Indices, Index Funds And ETFs, 1st ed. 2018 Exploring HCI, Nonlinear Risk and Homomorphisms

Indices, index funds and ETFs are grossly inaccurate and inefficient and affect more than €120 trillion worth of securities, debts and commodities worldwide. This book analyzes the mathematical/statistical biases, misrepresentations, recursiveness, nonlinear risk and homomorphisms inherent in equity, debt, risk-adjusted, options-based, CDS and commodity indices ? and by extension, associated index funds and ETFs. The book characterizes the ?Popular-Index Ecosystems,? a phenomenon that provides artificial price-support for financial instruments, and can cause systemic risk, financial instability, earnings management and inflation. The book explains why indices and strategic alliances invalidate Third-Generation Prospect Theory (PT³), related approaches and most theories of Intertemporal Asset Pricing. This book introduces three new decision models, and some new types of indices that are more efficient than existing stock/bond indices. The book explains why the Mean-Variance framework, the Put-Call Parity theorem, ICAPM/CAPM, the Sharpe Ratio, Treynor Ratio, Jensen?s Alpha, the Information Ratio, and DEA-Based Performance Measures are wrong. Leveraged/inverse ETFs and synthetic ETFs are misleading and inaccurate and non-legislative methods that reduce index arbitrage and ETF arbitrage are introduced.  

Chapter 1. Introduction.

1.1. How this Book Differs from Other Books About ETFs, Indices and Index Funds.

1.2. Regulatory Failure, Regulatory Capture and Regulatory Fragmentation.

1.3. Some Mathematical Commonalities Among Debt, Equity and Commodity Indices.

1.4. The Chapters: Activity Theory and HCI.  

1.5. Momentum Effects, Systemic Risk and Financial Instability.

1.6. The Usefulness of Alpha and Beta as Currently Construed; and the Debate About Active Management versus Passive Management. 

1.7. ETFs vs. Mutual Funds vs. Closed-End Funds.

1.8. The Case-Shiller Real Estate Indices Are Very Inaccurate and Mis-leading.

1.9. Tax Aspects of Investing in ETFs and Indices.

1.10. Forecasting and Comparisons of Stock Indices and ETFs. 

1.11. Network Analysis and Complexity in Stock Indices and ETFs.

Chapter 2. Decision-Making and Spatio-Temporal Cognitive 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 Regimes.    

2.1. Existing Literature. 

2.1.1. Traditional Indices As Options-Based Indices. 

2.2. MN-Transferable-Utility.

Theorem-1.

2.3. The ICAPM/CAPM Are Inaccurate.

Theorem-2.

Theorem-3.

2.4.The Traditional Index Calculation Methods (applicable to many equity, debt, real estate, commodity and currency indices).  

2.4.1 Market-Capitalization Weighted Indices (And “Diversity” Indices).

Theorem-4:

2.4.2. Free Float Adjusted Indices.

2.4.3. Fundamental Indices.

2.4.4. Stock-Price Weighted Indices.

2.4.5. Trading-Volume Weighted Indices.

2.4.6. Market-Cap Weighted and Volume-Weighted Indices (Two Methods). 

2.4.7. Dividend-Weighted Indices.  

2.4.8. Equal-Weight Indices. 

2.4.9. Thomson Reuters’s Indices.  

2.5. Other Distortions in Traditional Indices.

Theorem-5

Theorem-6

Theorem-7

Theorem-8

Theorem-9

Theorem-10

2.6. Traditional Index Calculation Methods Create Significant Incentives for Companies to Perpetrate Earnings Management. 

2.7. Conclusion.

Chapter 3. A Critique of Credit Default Swaps (CDS) Indices. 

