Indices, Index Funds And ETFs (eBook)

Michael I. C. Nwogugu is an author, entrepreneur, and consultant who has held senior management and Board-of-Director positions in companies in both the U.S. and Nigeria. Mr. Nwogugu has written three books: Risk in the Global Real Estate Market (Wiley); Illegal File-sharing Networks, Digital Goods Pricing and Decision Analysis (CRC Press); and Anomalies In Net Present Value, Returns And Polynomials And Regret Theory In Decision Making (Palgrave MacMillan). Mr. Nwogugu’s research articles have been cited in top academic journals such as International Journal of Approximate Reasoning; Applied Mathematics & Computation; Journal of Business Research; European Journal of Operational Research; PNAS; Annual Review of Psychology; Neural Computing & Applications; Mathematical Methods of Operations Research; Computers & Industrial Engineering; and Expert Systems With Applications among others. Mr. Nwogugu earned degrees from the University of Nigeria; CUNY, New York, USA; and Columbia University, New York, USA.

Contents 5
List of Tables 21
Chapter 1: Introduction 23
1.1 How This Book Differs from Other Books About ETFs, Indices and Index Funds 30
1.2 Regulatory Failure, Regulatory Capture and Regulatory Fragmentation 31
1.3 Some Mathematical Commonalities Among Debt, Equity and Commodity Indices 32
1.4 The Chapters: Activity Theory and HCI 33
1.5 Momentum Effects, Systemic Risk and Financial Instability 36
1.6 The Usefulness of Alpha and Beta as Currently Construed and the Debate About Active Management Versus Passive Management
1.7 ETFs Versus Mutual Funds Versus Closed-End Funds 43
1.8 The Case-Shiller Real Estate Indices Are Very Inaccurate and Misleading 44
1.9 Tax Aspects of Investing in ETFs, Index-Based ETNs and Index Funds 44
1.10 Forecasting of Stock Indices and ETFs 45
1.11 Network Analysis in Stock Indices and ETFs 45
1.12 Some Public Health and Social/Economic Sustainability Problems (Including Climate Change and Harmful Technological Innovation) Inherent in the Use of Financial Indices and Index Products (Index Funds, ETFs, Index Futures/Options and Index ETNs) 45
Bibliography 48
Chapter 2: Number Theory, “Structural Biases” and Homomorphisms in Traditional Stock/Bond/Commodity Index Calculation Methods in Incomplete Markets with Partially Observable Un-aggregated Preferences, MN-Transferable-Utilities and Regret–Minimization Regi 63
2.1 Existing Literature 66
2.2 MN-Transferable Utility 72
2.3 The ICAPM/CAPM Are Inaccurate 76
2.4 The Traditional Index Calculation Methods (Applicable to Many Stock/Equity, Debt, Real Estate, Commodity and Currency Indices) 77
2.4.1 Market-Capitalization Weighted Indices (and “Diversity” Indices) 78
2.4.2 Free Float Adjusted Indices 82
2.4.3 Fundamental Indices 83
2.4.4 Stock-Price-Weighted Indices 87
2.4.5 Trading-Volume Weighted Indices 88
2.4.6 Market-Cap Weighted and Volume-Weighted Indices (Two Methods Combined) 90
2.4.7 Dividend-Weighted Indices 91
2.4.8 Equal-Weight Indices 91
2.4.9 Thomson Reuters’s Indices 95
2.5 Other Distortions in Traditional Indices 95
2.6 Green Bonds Indices: A Combination of Market-Value Weighting and Fundamental Weighting 99
2.7 Bloomberg Barclays Bond Indices (Including the “Bloomberg Barclays Global Aggregate Bond Index”): Combination of “Market-Value” and “Fundamental” Weighting 101
2.8 The S& P Dow Jones Fixed Income Index Methodology (Combinations of “Market-Value” and “Fundamental” Weighting)
2.9 The S& P Global Carbon Efficient Indices
2.10 The Standard & Poor’s-Goldman Sachs Index (S&
