.6 The Evolution of Factors
Chapter 6 AI Again
6.1 AI Revisited
6.2 Classifying Cats and Dogs
6.3 Machine Learning for Hedge Funds
6.4 The Growth and Death of AI Funds
6.5 Von Neumann’s Elephant
6.6 One Financial History
6.7 Machine Learning Techniques
6.7.1 Algorithmic indifference?
6.8 Artificial Neural Networks
6.8.1 From perceptrons to neural nets
6.8.2 Training neural nets
6.8.3 Over-fitting markets
6.8.4 Routes to success
6.9 The Simplest ML Technique — kNN
6.9.1 Applying kNN to L/S equities
6.10 Comparison with Traditional Systematic Investing
6.10.1 Strategy conception
6.10.2 Signal combination
6.10.3 The strategy algorithm
6.10.4 Market adaptation
6.10.5 Model transparency
6.11 The Future of Machine Learning for Investing
Chapter 7 ESG Investing
7.1 The Ethics of Don Draper
7.2 An Unsustainable Forest of Sustainability Jargon
7.3 What are the E, S and G?
7.4 Arctic Drilling but No Beers Please, Especially at the Casino
7.5 The Minefield of ESG Ratings
7.5.1 Back to the future with the agencies
7.5.2 Weak correlations
7.5.3 The disclosure bias
7.5.4 Manageable versus unmanageable risks
7.5.5 Responsible managers and curious investors
7.6 ESG Investment Strategies
7.6.1 Ratings based investing
7.6.2 Discretionary versus systematic ESG
7.7 Does ESG Investing Work?
Chapter 8 Towards Quantamental
8.1 Nowcasting
8.2 Alternative Data
8.3 Types of Alternative Data
8.3.1 From individuals
8.3.2 From companies
8.3.3 From sensors
8.3.4 From the economy
8.3.5 From brokers
8.4 Integrating Alternative Data into the Investment Process
8.4.1 Selection and onboarding of datasets
8.4.2 Structured versus unstructured data
8.4.3 Data coverage and history
8.4.4 Dataset alpha determination
8.5 The Future is Quantamental
Appendix A Efficient Markets
A.1 Modern Portfolio Theory
A.2 Brownian Stock Prices
A.3 Ito’s Lemma
A.4 The Black–Scholes Differential Equation
A.5 The Black–Scholes Option Pricing Equation
A.6 Ergodic Processes
A.7 The St. Petersburg Paradox
A.8 Discount Factors, Zero Rates and Forward Rates
A.9 The Hull–White Model
A.10 Chooser Notes
A.11 Knock-in Reverse Convertibles
A.12 Equity Worst-of Options
A.13 Pulsar Protected Notes
Appendix B Discretionary Adventures
B.1 The Gordon Growth Model
B.2 A Brief Glossary of Corporate Events
B.2.1 Corporate mergers
B.2.2 Spin-offs
B.2.3 Private sale
B.2.4 Rights issue
B.2.5 Initial public offering
B.2.6 Company restructuring
B.2.7 Balance sheet re-leveraging
B.2.8 Balance sheet deleveraging
B.2.9 LBOs and MBOs
B.2.10 Potential LBOs
B.2.11 Share buybacks
B.2.12 Acquisitions
B.2.13 Special dividend recap
B.2.14 Distressed
B.3 Present Valuing Cashflows
B.4 Bond Yields
B.5 Floating Rate Notes
B.6 Par Asset Swap
B.7 The Stochastic Default Model
B.8 The Reduced Form Model
B.9 Credit Event Definitions
B.10 Pricing Credit Default Swaps
B.11 Normal Copula Model for Correlated Default Times
B.12 Credit Curve Trades
B.13 Sharpe Ratio
B.14 Value-at-Risk
Appendix C Systematic Profits
C.1 Generic Signal Methodology
C.1.1 Signal Z-scores
C.1.2 Combining signals
C.1.3 Signal response functions
C.1.4 Position risk-scaling
C.2 Basic Time Series Manipulation
C.2.1 Simple moving average
C.2.2 Exponentially weighted moving average
C.2.3 Exponentially weighted volatility
C.3 Fundamental Signals
C.3.1 Output gap
C.3.2 Phillips curve
C.3.3 Taylor rule
C.4 CTA Momentum Signal
C.4.1 Momentum using kernels
C.5 CTA Carry Signal
C.6 CTA Value Signal
C.7 CTA Credit Trading
C.8 CTA Spread Trading
C.9 Execution and Slippage
C.9.1 Empirical market impact equation
Appendix D The Factor Game
D.1 Alpha and Beta
D.2 Capital Asset Pricing Model
D.3 Arbitrage Pricing Theory
D.4 The Fama–French Three-Factor Model
D.5 The Cahart Four-Factor Model
D.6 A Quality Definition
D.7 Joel Greenblatt’s “Magic Formula” Investing
D.8 Implied Volatility Factors
D.9 Statistical Arbitrage
D.9.1 Distance method
D.9.2 Cointegration method
D.9.3 Copula method
D.10 Principal Components Analysis
Appendix E AI Again
E.1 Machine Learning Basic Definitions
E.2 Linear Regression Model
E.3 The Perceptron
E.4 kNN Methodology
E.5 Granger Causality
Further Reading
Index