3.1. Existing Literature.

3.2. Quasi-Default Versus Reported Default: the Difference Reduces the Usefulness of CDS Indices.

3.3. The Credit-Ratings Lag.

3.5. Behavioral Effects and Externalities Inherent in the Use CDS, and Which May Distort the Accuracy of CDS-Indices.

3.6. Financial Stability.

3.7. S&P’s Credit Default Swap (CDS) Indices - the S&P CDS Index Calculation Methods Are Wrong.

3.8. Conclusion.

Chapter 4. Invariants and Homomorphisms Implicit in, and the Irrelevance of the Mean-Variance Framework in Risk Analysis, Decision-Making and Portfolio Management

4.1. Existing Literature.  

4.2. The Mean Variance Framework is Inaccurate

Theorem-2

Theorem 3

Corollary-#1

Corollary-#2

Corollary-#3

Corollary-#4

Corollary-#5

Corollary-#6

Corollary-#7

Corollary-#8

Corollary-#9

Corollary-#10

Corollary-#11

Corollary-#12

Corollary-#13

Corollary-#14

Corollary-#15

Chapter 5. Decision-Making, Sub-Additive Recursive “Matching” Noise and Biases in Risk-Weighted Stock/Bond Index Calculation Methods in Incomplete Markets with Partially Observable Multi-Attribute Preferences.   

5.1. Existing Literature.  

5.2. The ICAPM/CAPM is Inaccurate.

Theorem-1

5.3. For any investment horizon and any market, all risk-weighting methods distort the risk of constituent companies.

Theorem-4

5.4. The Risk-Adjusted Index Calculation Methods are Wrong.

5.4.1. Free-float Adjusted Indices.

5.4.2. Equal risk contribution (“ERC”) Indices. 

5.4.3. “Most-diversified” (“Diversity”) Indices.

5.4.4. “Minimum-Variance” Indices.

5.4.5. FTSE/EDHEC Risk-adjusted Indices. 

5.4.6. The Hang Seng Risk-adjusted Indices.

5.4.7. The S&P Risk-control Index Series: S&P Developed Market Risk-control Index Series, S&P Emerging Market Risk-control Indices and S&P Global Thematic Risk-control Indices.

5.4.8. The Thomson Reuters Lipper Optimal Target Risk Indices. 

5.4.9. The Dow Jones Relative-risk Indices.

Theorem-5

Theorem-6

Theorem-7

Theorem-8

Theorem-9

Theorem-10

Theorem-11

5.4.10. The Dow Jones RPB Indices. 

5.4.11. The FTSE Stablerisk Index Series.  

5.4.12. The Minimum Correlation Indices.

5.4.13. Risk Parity (RP) Indices.

5.5. Conclusion.

Chapter 6. Informationless Trading and Biases in Performance Measurement: Inefficiency of the Sharpe Ratio, Treynor Ratio, Jensen Alpha, the Information Ratio and DEA-Based Performance Measures and Related Measures.   

6.1. Existing Literature.

6.2. CAPM/ICAPM/IAPT Are Inaccurate

6.3. Inherent Biases And Effects That May Affect Performance Measures.

6.4. Effect Of The Investment Horizon.

6.5.1. Error Assumption #1

6.5.2. Error Assumption #2

6.5.3. Error Assumption #3

6.5.4. Error Assumption #4

6.5.5. Error Assumption #5

6.5.6. Error Assumption #6

6.5.7. Error Assumption #7

6.5.8. Error Assumption #8

6.5.9. Error Assumption #9

6.5.10. Error Assumption #10

6.5.11. Error Assumption #11

6.5.12. Error Assumption #12

6.5.13. Error Assumption #13

6.5.14. Error Assumption #14

6.5.15. Error Assumption #15

6.5.16. Error Assumption #16

6.5.17. Error Assumption #17

6.5.18. Error Assumption #18

6.5.19. Error Assumption #19

6.5.20. Error Assumption #20

6.5.21. Error Assumption #21

6.5.22. Error Assumption -#22

6.5.24. Error Assumption -#24

6.5.25. Error Assumption -#25

6.5.26. Error Assumption-#26

6.5.27. Error Assumption-#27

6.5.28. Error Assumption-#28

6.5.29. Error Assumption-#29

6.5.31. Error Assumption-#31

6.6. Properties of a Manipulation-Proof Performance Measurement System.

6.6.1. Goetzmann, Ingersoll, Spiegel & Welch (2007) – Properties of a “Manipulation proof Performance Measure” (“MPPM”)  

6.6.2. New Properties of a Manipulation-Proof Performance System (“MPPS”)    

6.7. Conclusion.

Chapter 7. Anomalies in Taylor-Series; and Tracking-Errors And Homomorphisms in the Returns of Leveraged/Inverse ETFs and Synthetic ETFs/Funds.   