2.11 The Bloomberg Commodity Index Family (Including the Bloomberg Commodity Index “BCOM”) 108
2.12 The ICE BofAML Commodity Index Extra (MLCX Family of Commodity Indices) 110
2.13 Traditional Index Calculation Methods Create Significant Incentives for Companies to Perpetrate Earnings Management, “Asset-Quality Management” and “Incentive-Effects Management” 111
2.14 Conclusion 117
Bibliography 118
Chapter 3: A Critique of Credit Default Swaps (CDS) Indices 132
3.1 Existing Literature 133
3.2 “Quasi-Default” Versus Reported Default: The Difference Reduces the Usefulness of CDS Indices 134
3.3 The Credit Ratings Lag 137
3.4 The Methods for Pricing of Debt Reduces the Accuracy of CDS Indices 138
3.5 Behavioral Effects and Externalities Inherent in the Use of CDSs, and Which May Distort the Accuracy of CDS-Indices 139
3.6 Financial Instability and Systemic Risk 140
3.7 The S& P CDS Indices
3.8 CDSs are Inefficient, Unethical and Probably Illegal 145
3.9 Conclusion 153
Bibliography 153
Chapter 4: Invariants and Homomorphisms Implicit in, and the Invalidity of the Mean-Variance Framework and Other Causality Approaches: Some Structural Effects 159
4.1 Existing Literature 160
4.2 The Mean–Variance Framework Is Inaccurate 162
4.3 Implications for Systems Science and Reliability Engineering: Invalidity of Global Sensitivity Indices and Sobol Indices 190
4.4 Conclusion 191
Bibliography 191
Chapter 5: Decision-Making, Sub-additive Recursive “Matching” Noise and Biases in Risk-Weighted Stock/Bond Commodity Index Calculation Methods in Incomplete Markets with Partially Observable Multi-attribute Preferences 196
5.1 Existing Literature 197
5.2 The ICAPM/CAPM Is Inaccurate 201
5.3 The Risk-Adjusted Index Calculation Methods Are Wrong 204
5.3.1 Free-Float Adjusted Indices 204
5.3.2 Equal Risk Contribution (ERC) Indices 205
5.3.3 “Most-diversified” (“Diversity”) Indices 207
5.3.4 “Minimum-Variance” Indices 209
5.3.5 FTSE/EDHEC Risk-Adjusted Indices 211
5.3.6 The Hang Seng Risk-Adjusted Indices 215
5.3.7 The S& P Risk-Control Index Series: S&
5.3.8 The Thomson Reuters Lipper Optimal Target Risk Indices 224
5.3.9 MSCI Factor Indices 225
5.3.10 MSCI Risk-Weighted Indices 227
5.3.11 The Dow Jones Relative-Risk Indices 228
5.3.12 The Dow Jones RPB Indices 237
5.3.13 The FTSE StableRisk Index Series 237
5.3.14 The Minimum Correlation Indices 241
5.3.15 Risk Parity (RP) Indices 241
5.4 Conclusion 241
Bibliography 242
Chapter 6: Informationless Trading and Biases in Performance Measurement: Inefficiency of the Sharpe Ratio, Treynor Ratio, Jensen’s Alpha, the Information Ratio and DEA-Based Performance Measures and Related Measures 252
6.1 Existing Literature 253
6.2 CAPM/ICAPM/IAPT Are Inaccurate 260
6.3 Inherent Biases and Structural Effects That May Affect Performance Measures 260
6.4 Critical Assumptions, Noise and Error Inherent in Mean–Variance-Based Performance Measures 261
6.4.1 Error Assumption #1: All Investors Agree About the Risk and Expected Return for All Securities and All Investors Have the Same Investment Preferences Which Don’t Vary Over Any Time Interval
6.4.2 Error Assumption #2: All Investors Can Short-Sell All Securities Without Restriction 262
6.4.3 Error Assumption #3: All Investors Have the Same or Similar Investment Horizon, or Their Investment Decisions Don’t Consider Investment Horizons 262
6.4.4 Error Assumption #4: All Investors Don’t Pay Federal or State Income Taxes, or All Investors Have the Same Tax Rates 262
6.4.5 Error Assumption #5: There Are Minimal or No Transaction Costs, or All Investors Have the Same Transaction Costs 263
