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" Indices, index funds and ETFs : "
Michael I.C. Nwogugu.
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BL
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840848
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Nwogugu, Michael C. I.
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| Title & Author
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Indices, index funds and ETFs : : exploring HCI, nonlinear risk and homomorphisms /\ Michael I.C. Nwogugu.
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| Publication Statement
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London :: Palgrave Macmillan,, [2018]
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1 online resource (xxii, 696 pages) :: illustrations
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| ISBN
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113744701X
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: 1349685887
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: 1349685895
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: 9781137447012
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: 9781349685882
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: 9781349685899
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1137447001
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9781137447005
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9781349685882
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9781349685899
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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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. 3.4. The Methods for Pricing Of Debt Reduces the Accuracy of CDS Indices. 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. Critical Assumptions, Noise And Error. 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 6.5.23. Error Assumption -#23 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 6.5.30. Error Assumption-#30 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. 7.1.2.13. Investors Can Replicate the Leverage/Inverse Effects More Cheaply And More Efficiently By Themselves. 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. 7.2.2.8. Intra-Day Volatility is Irrelevant and Only End-of Day Prices Matter; The Co & Labuszewski (July 2012) Study is also Inaccurate; and Volatility Has Minimal Effects on The Downward Returns Bias. 7.2.2.9. The Effect of Underlying Indices. 7.2.2.10.
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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. 8.1. Existing Literature. 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. 8.3. Conclusion. 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. New Methods for Eliminating Index Arbitrage. 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 T
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| Abstract
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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.
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Exchange traded funds.
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Index mutual funds.
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BUSINESS ECONOMICS-- Finance.
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Exchange traded funds.
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Index mutual funds.
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| Dewey Classification
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332.63/27
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| LC Classification
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HG6043.N86 2018
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