7.1. Inverse/leveraged ETFs.

7.1.1. Existing Literature.

7.1.2. Some Biases and Problems Inherent in Leveraged ETFs and Inverse ETFs.

7.1.2.1. There cannot be an “Optimal” Degree Of Positive/Negative Leverage for Leveraged/Inverse ETFs.

7.1.2.2 The Hill & Foster (2009) Study is Misleading and Inaccurate.

7.1.2.3. Compounding Has A Significant Effect on Leveraged/Inverse ETFs.

7.1.2.4. Intra-Day Volatility is Irrelevant and Only End-of Day Prices Matter; The Co & Labuszewski (July 2012) Study Is Inaccurate; And Volatility Has Minimal Effects On The Downward Returns Bias.

Theorem-1

7.1.2.5. Portfolio Re-Balancing by Investors That Own Leveraged/Inverse ETFs Is Not Always Feasible.

7.1.2.6. The Effect of Underlying Indices.

7.1.2.7. Leveraged/Inverse ETFs Are Highly Sensitive To Manipulation Of End-Of-Day Prices and to the Calculation of End-Of-Day Prices.

7.1.2.8. Changing Margin Requirements Will Not Be Very Helpful. 

7.1.2.9. Leveraged/Inverse ETFs Are Gambling Tools.

7.1.2.10. There Are No Basis for Comparisons of Leverage/Inverse ETFs to Leveraged Companies (or Leveraged Mutual Funds).   

7.1.2.11. Implied Portfolio Weights.  

7.1.2.12. The Inaccuracy of the Put Call Parity Theorem, the Early Exercise Premia and the Structure of Leveraged/Inverse ETFs. 

7.1.2.14. Risk Return Tradeoff. 

7.1.2.15. Suitability & Disclosure.

7.1.2.16. Manager-Risk Inherent in Leveraged/Inverse ETFs. 

7.2. Synthetic ETFs and Synthetic Funds. 

7.2.1. Existing Literature

7.2.2. Synthetic ETFs and Synthetic Index Funds.

7.2.2.1. The Inaccuracy of the Put Call Parity Theorem, the Early Exercise Premia and the Structure Of Synthetic Funds/ ETFs. 

7.2.2.2. Implied Portfolio Weights.  

7.2.2.3. Some Investors Can Create the Same Economic Effects/Benefits Of Synthetic Funds/ETFs More Cheaply and More Efficiently By Themselves.  

7.2.2.4. Investment Horizon.

7.2.2.5. Counter-Party Credit Risk.

7.2.2.6. Tracking Errors and Compounding And Their Effects On Synthetic Funds/ETFs.

7.2.2.7. Changing Margin Requirements Will Not Be Very Helpful. 

7.2.2.9. The Effect of Underlying Indices.

7.2.2.10. Synthetic Funds/ETFs Are Highly Sensitive to Manipulation of End-Of-Day Prices and to the Calculation of End-Of-Day Prices.

7.2.2.11. Manager-Risk Inherent in Synthetic Funds/ETFs. 

7.3. Conclusion.

Chapter 8.Spatio-Temporal Cognitive Biases, Misrepresentation and Homomorphisms in the VIX and Options Based Indices in Incomplete Markets with Un-Aggregated Preferences and NT-Utilities Under a Regret–Minimization Regime.

8.2. Critique of Calculation Methods for Options-Based Indices.

8.2.1. Buy-Write Indices.

Theorem-1

8.2.2. The CBOE Put-Write Indices.  

Theorem-2

8.2.3. The Thomson Reuters “Realized Volatility Index”.

8.2.4. VIX Volatility Index.

Theorem-3

8.2.5. Other Options-Based Indices.    

Chapter 9. Investors’ Preferences, Human-Computer Interaction and Non-Legislative Approaches for Eliminating Index Arbitrage and ETF Arbitrage.    