6.4.6 Error Assumption #6: The Investment Opportunity Set for All Investors Holding Any Security in the Index Is Restricted to the Securities in the Public Markets (or in the Specific Sub-market on Which the Index Is Based) 263
6.4.7 Error Assumption #7: All Investors Have the Same Marginal Rate of Substitution (of Assets in Their Portfolios) Which Is Constant Over Any Time Interval, and Which Has a Fixed and Directly Proportional Relationship with Their Total Investable/Li 263
6.4.8 Error Assumption #8: For All Investors, Total Wealth and Total Investable Wealth Are Perceived as the Same, and Both Have No Effect on Investors’ Perception of the Risk–Reward Ratio 264
6.4.9 Error Assumption #9: For All Investors, and for All Time Intervals, the Marginal Utility of Wealth Decreases as Total Wealth Increases 264
6.4.10 Error Assumption #10: All Investors Have Positive Total Wealth and Positive Total Investable Wealth in Any Time Interval 265
6.4.11 Error Assumption #11: For Every Investor, the Risk–Reward Trade-Off Is a More Important Investment Criteria Than the Absolute Magnitude of Returns, and/or the Investor’s “Reference Point” (i.e. Cost of Capital, etc.) 265
6.4.12 Error Assumption #12: Bid–Ask Spreads Are Small and Don’t Affect Investors’ Decisions or the Calculation of Standard Deviations of Returns 266
6.4.13 Error Assumption #13: All Losses Produce Strictly Negative Utilities (Investors Don’t Gain Any Utility from Tax Loss Carry-Forwards) or Losses Don’t Have Any Utility and Don’t Cause Regret and There Is No Utility Derived from Merely Holding a
6.4.14 Error Assumption #14: For All Investors, the Non-monetary Utilities (Such as Hedging, Long-Term Security, etc.) that Arise from Investing Are Irrelevant 266
6.4.15 Error Assumption #15: All Investors Can Make Investments That Earn the Risk-Free Rate at All Times and for Any Amount of Capital 267
6.4.16 Error Assumption #16: The Returns of the Underlying Asset Have a Normal Distribution or a Quasi-Normal Distribution 267
6.4.17 Error Assumption #17: The Rate of Change of the Standard Deviation (?) with Respect to the Realized Return (??/?R) Is Constant for Any Time Interval During the Investment Horizon 267
6.4.18 Error Assumption #18: The Realized Return (R) of the Underlying Asset Is Constant Over Any Time Interval During the Investment Horizon and As T (Time) ? ?, ??/?R Is Constant
6.4.19 Error Assumption #19: As r ? ?, ??/?r Is Constant 268
6.4.20 Error Assumption #20: Any Correlation Between ? and r, or Between ?? and ?r, Is Irrelevant 268
6.4.21 Error Assumption #21: ICAPM/CAPM Are Valid 268
6.4.22 Error Assumption #22: There Is Continuous Trading and Portfolio Rebalancing 268
6.4.23 Error Assumption #23: There Are No Transaction Costs and There Are No Taxes 269
6.4.24 Error Assumption #24: There Are No Synthetic Securities and There Is No Hedging
6.4.25 Error Assumption #25: There Are No Framing Effects 269
6.4.26 Error Assumption #26: All “Risk-Free Assets” (Typically Treasury Securities) Are Truly Risk-Free The Risk-Free Rate Is Constant
6.4.27 Error Assumption #27: The Risk-Free Rate and Beta Remain Constant Over All Time Periods 270
6.4.28 Error Assumption #28: All Assets Have the Same “Duration” 270
6.4.29 Error Assumption #29: The Investor’s Investment Horizon Does Not Matter and the Changes in the Investor’s Preferences and Risk Tolerance Do Not Matter
6.4.30 Error Assumption #30: There Is No Inflation or Deflation 271
6.4.31 Error Assumption #31: Intertemporal Risk and Benefits Can Be Defined Solely in Terms of Standard Deviation, Mean Return, and Consumption 271
6.5 Other and More Recent Performance Measures 271
