Table of Contents
Preface xi
1 Introduction 1
1.1 The Fourth Industrial Revolution 2
1.2 Unhealthy Myths 3
1.3 New Regulatory Framework 4
1.4 Defining a Road Map 5
1.4.1 Nature of Financial Services 5
1.4.2 What Al and FinTech Cannot Accomplish 6
2 Navigation and Vocabulary 9
2.1 Use Case 9
2.2 Platform Navigation 10
2.2.1 Investment Categories 10
2.2.2 Product Attributes 12
2.2.3 Long and Short Exposure 12
2.2.4 Portfolio Gauges 12
2.2.5 Product Statistics 22
I Construct Portfolios 25
3 Understanding Risk 27
3.1 Use Case 27
3.2 A Brief History of Risk Management 28
3.2.1 Evolving from Insurance to Risk Management 28
3.3 Extreme Risk Measures 29
3.4 Related Risk Modeling Techniques 32
3.4.1 Fat Tails 32
3.4.2 Uniform Margins 34
3.4.3 Arrival Times 34
3.4.4 Empirical Observations 35
4 Objective Functions in Portfolio Construction 40
4.1 Use Case 40
4.2 Seven Objective Functions 41
4.2.1 Minimum Absolute Residual 41
4.2.2 Minimum Variance 41
4.2.3 Minimum Peak-to-Trough MDD 42
4.2.4 Minimum 95% Value-at-Risk 44
4.2.5 Minimum 95% Conditional Value-at-Risk 46
4.2.6 Maximum Sharpe Ratio 48
4.2.7 Maximum Alternative Sharpe Ratio 49
4.2.8 Assumption 51
5 Risk and Return Attribution 52
5.1 Use Case 52
5.1.1 A Graph to Illustrate the Point 52
5.2 Risk Attribution 53
5.3 Ex-Ante Return Attribution 54
5.4 Difference between Return Attribution & Risk Attribution 60
5.5 Conclusion 60
6 Portfolio-Level Factor Analysis 61
6.1 Use Case 61
6.2 Portfolio-Level Factor Exposure 62
6.3 Conclusion 64
7 A Hedging Use Case 66
7.1 Use Case 66
7.1.1 Controlling Extreme Risks through Volatility Derivatives 67
7.2 Methodology 68
7.2.1 VIX Futures 68
7.2.2 Variance Futures 71
7.2.3 OTM Put Options on SPX 80
7.3 Hedging Performance 85
7.3.1 1-Month VIX Futures 85
7.3.2 3-Month Variance Futures 88
7.3.3 1-Month OTM Put Options on SPX 91
7.4 Overall Comparison of Choices of Objective Functions 98
7.5 Step-by-Step Walk Through 100
II Select Assets 105
8 Alpha Selection Using Factors 107
8.1 Use Case 107
8.2 Methodology 108
8.2.1 Balance Sheet 101 108
8.2.2 Fundamental Factors 109
8.3 Factors 119
8.3.1 Compound Factors 119
8.3.2 Factor Set Definition 121
8.4 Statistical Criteria 123
8.5 Implementation 124
8.5.1 Reviewing Fundamental Data 126
8.5.2 Default Settings 128
9 Standard Derivative Instruments 129
9.1 Use Case 129
9.2 Options Pricing Model 129
9.2.1 Options Implied Volatility 130
9.3 Interest Rate Term Structure 132
9.4 Commodity Term Structure 132
III Decide and Execute 135
10 Rebalancing 137
10.1 Use Case 137
10.2 Goals in a Typical Portfolio Rebalancing Process 137
10.3 Methodology for Capital Adequacy 140
10.3.1 SCR Ratio and MCR Calculation 140
10.3.2 Risk Modules 140
11 Forward Scenarios and Historical Simulations 145
11.1 Use Case 145
11.2 Forward-Looking Scenarios 146
11.3 Historical Simulation 149
12 Combining Upside with Black Swan Scenarios 151
12.1 Use Case 151
12.1.1 Defining the Investment Problem 152
12.1.2 Potential Scenarios on Watch 153
12.1.3 Traditional Approach 153
12.1.4 Stochastic Analysis Solution 154
12.1.5 Outcome 155
12.2 Methodology 156
12.2.1 Objective 156
12.2.2 Overview 157
12.2.3 Formula 157
12.2.4 Computational Process 158
12.3 Worked Example 160
12.3.1 Overview 160
12.3.2 Definitions 160
12.4 Conclusion 170
IV Deliver Reports 171
13 Customary Back Office Reporting 173
13.1 Use Case 173
13.2 Investment Reports 173
13.2.1 Investor Summary 174
13.2.2 Transactions 176
13.2.3 Consolidated Positions 177
13.2.4 Portfolio Summary 177
13.2.5 Profit and Loss 178
13.2.6 Allocation 179
13.2.7 Net Asset Value 180
13.2.8 Portfolio Statistics 184
13.2.9 Risk and Return 185
13.2.10 Correlation 187
13.2.11 Exposures 188
13.2.12 Aggregated Reports 188
14 Additional Reporting 194
14.1 Use Case 194
14.2 Maintenance and Accounting Reports 194
14.2.1 Custom Benchmark 195
14.2.2 Product Benchmark Mapping 198
14.2.3 Accounting Details 201
14.2.4 Subscription Redemption Details 202
14.2.5 Transactions 205
15 Compliance Analysis 208
15.1 Use Case 208
15.2 Monitoring Compliance Rules 209
16 Data Integrity Validation 212
16.1 Use Case 212
16.2 Defining Data Integrity 212
16.2.1 A Practical Example 213
16.3 Standard Data Integrity Tests 216
16.4 Mitigation Methods 218
16.4.1 Sample Algorithm to Fill Missing Data: Expectation-Maximization 218
16.4.2 Sample Treatment of Outliers and Influential Cases 219
16.4.3 Sample Data Integrity Validation Process 220
16.5 Conclusion 221
V Deploy 223
17 Deployment Best Practices 225
17.1 Use Case 225
17.2 Dashboard for Investment Teams 226
17.3 API for End-Investor Access 230
17.4 Management Approval Panel 232
18 Implications of a Post-IA+AI Society 234
18.1 Winners and Losers 235
18.2 Enlarging the Overall Pie in the Fight against Poverty 235
18.3 Changing Global Asset Management Landscape 236
18.4 More Frauds Initially Until Robust Solutions Stand Out 237
18.5 Steady-State Outcomes 238
18.5.1 How may the Steady-State Outcome Impact the Industry? 238
18.5.2 How may the Steady-State Outcome Manifest in Time? 239
18.5.3 How may the Steady-State Outcome Manifest Geographically? 241
18.6 Final Conclusion 243
Bibliography 245
Index 249