9.1. Existing Literature. 

9.2. Investor Preferences and Transferable Utilities. 

9.2.1. The Chiappori (2007) Conditions.

9.3. Optimal Conditions for Reducing/Eliminating Index Arbitrage and ETF Arbitrage.

9.4. The Industry’s Responses to Index Arbitrage and ETF Arbitrage; and Why Index Arbitrage Has Not Been Criminalized.

9.4.1. Why Index Arbitrage Has Not Been Criminalized To Date.

9.5.1. Elimination of Popular Metrics.

9.5.2. Delayed Announcement of Index Weights; or Non-Disclosure of Details of Index Revisions.

9.5.3. Dynamic Index Revision Dates (Composite Conditional Change). 

9.5.4. Change The Structure of Index Futures Contracts. 

9.5.6. Trading Volume Multiplier. 

9.5.7. Implement a Trading Price Multiplier.  

9.5.8. Combined Trading Price and Trading Volume Multiplier. 

9.5.9. Index-Futures Trading-Volume Multiplier. 

9.6. New Methods For Eliminating ETF Arbitrage.

9.6.1. Non-Disclosure Of Methodology Of Calculating ETF Portfolio Weights.

9.6.2. Eliminate “Popular Metrics” in Indices.

9.6.3. Dynamic Conditional Re-Balancing of The ETF.

9.6.4. There Should Not Be Any Exchange of the ETF’s Creation Units - the Creation and Redemption Processes for Traditional ETFs are Flawed

9.6.6. “State Contingent” ETF Shares. 

9.6.7. Volume-Contingent Dissolution of ETFs.  

9.6.8. Index Futures–Contingent Dissolution or Re-Creation of ETF. 

9.7. The Economic Rationale for Making Index Arbitrage and ETF Arbitrage Illegal; and New Theories of Liability Against Perpetrators of Index Arbitrage and ETF Arbitrage. 

9.8. Conclusion. 

Chapter 10. Eliciting Investors’ Preferences: Some New Index-Calculation Methods and their Mathematical Properties.

10.1. Existing Literature.

10.2. Investor Preferences, Transferable Utilities and Optimal Conditions for Indices.

10.3. New Index Calculation/Weighting Methods. 

10.3.1. Broad Market Index-1™.

10.3.3. Broad Market Index-3™.

10.3.4. Broad Market Index-4™.

            10.3.5. Broad Market Index-5™.

            10.3.6. Broad Market Index-6™. 

            10.3.7. Broad Market Index-7™. 

            10.3.8. Broad Market Index-8™.

            10.3.9. Broad Market Index-9™.

            10.3.10. Broad Market Index-10™. 

            10.3.11. Broad Market Index-11™. 

            10.3.12. Broad Market Index-12™.

            10.3.13. Broad Market Index-13™.

            10.3.14. Broad Market Index-14™.

            10.3.15. Broad Market Index-15™.

            10.3.16. Broad Market Index-16™.

            10.3.17. Broad Market Index-17™.

            10.3.18. Broad Market Index-18™.

            10.3.19. Broad Market Index-19™.

            10.3.20. Broad Market Index-20™.

            10.3.21. Broad Market Index-21™.

            10.3.22. Broad Market Index-22™.

            10.3.23. Broad Market Index-23™.

            10.3.24. Broad Market Index-24™.

            10.3.25. Broad Market Index-25™.

            10.3.26. Broad Market Index-26™.

            10.3.27. Broad Market Index-27™.

            10.3.28. Broad Market Index-28™.

            10.3.30. Factor Index-1 (Operational Risk).

10.3.31. Factor Index-2: Value. 

            10.3.32. Factor Index-3: Value.

Chapter 11. Stock-Indices and Strategic Alliances Invalidate Third-Generation Prospect Theory, Related Approaches and Intertemporal Asset Pricing Theory: HCI and Three New Decision Models.     