6.5.1 The “Tracking Error Volatility” and “Active Share” Measures 271
6.5.2 Alpha-TEV Frontier 274
6.5.3 Best Gain—Loss Ratio and The Substantial-Gain—Loss-Ratio (SGLR) 274
6.5.4 Generalized Sharpe Ratio 274
6.5.5 Modified Turnover (MT) 275
6.5.6 The Andreu et al. (2018) Performance Measure 275
6.6 Properties of a Manipulation-Proof Performance Measurement System 275
6.6.1 Goetzmann et al. (2007): Properties of a “Manipulation Proof Performance Measure” (“MPPM”) 275
6.6.2 New Properties of a Manipulation-Proof Performance System (MPPS) 278
6.7 Conclusion 278
Bibliography 279
Chapter 7: Anomalies in Taylor Series, and Tracking Errors and Homomorphisms in the Returns of Leveraged/Inverse ETFs and Synthetic ETFs/Funds 285
7.1 Inverse/Leveraged ETFs 288
7.1.1 Existing Literature 288
7.1.2 Some Biases and Problems Inherent in Leveraged ETFs and Inverse ETFs 296
7.1.2.1 There Cannot Be an “Optimal” Degree of Positive/Negative Leverage for Leveraged/Inverse ETFs 297
7.1.2.2 The Hill and Foster (2009) Study Is Misleading and Inaccurate 298
7.1.2.3 Compounding Can Have a Significant Effect on Leveraged/Inverse ETFs 299
7.1.2.4 Intraday Volatility Is Irrelevant and Only End-of-Day Prices Matter, the Co and Labuszewski (July 2012) Study Is Inaccurate, and Volatility Has Minimal Effects on the Downward Returns Bias 300
7.1.2.5 Portfolio Rebalancing by Investors That Own Leveraged/Inverse ETFs Is Not Always Feasible 306
7.1.2.6 The Effect of Underlying Indices 307
7.1.2.7 Leveraged/Inverse ETFs Are Highly Sensitive to Any Manipulation of End-of-Day Prices and to the Calculation of End-of-Day Prices 308
7.1.2.8 Changing Margin Requirements Will Not Be Very Helpful 310
7.1.2.9 Leveraged/Inverse ETFs Are Gambling Tools 310
7.1.2.10 There Are No Bases for Comparisons of Leverage/Inverse ETFs to Leveraged Companies (or Leveraged Mutual Funds) 311
7.1.2.11 Implied Portfolio Weights 311
7.1.2.12 The Inaccuracy of the Put–Call Parity Theorem, the Early Exercise Premia and the Structure of Leveraged/Inverse ETFs 312
7.1.2.13 Investors Can Replicate the Leverage/Inverse Effects More Cheaply and More Efficiently by Themselves 314
7.1.2.14 Risk–Return Trade-Off 316
7.1.2.15 Suitability and Disclosure 316
7.1.2.16 Manager-Risk Inherent in Leveraged/Inverse ETFs 317
7.2 Synthetic ETFs and Synthetic Funds 319
7.2.1 Existing Literature 319
7.2.2 Synthetic ETFs and Synthetic Index Funds 320
7.2.2.1 The Inaccuracy of the Put–Call Parity Theorem, the Early Exercise Premia and the Structure of Synthetic Funds/ETFs 320
7.2.2.2 Implied Portfolio Weights 324
7.2.2.3 Some Investors Can Create the Same Economic Effects/Benefits of Synthetic Funds/ETFs More Cheaply and More Efficiently by Themselves 325
7.2.2.4 Investment Horizon 328
7.2.2.5 Counterparty Credit Risk Interest Rate and Documentation Risk 328
7.2.2.6 Tracking Errors and Compounding and Their Effects on Synthetic Funds/ETFs 329
7.2.2.7 Changing Margin Requirements for Synthetic ETFs and Synthetic Index Funds Will Not Be Very Helpful 330
7.2.2.8 Intraday Volatility Is Irrelevant and Only End-of-Day Prices Matter, the Co and Labuszewski (July 2012) Study Is Also Inaccurate, and Volatility Has Minimal Effects on the Downward Returns Bias 330
7.2.2.9 The Effect of Underlying Indices 331
7.2.2.10 Synthetic Funds/ETFs Are Highly Sensitive to Manipulation and Calculation of End-of-Day Prices 331
7.2.2.11 Manager Risk Inherent in Synthetic Funds/ETFs 332
7.3 VIX-Based Leveraged and Inverse ETFs, and Exchange-Traded Notes (ETNs) 333
7.4 Buy-Write ETFs (Ordinary, Leveraged, Inverse, or Synthetic ETFs) 339
