Advanced guidance for institutional investors, academics, and researchers on how to manage a portfolio of private capital funds
The Art of Commitment Pacing: Engineering Allocations to Private Capital provides a much-needed analysis of the issues that face investors as they incorporate closed ended-funds targeting illiquid private assets (such as private equity, private debt, infrastructure, real estate) into their portfolios. These private capital funds, once considered ‘alternative’ and viewed as experimental, are becoming an increasingly standard component of institutional asset allocations.
However, many investors still follow management approaches that remain anchored in the portfolio theory for liquid assets but that often lead to disappointing results when applied to portfolios of private capital funds where practically investors remain committed over nearly a decade.
When planning for such commitments, investment managers and researchers are faced with practical questions such as:
- How to measure and control the real exposure to private assets?
- How to forecast cash-flows for commitments to private capital funds?
- What ranges for their returns and lifetime are realistic, and how can the investor’s skill be factored in?
- Over which dimensions should a portfolio be diversified and how much diversification is enough?
- How can the impact of co-investments or secondaries be modelled?
- How to design pacing plans that lead to resilient and efficient portfolios?
- What stress scenarios should be considered and how can they be applied?
These are just examples of the many questions for which answers are provided. The Art of Commitment Pacing describes established and new methodologies for building up and controlling allocations to such investments. This book offers a systematic approach for building up and controlling allocations to such investments.
The Art of Commitment Pacing is a valuable addition to the libraries of investment managers, as well as portfolio and risk managers involved in institutional investment. The book will also be of interest to advanced students of finance, researchers, and other practitioners who require a detailed understanding of forecasting and portfolio management methodologies.
Table des matières
Acknowledgments xiii
Chapter 1 Introduction 1
Scope of the book 1
Quick glossary 2
The challenge of private capital 2
Risk and uncertainty 3
Why do we need commitment pacing? 4
Illiquidity 4
The siren song of the secondary market 4
How does commitment pacing work? 5
Significant allocations needed 7
Multi‐asset‐class allocations 8
Intra‐asset‐class diversification 8
Engineering a resilient portfolio 9
Organisation of the book 10
Chapter 2 Institutional Investing in Private Capital 15
Limited partnerships 15
Structure 16
Criticism 18
Costs of intermediation 18
Inefficient fund raising 18
Addressing uncertainty 19
Conclusion 19
Chapter 3 Exposure 21
Exposure definition 21
Layers of investment 23
Net asset value 23
Undrawn commitments 24
Commitment risk 24
Timing 24
Classification 25
Exposure measures – LP’s perspective 25
Commitment 26
Commitment minus capital repaid 26
Repayment‐age‐adjusted commitment 27
Exposure measures – fund manager’s perspective 28
Ipev Nav 28
IPEV NAV plus uncalled commitments 29
Repayment‐age‐adjusted accumulated contributions 30
Summary and conclusion 31
Chapter 4 Forecasting Models 37
Bootstrapping 37
Machine learning 38
Takahashi–Alexander model 40
Model dynamics 40
Strengths and weaknesses 46
Variations and extensions 47
Stochastic models 49
Stochastic modelling of contributions, distributions, and NAVs 49
Comparison 50
Conclusion 51
Chapter 5 Private Market Data 53
Fund peer groups 53
Organisation of benchmarking data 53
Bailey criteria 54
Data providers 55
Business model 55
Public route 55
Voluntary provision 56
Problem areas 56
Biases 57
Survivorship bias 57
Survivorship bias in private markets 58
Impact 58
Conclusion 59
Chapter 6 Augmented TAM – Outcome Model 61
From TAM to stochastic forecasts 61
Use cases for stochastic cash‐flow forecasts 62
Funding risk 62
Market risk 65
Liquidity risk 65
Capital risk 66
Model architecture 66
Outcome model 67
Pattern model 67
Portfolio model 68
System considerations 68
Semi‐deterministic TAM 68
Adjusting ranges for lifetime and TVPI 70
Ranges for fund lifetimes 71
Ranges for fund TVPIs 73
Picking samples 76
Constructing PDF for TVPI based on private market data 78
A1*TAM results 82
Chapter 7 Augmented TAM – Pattern Model 85
A2*tam 86
Reactiveness of model 86
Model overview 87
Changing granularity 89
Injecting randomness 89
Setting frequency of cash flows 90
Setting volatility for contributions 92
Setting volatility for distributions 94
Scaling and re‐ picking cash‐ flow samples 94
Convergence A2*TAM to TAM 95
Split cash flows in components 97
Fees 98
Fixed returns 102
Cash‐ flow‐ consistent NAV 103
Principal approach 103
First contributions, then distributions 103
Forward pass 104
Backward pass 104
Combination 104
Summary 105
Chapter 8 Modelling Avenues into Private Capital 109
Primary commitments 109
Modelling fund strategies 110
Parameter as suggested by Takahashi and Alexander (2002) 110
Further findings on parameters 113
Basing parameters on comparable situations 113
Funds of funds 114
Secondary buys 114
Secondary FOFs 116
Co‐investments 118
Basic approach 118
Co‐investment funds 119
Syndication 119
Side funds 119
Impact on portfolio 120
Chapter 9 Modelling Diversification for Portfolios of Limited Partnership Funds 123
The LP diversification measurement problem 123
Fund investments 124
Diversification or skills? 124
Aspects of diversification 125
A (non‐ESG‐compliant) analogy 125