11.1. Existing Literature.         

11.2. Risk-Adjusted Indices (RAIs) and Traditional Stock Indices in China and the US as Evidence of the Invalidity of Prospect Theory, Cumulative Prospect Theory, Third-Generation Prospect Theory and Related Approaches.           

11.3. RAIs, Fundamental Indices and Game Theory. 

11.4. RAIs and Options Based Indices (OIs) Can Cause Systemic Risk.         

11.5. Errors in Some Studies Of CPT/PT/PT³ in the Context of Financial Decisions.

11.6. The Invalidity Of PT/CPT/PT³ and Related Approaches.

11.7. PT-Portfolios, CPT-Portfolios and PT³ Portfolios (and Related Portfolios) Can Cause Substantial Systemic-Risk/Contagion and Financial Instability.        

11.8. Intertemporal Strategic Alliances (ITSA) and Joint Ventures (ITJV) as Elements Of Regulation; and as Evidence of the Invalidity of the Intertemporal Asset Pricing Models.

11.9. RAIs, Fundamental Indices and Options-Based Indices as Asset Pricing Models That Contravene Most Theories of Intertemporal Asset Pricing.           

11.10. Three New Models of Decision-Making That Are Derived From the Structure of Indices and Associated Investor Preferences.    

11.10.1. The MN Type-I Decision Model.

11.10.2. The MN Type-II Decision Model.           

11.10.3. The MN Type-III Decision Model.          

11.11. Conclusion.     

Chapter 12. Economic Policy and “Popular-Index Ecosystems”: Managerial Psychology, Human-Computer Interaction, Corporate Governance and Risk Effects.   

12.1. Introduction. 

12.2. Existing Literature. 

12.3. The Popular-Index Ecosystems Increase Systemic Risk and Financial Instability; and are a New Form of Un-documented/Informal Multi-Party Anti-Compliance Strategic Alliance.

            12.3.1. The Popular-Index Ecosystems increase Systemic Risk and Financial Instability.

            12.3.2. Increased “Herding” Behavior.

12.3.3. Over-Investment in Popular-Indices, and the Resulting Under-Investment in Other Companies Around the World; and Increased Systemic Risk and Financial Instability.

12.4. Characterization of The Popular-Index Ecosystems.

            12.4.1. Operational Contagion and Corporate Governance Contagion.

            12.4.2. Prioritization of Stakeholders.

            12.4.3. Self-Propagation.

            12.4.4. Self-replication.

            12.4.5. Short term focus. 

            12.4.6. Super-Additive Group Information Dominance Theory.

            12.4.7. Information Chain Alliance VolatilityTheory.

            12.4.8. Information-Chain Execution Gaps Theory.

            12.4.10. Low Merger Activity.

            12.4.11. Under-Investment in technology Portfolios.

            12.4.12. Share Repurchases. 

            12.4.14. Congruence Between Corporate Strategies And Financial Management. 

            12.4.15. Un-Intended Wealth Transfers.

            12.4.16. Managerial Entrenchment.

12.5. Other Problems Inherent in the Popular-Index Ecosystems.

12.5.1. The Possible Effects of the Popular-Index Ecosystems On Organizational Behavior and Group Decisions. 

            12.5.1.1. Inclusion Pressure.

            12.5.1.2. Deletion Pressure. 

            12.5.1.3. Corporate Governance Contagion.

            12.5.1.4. Human Capital Contagion.

            12.5.1.5. Excessive Managerial Risk-Taking.

            12.5.1.6. Distorted Incentives.

            12.5.1.7. Aggregate Super-Additivity.

            12.5.1.8. Managers’ Homomorphic Utility Functions. 

            12.5.1.9. Asymmetric Risk Reactions.  

            12.5.1.10. Contingent Renegotiation-Proofness.

            12.5.1.11. Sequential Bargaining.

            12.5.1.12. The Cumulative Non-Separability of aggregated managers’ utility-functions.

            12.5.1.13. The “Long-Memory” component of managers’ capital allocation decisions.

            12.5.1.14. Contingent Aggregate Rationality of Managers.

            12.5.1.15. Managerial Manipulation.

            12.5.2.16. Preference Matching.

            12.5.2.17. Substitutability of Managers.

            12.5.1.18. Substitutability of Managerial Compensation.

            12.5.1.19. Managers’ Willingness to Accept Losses (WTAL).

            12.5.1.20. Self-Insurance.

            12.5.1.21. The Monotonicity of Managerial “Compliance Functions”. 

12.6. Earnings Management, Incentive-Effects Management and Asset-Quality Management Within Popular-Index Companies; and the Manipulation of their Cash and Cash-Equivalents, and the Associated Stock-Price Crash-Risk.   