7.5 Long-Short ETFs and Synthetic Hedge Funds (Ordinary or Leveraged, or Inverse or Synthetic ETFs) 341
7.6 Synthetic Hedge Funds 344
7.7 Conclusion 345
Bibliography 345
Chapter 8: Human Computer Interaction, Misrepresentation and Evolutionary Homomorphisms in the VIX and Options-Based Indices in Incomplete Markets with Unaggregated Preferences and NT-Utilities Under a Regret Minimization Regime 357
8.1 Existing Literature 358
8.2 Regret-Minimization and MN-TU, and Options-Based Indices as Evolutionary Algorithms and Evolutionary Homomorphisms 364
8.3 Critique of Calculation Methods for Options-Based Indices 366
8.3.1 Call-Write Indices 366
8.3.2 The CBOE Put-Write Indices 374
8.3.3 The RUT Options Indices 383
8.3.4 The Thomson Reuters “Realized Volatility Index” 384
8.3.5 The VIX Volatility Index, and Similar Volatility Indices in Other Countries 385
8.3.6 Other Options-Based Indices That Are Based on the US VIX Model 401
8.4 Conclusion 401
Bibliography 402
Chapter 9: Human–Computer Interaction, Incentive-Conflicts and Methods for Eliminating Index Arbitrage, Index-Related Mutual Fund Arbitrage and ETF Arbitrage 414
9.1 Existing Literature 416
9.2 Investor Preferences and Transferable Utilities 421
9.2.1 The Chiappori (2010) Conditions 423
9.3 Some “Incentive Conflicts” (and Potential Theories of Liability) Inherent in Index Funds, Passive ETFs, Active ETFs and Index-Based ETNs 424
9.3.1 The “Manager Fee Conflict” 425
9.3.2 The “Manager Performance Fund Flows Conflict” 426
9.3.3 The “Manager Leverage Conflict” 426
9.3.4 The “Manager Track Record Conflict” 427
9.3.5 The “Institutional Investor Participation Conflict” 427
9.3.6 The “Index Provider Leverage Conflict” 428
9.3.7 The “Index Provider Fee Conflict” 429
9.3.8 The “Index Provider Component Conflict” 429
9.3.9 The “Arbitrageur Participation Conflict” 430
9.3.10 The “Index-Sponsor Investor-Preference Conflict” 430
9.3.11 The “Research Analyst Fee Conflict” 431
9.3.12 The “Market-Maker Fee-Conflict” 432
9.3.13 The “Market-Maker Leverage Conflict” 432
9.3.14 The “Market-Maker Liquidity Conflict” 433
9.3.15 The Mutual-Fund/ETF Underwriter Fee Conflict 433
9.3.16 The Fund/ETF Underwriter Research Conflict 434
9.3.17 The “Employee Regulatory-Enforcement Conflict” 435
9.4 Optimal Conditions for Reducing/Eliminating Harmful Index Arbitrage, ETF Arbitrage and Associated Derivatives Arbitrage 435
9.5 The Industry’s Responses to Index Arbitrage, Mutual Fund Arbitrage and ETF Arbitrage, and Why Such Arbitrage Has Not Been Criminalized 437
9.6 New Methods for Eliminating Index Arbitrage and Index Fund Arbitrage 440
9.6.1 Elimination of Popular Metrics 441
9.6.2 Delayed Announcement of Index Weights, or Non-disclosure of Details of Index Revisions 441
9.6.3 Dynamic Index Revision Dates (Composite Conditional Change) 441
9.6.4 Change the Structure of Index Futures Contracts 443
9.6.5 Change the Structure of Swap Contracts 444
9.6.6 Trading Volume Multiplier 445
9.6.7 Implement a “Trading Price Multiplier” 446
9.6.8 Combined “Trading Price and Trading Volume Multiplier” 448
9.6.9 Index Futures “Trading Volume Multiplier” 449
9.7 New Methods for Eliminating Harmful ETF Arbitrage, Index-Based Mutual Fund Arbitrage and Associated Derivatives Arbitrage 451
9.7.1 Non-disclosure or Delay of Announcement of Methodology of Calculating ETF Portfolio Weights 451
9.7.2 Eliminate “Popular Metrics” That Are Used in Index Calculation Formulas 451
9.7.3 Dynamic Conditional Rebalancing of the ETF 452
9.7.4 There Should Not Be Any Exchange of the ETF’s Creation Units: The Creation and Redemption Processes for Traditional ETFs Are Flawed 452