Commitment efficiency 126
Exposure efficiency 126
Outcome assessment 126
Diversifying commitments 127
Assigning funds to clusters 127
Diversification dimensions 128
Self‐proclaimed definitions 128
Market practices 128
The importance of diversification over vintage years 129
Other dimensions and their impact on risks 129
Include currencies? 130
Definitions 131
Styles 131
Classification groups 132
Style drifts 133
Robustness of classification schemes 133
Modelling vintage year impact 134
Commitment efficiency 135
Importance of clusters 135
Partitioning into clusters 136
Measurement approach 137
Remarks 139
Mobility barriers 139
Similarity is a measure for barriers to switching between classes 140
Similarity is not correlation 140
Is there an optimum diversification? 141
How many funds? 141
Costs of diversification 141
How to set a ‘satisficing’ number of funds? 143
Portfolio impact 143
Commitment efficiency timeline 143
Portfolio‐level forecasts 143
Appendix A – Determining similarities 145
Appendix B – Geographical similarities 146
Geographical diversification for private capital 146
Regional groups 146
Trade blocs 147
Transport way connection 148
Language barriers 148
Limits to geography as diversifier 148
Appendix C – Multi‐strategies and others 149
Appendix D – Industry sector similarities 149
Appendix E – Strategy similarities 149
Appendix F – Fund management firm similarities 150
Appendix G – Investment stage similarities 151
Appendix H – Fund size similarities 152
Chapter 10 Model Input Data 155
Categorical input data 155
Perceptions 156
Regulation 156
Risk managers 157
Can data be objective? 157
Moving from weak to strong data 158
Chapter 11 Fund Rating/Grading 161
Private capital funds and ratings 161
Fiduciary ratings 161
Fund rankings 162
Internal rating systems 162
Further literature 163
Private capital fund gradings 163
Scope and limitations 163
Selection skill model 164
Assumptions for grading 165
Prototype fund grading system 165
Ex‐ante weights 166
Expectation grades 166
Risk grades 169
Quantification 171
Chapter 12 Qualitative Scoring 173
Objectives and scope 173
Relevant dimensions 174
Investment style 175
Management team 176
Fund terms 177
Liquidity and exits 178
Incentive structure 178
Alignment and conflicts of interest 180
Independence of decision‐making 181
Viability 181
Confirmation 182
Scoring method 183
Tallying 183
Researching practices 184
Ex‐post monitoring 184
Assigning grades 185
Appendix – Search across several private market data providers 186
Interoperability 186
Matching 187
Chapter 13 Quantification Based on Fund Grades 191
Grading process 191
Quartiling 191
Quantiles 192
Quartiling 193
Approach 194
Example – how tall will she be? 195
Probabilistic statement 196
Controlling convergence 196
LP selection skills 198
Impact of risk grade 201
TVPI sampling 203
Chapter 14 Bottom- up Approach to Forecasting 205
Look‐ through 205
Regulation 205
Fund ratings 206
Look‐ through in practice 206
Bottom‐ up 207
Stochastic bottom‐ up models 207
Machine‐ learning‐ based bottom‐ up models 207
Overrides 208
Investment intelligence 208
Advantages and restrictions 208
Treatment as exceptions 209
Integration of overrides in forecasts by a top‐ down model 209
Probabilistic bottom‐ up 211
Expert knowledge for probability density functions? 212
Estimating ranges 212
Combining top‐ down with bottom‐ up 214
Chapter 15 Commitment Pacing 217
Defining a pacing plan 217
Pacing phases 218
Ramp‐up phase 219
Maintenance phase 219
Ramp‐down phase 220
Controlling allocations 221
Simulating the pacing plan 221
Ratio‐based commitment rules 222
Dynamic commitments 222
Pacing plan outcomes 222
‘Slow and steady’ 223
Accelerated pacing plan 223
Liquidity constraints 224
Impact on cash‐flow profile 224
Impact of commitment types 225
Maintenance phase 228
Recommitments 229
Target NAV 229
Cash‐flow matching 230
Additional objectives and constraints 231
Commit to high‐quality funds 231
Achieve intra‐asset diversification 231
Minimise opportunity costs 233
Satisficing portfolios 233
Conclusion 234
Chapter 16 Stress Scenarios 235
Make forecasts more robust 235
Communication 235
Specific to portfolio 236
Impact of ‘Black Swans’ 236
Interest rates and inflationary periods 237
Modelling crises 238
Delay of new commitments 238
Changes in contribution rates 238
Changes in distributions 239
NAV impact and secondary transactions 240
Lessons 240
Building stress scenarios 241
Market replay 241
Varying outcomes 242
Foreign exchange rates 244
Varying portfolio dependencies 244
Increasing and decreasing outcome dependencies 244
Increasing and decreasing cash‐flow dependencies 247
Blanking out periods of distributions 247
Varying patterns 248
Stressing commitments 249
Extending and shortening of fund lifetimes 250
Front‐loading and back‐loading of cash flows 251
Foreign exchange rates and funding risk 251
Increasing and decreasing frequency of cash flows 253
Increasing and decreasing volatility of cash flows 254
Conclusion 256
Chapter 17 The Art of Commitment Pacing 259
Improved information technology 259
Direct investments 260
Use of artificial intelligence 260
Risk of private equity 261
Securitisations 261
Judgement, engineering, and art 262
Abbreviations 263
Glossary 267
Biography 275
Bibliography 277
Index 289
A propos de l’auteur
THOMAS MEYER, is the co-author of Beyond the J Curve (translated into Chinese, Japanese, and Vietnamese), J Curve Exposure, Mastering Illiquidity (all by Wiley), and two CAIA books, which are required reading for Level II of the Chartered Alternative Investment Analyst ® Program. He authored Private Equity Unchained (by Palgrave Mac Millan).
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