            12.6.1. Significant Tax-Evasion by Fortune-500 Companies.  

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 Program (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”); and DERs are or Maybe Illegal. 

            12.6.5. Option-Grant Backdating.

12.6.6. Earnings Management and Asset-Quality Management by Other Popular-Index Companies In Europe, Asia And Latin-America During 2000-2017.

12.7. Human Behavior Issues.

            12.7.1. Evidence; And Theories of Corporate Governance and Organizational Psychology. 

            12.7.1.1. Standardization Illusions Bias. 

            12.7.1.2. Risk-Horizon Contingent Cognition Theory (Group Cognition Dissonance).

            12.7.1.3. Uniformity Inertia Bias.

            12.7.1.5. Salary & Tenure Neutrality Theory.

            12.7.1.6. Reversibility Theory.

            12.7.1.7. The Dynamic Reference-Points Bias. 

            12.7.1.8. Temporal Disassociation Theory and Temporal Cohesion Theory.

            12.7.1.9. Sub-Additive Group-Regret And Super-Additive Group-Regret.

            12.7.1.10. Preference For Declining Or Constant Returns To Losses. 

            12.7.1.11. Event Driven Over-dependence Theory.

            12.7.1.12. High Error-Sensitivity And Negative-Information Sensitivity Theory.

            12.7.1.13. Knowledge-mediated Splits Theory.

            12.7.1.15. Willingness To Accept Losses (WTAL).

            12.7.1.16. Disappointment Aversion.

            12.7.1.17. Framing Effects and or Static Risk Management.

            12.7.1.18. Coalition Formation Synthesis Theory.

            12.7.1.19. Sub-Additive Loss Internalization Theory.

            12.7.1.20. Selective Risk Tolerance Theory.

            12.7.1.21. Complex “higher-order behaviors”.

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).

            12.7.1.23. Enforcement Leakages.

            12.7.1.24. The Sub-optimal Investment Theory.

            12.7.1.25. Strategy Permeation Deficits Theory.

            12.7.1.26. Deadweight Losses.

            12.7.1.27. Entrenchment.

            12.7.1.28. Selective Concern for Social Welfare.

            12.7.1.29. The Policy-Dampening Alliance Theory.

            12.7.1.30. The Dynamic Coordination-Gaps Theory. 

            12.7.1.31. Resource Allocation Efficiency Deficits.

            12.7.1.32. The Sub-optimally Exercised Time-Varying Asymmetric Power Theory.

12.7.1.33. Regulatory Failure (that may be caused or amplified by Political Influence and Lobbying).

12.8. Conclusion.

Chapter 13. Conclusion: Implications for Decision Theory, Enforcement, Financial Stability and Systemic Risk.       

13.2. Implications for Decision Theory (Cumulative Prospect Theory and Third-Generation-Prospect-Theory (PT³)). 

13.3. Implications for Game Theory.

13.4. Implications of Indices and Index Funds for Nonlinear Systemic Risk and Nonlinear Financial Instability.

13.5. Implications of ETFs for Nonlinear Systemic Risk and Nonlinear Financial Instability.

Chapter 14. Bibliography.   

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.

Analyses nonlinear risk, human biases and computational biases inherent in indices, ETFs and Index Funds.

Discusses the complex adaptive systems approach and the “ecosystem of the index world”; and implications of indices, ETFs and index funds for asset pricing and Cumulative Prospect Theory.

Critiques the introduction of new index models and ETFs models, and methods or preventing or reducing index-arbitrage and ETF arbitrage.