9.7.5 The Implicit Interest Rates for Shorting ETF Shares Should Be Increased 454
9.7.6 “State Contingent” ETF Shares and Index Fund Units 454
9.7.7 Volume-Contingent Dissolution of ETFs and Index-Funds 455
9.7.8 Index Futures–Contingent Dissolution or Re-creation of ETF or Index Fund 456
9.7.9 Money Supply Linked ETFs and Index-Funds 456
9.8 The Economic Rationale for Making Index Arbitrage and ETF Arbitrage Illegal, and New Theories of Liability Against Perpetrators 456
9.9 Punitive Measures and Resolution of the Hedge-Fund/Mutual Fund Governance Problems 463
9.9.1 Solving the Hedge Fund Governance Problem (Fraud, Operational Risk, etc.) 463
9.9.2 Solving the Mutual Fund Governance Problem 466
9.9.3 The Creation of an “Arbitrage Resolution Fund” 468
9.10 Conclusion 469
Bibliography 469
Chapter 10: Some New Index-Calculation Methods and Their Mathematical Properties 480
10.1 Existing Literature 480
10.2 Investor Preferences, Transferable Utilities and Optimal Conditions for Indices 486
10.3 New Index Calculation/Weighting Methods 487
10.3.1 MN Market Index-1™ 488
10.3.2 MN Market Index-2™ 489
10.3.3 MN Market Index-3™ 490
10.3.4 MN Market Index-4™ 492
10.3.5 MN Market Index-5™ 493
10.3.6 MN Market Index-6™ 494
10.3.7 MN Market Index-7™ 495
10.3.8 MN Market Index-8™ 496
10.3.9 MN Market Index-9™ 497
10.3.10 MN Market Index-10™ 498
10.3.11 MN Market Index-11™ 500
10.3.12 MN Market Index-12™ 501
10.3.13 MN Market Index-13™ 502
10.3.14 MN Market Index-14™ 503
10.3.15 MN Market Index-15™ 504
10.3.16 MN Market Index-16™ 505
10.3.17 MN Market Index-17™ 506
10.3.18 MN Market Index-18™ 508
10.3.19 MN Market Index-19™ 509
10.3.20 MN Market Index-20™ 510
10.3.21 MN Market Index-21™ 511
10.3.22 MN Market Index-22™ 512
10.3.23 MN Market Index-23™ 513
10.3.24 MN Market Index-24™ 514
10.3.25 MN Market Index-25™ 516
10.3.26 MN Market Index-26™ 517
10.3.27 MN Market Index-27™ 518
10.3.28 MN Market Index-28™ 519
10.3.29 MN Market Index-29™ 519
10.3.30 MN Factor Index-1 (Operational Risk)™ 520
10.3.31 MN Factor Index-2: Value™ 521
10.3.32 MN Factor Index-3: Value™ 522
10.4 Conclusion 522
Bibliography 523
Chapter 11: Financial Indices, Joint Ventures and Strategic Alliances Invalidate Cumulative Prospect Theory, Third-Generation Prospect Theory, Related Approaches and Intertemporal Asset Pricing Theory: HCI and Three New Decision Models 531
11.1 Existing Literature 534
11.2 Risk-Adjusted Indices (RAIs) and Traditional Indices in China, Europe, Latin America and the USA as Evidence of the Invalidity of Prospect Theory, Cumulative Prospect Theory, Third-Generation Prospect Theory and Related Approaches 536
11.2.1 RAIs, Fundamental Indices and Game Theory 536
11.2.2 Errors in Some Studies of CPT/PT/PT3 in the Context of Financial Decisions 537
11.2.3 Financial Indices Invalidate PT/CPT/PT3 and Related Approaches 540
11.2.4 International Strategic Alliances (ITSA) and International Joint Ventures (ITJV) as Evidence of the Invalidity of Prospect Theory (PT), Cumulative Prospect Theory (CPT) and Third-Generation Prospect Theory (PT3) 543
11.3 International Strategic Alliances (ITSA) and International Joint Ventures (ITJV) as Elements of Regulation and as Evidence of the Invalidity of Intertemporal Asset Pricing Models
11.4 Risk-Adjusted Indices (RAIs), Fundamental Indices and Options-Based Indices as Asset Pricing Models That Contravene Most Theories of Intertemporal Asset Pricing 552
11.5 Three New Models of Decision-Making 554
11.5.1 The MN Type-I Decision Model 554
11.5.2 The MN Type-II Decision Model 558
11.5.3 The MN Type-III Decision Model 560
11.6 Conclusion 563
Bibliography 563
Chapter 12: Economic Policy, Complex Adaptive Systems, Human-Computer-Interaction and Managerial Psychology: Popular-Index Ecosystems 580
12.1 Introduction 581
12.2 Existing Literature 582
12.3 The Popular-Index Ecosystems Increase Systemic Risk and Financial Instability, and Are a New Form of Undocumented/Informal Multiparty Anti-compliance Strategic Alliance 584
12.3.1 The Popular-Index Ecosystems Increase Systemic Risk and Financial Instability 585
12.3.2 Increased “Herding” Behavior 586
12.3.3 Overinvestment in Popular-Indexes and the Resulting Underinvestment in Other Companies Around the World, and Increased Systemic Risk and Financial Instability 587
12.4 Characterization of the Popular-Index Ecosystems 588
12.4.1 Operational Contagion and Corporate Governance Contagion 588
12.4.2 Prioritization of Stakeholders 589
12.4.3 Self-propagation 589
12.4.4 Self-replication 589
12.4.5 Short-Term Focus 589
12.4.6 Super-additive Group Information Dominance Theory 589
12.4.7 Information Chain Alliance Volatility Theory 589
12.4.8 Information-Chain Execution Gaps Theory 590
12.4.9 Information Production Capabilities 590
12.4.10 Low Merger Activity 590
12.4.11 Underinvestment in Technology Portfolios 590
12.4.12 Share Repurchases 590
12.4.13 Exploration and “Exploitation Activities” 591
12.4.14 Congruence Between Corporate Strategies and Financial Management 591
12.4.15 Unintended Wealth Transfers 591
12.4.16 Managerial Entrenchment 592
12.5 Other Problems Inherent in the Popular-Index Ecosystems 592
12.5.1 The Possible Effects of the Popular-Index Ecosystems on Organizational Behavior and Group Decisions 596
12.5.1.1 Inclusion Pressure 597
12.5.1.2 Deletion Pressure 597
12.5.1.3 Corporate Governance Contagion 597
12.5.1.4 Human Capital Contagion 598
12.5.1.5 Excessive Managerial Risk-Taking 598
12.5.1.6 Distorted Incentives 598
12.5.1.7 Aggregate Super-additivity 598
12.5.1.8 Managers’ Homomorphic Utility Functions 598
12.5.1.9 Asymmetric Risk Reactions 598
12.5.1.10 Contingent Renegotiation-Proofness 599
12.5.1.11 Sequential Bargaining 599
12.5.1.12 The Cumulative Non-separability of Aggregated Managers’ Utility Functions 599
12.5.1.13 The “Long-Memory” Component of Managers’ Capital Allocation Decisions 599
12.5.1.14 Contingent Aggregate Rationality of Managers 599
12.5.1.15 Managerial Manipulation 599
12.5.1.16 Preference Matching 600
12.5.1.17 Substitutability of Managers 600
12.5.1.18 Substitutability of Managerial Compensation 600
12.5.1.19 Managers’ Willingness to Accept Losses (WTAL) 600
12.5.1.20 Self-insurance 600
12.5.1.21 The Monotonicity of Managerial “Compliance Functions” 600
12.6 Earnings Management, Incentive-Effects Management and Asset Quality Management Within Popular-Index Companies, the Manipulation of Their Cash and Cash-Equivalents, and the Associated Stock-Price Crash-Risk 601
12.6.1 Significant Tax Evasion by Fortune 500 Companies 607
12.6.2 The Periodic Changes in the Cash Balances and Cash-Equivalents of S& P-500 Companies Didn’t Match Changes in Their Real Earnings
12.6.3 Many S& P-500 Companies Didn’t Provide Adequate Disclosure About Their Accelerated Share Repurchase Programs (ASR) and ASRs Are, or May Be Illegal
12.6.4 Many S& P-500 Companies Didn’t Provide Sufficient Disclosures About Their Dividend Equivalent Rights (“DERs”)
12.6.5 Option-Grant Backdating 623
12.6.6 Earning Management and Asset Quality Management by Other Popular-Index Companies in Europe, Asia and Latin America During 2000–2017 624
12.7 Human Behavior Issues, Organizational Psychology and Complex Systems Issues 626
12.7.1 Evidence and Theories of Corporate Governance Organizational Psychology 634
12.7.1.1 Standardization Illusions Bias 634
12.7.1.2 Risk-Horizon Contingent Cognition Hypothesis (Group Cognition Dissonance) 634
12.7.1.3 Uniformity Inertia Bias 635
12.7.1.4 Incentive Neutrality Hypothesis 635
12.7.1.5 Salary and Tenure Neutrality Hypothesis 635
12.7.1.6 Reversibility Hypothesis 636
12.7.1.7 The Dynamic Reference Points Bias 636
12.7.1.8 Temporal Disassociation Hypothesis and Temporal Cohesion Hypothesis 636
12.7.1.9 Sub-additive Group Regret and Super-additive Group Regret 636
12.7.1.10 Preference for Declining or Constant Returns to Losses 637
12.7.1.11 Event-Driven Overdependence Hypothesis 637
12.7.1.12 High Error-Sensitivity and Negative Information-Sensitivity Hypothesis 637
12.7.1.13 Knowledge-Mediated Splits Hypothesis 638
12.7.1.14 Time-Consistent Preferences Bias 638
12.7.1.15 Willingness-to-Accept-Losses (WTAL) 638
12.7.1.16 Disappointment Aversion 638
12.7.1.17 Framing Effects and Static Risk Management 638
12.7.1.18 Coalition Formation Synthesis Hypothesis 639
12.7.1.19 Sub-additive Loss Internalization Hypothesis 639
12.7.1.20 Selective Risk Tolerance Hypothesis 639
12.7.1.21 Complex “Higher-Order Behaviors” 639
12.7.1.22 Corporate Governance Statutes and Corporations’ Strategies/Mechanisms/Alliances as Non-public Goods (That May Be Created, Diminished or Amplified by Political Influence and Lobbying) 640
12.7.1.23 Enforcement Leakages 640
12.7.1.24 The Sub-optimal Investment Hypothesis 640
12.7.1.25 Strategy Permeation Deficits Hypothesis 641
12.7.1.26 Deadweight Losses 641
12.7.1.27 Entrenchment (Vertical and Horizontal) Hypothesis 641
12.7.1.28 Selective Concern for Social Welfare Hypothesis 642
12.7.1.29 The Policy-Dampening Alliance Hypothesis 642
12.7.1.30 The Dynamic Coordination-Gaps Hypothesis 642
12.7.1.31 Resource Allocation Efficiency Deficits Hypothesis 642
12.7.1.32 The Sub-optimally Exercised Time-Varying Asymmetric Power Hypothesis 643
12.7.1.33 Regulatory Failure Hypothesis (That May Be Caused or Amplified by Political Influence and Lobbying) 643
12.8 Conclusion 646
Bibliography 647
Chapter 13: Implications for Decision Theory, Enforcement, Financial Stability and Systemic Risk 655
13.1 Misrepresentation, Deceit and Implications for Legislation and Enforcement 655
13.1.1 Some New Models of “ Hybrid” Government-Controlled Policies, Intervention and Reallocation 661
13.1.1.1 The Creation of an “Arbitrage Resolution Fund” 663
13.1.1.2 The Creation of a “Sustainable Growth and Harmful Technological Change Resolution Fund” 663
13.1.1.3 The Creation of a “Pollution and Climate Change Resolution Fund” 669
13.1.1.4 The Creation of an “Inequality and Globalization Resolution Fund” 676
13.1.1.5 The Creation of a “Destructive Urbanization Resolution Fund” 680
13.2 Implications for Decision Theory (Game Theory Cumulative Prospect Theory and Third-Generation-Prospect-Theory (PT3))
13.3 Implications of Indices and Index Funds for Nonlinear Systemic Risk and Nonlinear Financial Instability 685
13.4 Implications of ETFs for Nonlinear Systemic Risk and Nonlinear Financial Instability 689
13.5 RAIs and Options-Based Indices (OIs) Can Cause Systemic Risk 695
13.6 PT-Portfolios, CPT-Portfolios and PT3 Portfolios (and Related Portfolios) and Mean–Variance Portfolios Can Cause or Amplify Systemic Risk and Financial Instability 696
13.7 Mean–Variance Portfolios Can Cause or Amplify Systemic Risk and Financial Instability 697
13.8 Inaccuracy of Hidden Markov Models (HMMs) 697
13.9 Conclusion 698
Bibliography 699