{"product_id":"indices-index-funds-and-etfs-exploring-hci-nonlinear-risk-and-homomorphisms-9781137447005","title":"Indices Index Funds And ETFs Exploring HCI","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e1. Introduction.- 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 RegretMinimization Regimes.- 3. A Critique of Credit Default Swaps (CDS) Indices.- 4. Invariants and Homomorphisms Implicit in, and the Invalidity of the Mean-Variance Framework and Other Causality Approaches: Some \u003ci\u003eStructural Effects\u003c\/i\u003e.- 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.- 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.- 7. Anomalies in Taylor-Series, and Tracking Errors and Homomorphis\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eChapter 1\u003c\/b\u003e. \u003cb\u003eIntroduction\u003c\/b\u003e\u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e1.1. How this Book Differs from Other Books About ETFs, Indices and Index Funds.\u003c\/p\u003e  \u003cp\u003e1.2. Regulatory Failure, Regulatory Capture and Regulatory Fragmentation.\u003c\/p\u003e  \u003cp\u003e1.3. Some Mathematical Commonalities Among Debt, Equity and Commodity Indices.\u003c\/p\u003e  \u003cp\u003e1.4. The Chapters: Activity Theory and HCI.   \u003c\/p\u003e  \u003cp\u003e1.5. Momentum Effects, Systemic Risk and Financial Instability.\u003c\/p\u003e  \u003cp\u003e1.6. The Usefulness of Alpha and Beta as Currently Construed; and the Debate About Active Management versus Passive Management.  \u003c\/p\u003e  \u003cp\u003e1.7. ETFs vs. Mutual Funds vs. Closed-End Funds.\u003c\/p\u003e  \u003cp\u003e1.8. The Case-Shiller Real Estate Indices Are Very Inaccurate and Mis-leading.\u003c\/p\u003e  \u003cp\u003e1.9. Tax Aspects of Investing in ETFs and Indices.\u003c\/p\u003e  \u003cp\u003e1.10. Forecasting and Comparisons of Stock Indices and ETFs.  \u003c\/p\u003e  \u003cp\u003e1.11. Network Analysis and Complexity in Stock Indices and ETFs.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 2\u003c\/b\u003e. \u003cb\u003eDecision-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.\u003c\/b\u003e     \u003c\/p\u003e  \u003cp\u003e2.1. Existing Literature.  \u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e2.1.1. Traditional Indices As Options-Based Indices.  \u003c\/p\u003e  \u003cp\u003e2.2. MN-Transferable-Utility.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-1.\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e2.3. The ICAPM\/CAPM Are Inaccurate.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-2.\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-3.\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e2.4.\u003cb\u003e \u003c\/b\u003eThe Traditional Index Calculation Methods\u003cb\u003e (\u003c\/b\u003eapplicable to many equity, debt, real estate, commodity and currency indices).   \u003c\/p\u003e  \u003cp\u003e2.4.1 Market-Capitalization Weighted Indices (And “Diversity” Indices). \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-4:\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e2.4.2. Free Float Adjusted Indices.\u003c\/p\u003e  \u003cp\u003e2.4.3. Fundamental Indices.\u003c\/p\u003e  \u003cp\u003e2.4.4. Stock-Price Weighted Indices.\u003c\/p\u003e  \u003cp\u003e2.4.5. Trading-Volume Weighted Indices.\u003c\/p\u003e  \u003cp\u003e2.4.6. Market-Cap Weighted and Volume-Weighted Indices (Two Methods).  \u003c\/p\u003e  \u003cp\u003e2.4.7. Dividend-Weighted Indices.  \u003c\/p\u003e  \u003cp\u003e2.4.8. Equal-Weight Indices.  \u003c\/p\u003e  \u003cp\u003e2.4.9. Thomson Reuters’s Indices.   \u003c\/p\u003e  \u003cp\u003e2.5. Other Distortions in Traditional Indices.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-5\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-6\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-7\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-8\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eTheorem-9\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-10\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e2.6. Traditional Index Calculation Methods Create Significant Incentives for Companies to Perpetrate Earnings Management.  \u003c\/p\u003e  \u003cp\u003e2.7. Conclusion.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 3\u003c\/b\u003e. \u003cb\u003eA Critique of Credit Default Swaps (CDS) Indices\u003c\/b\u003e.  \u003c\/p\u003e  \u003cp\u003e3.1. Existing Literature. \u003c\/p\u003e  \u003cp\u003e3.2. Quasi-Default Versus Reported Default: the Difference Reduces the Usefulness of CDS Indices.\u003c\/p\u003e  \u003cp\u003e3.3. The Credit-Ratings Lag.\u003c\/p\u003e  3.4. The Methods for Pricing Of Debt Reduces the Accuracy of CDS Indices. \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e3.5. Behavioral Effects and Externalities Inherent in the Use CDS, and Which May Distort the Accuracy of CDS-Indices. \u003c\/p\u003e  \u003cp\u003e3.6. Financial Stability.\u003c\/p\u003e  \u003cp\u003e3.7. S\u0026amp;P’s Credit Default Swap (CDS) Indices - the S\u0026amp;P CDS Index Calculation Methods Are Wrong.\u003c\/p\u003e  \u003cp\u003e3.8. Conclusion.\u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 4. Invariants and Homomorphisms Implicit in, and the Irrelevance of the Mean-Variance Framework in Risk Analysis, Decision-Making and Portfolio Management\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e4.1. Existing Literature.   \u003c\/p\u003e  \u003cp\u003e4.2. The Mean Variance Framework is Inaccurate\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-2\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem 3\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eCorollary-#1\u003c\/p\u003e  \u003cp\u003eCorollary-#2\u003c\/p\u003e  \u003cp\u003eCorollary-#3\u003c\/p\u003e  \u003cp\u003eCorollary-#4\u003c\/p\u003e  \u003cp\u003eCorollary-#5\u003c\/p\u003e  \u003cp\u003eCorollary-#6\u003c\/p\u003e  \u003cp\u003eCorollary-#7\u003c\/p\u003e  \u003cp\u003eCorollary-#8\u003c\/p\u003e  \u003cp\u003eCorollary-#9\u003c\/p\u003e  \u003cp\u003eCorollary-#10\u003c\/p\u003e  \u003cp\u003eCorollary-#11\u003c\/p\u003e  \u003cp\u003eCorollary-#12\u003c\/p\u003e  \u003cp\u003eCorollary-#13\u003c\/p\u003e  \u003cp\u003eCorollary-#14\u003c\/p\u003e  \u003cp\u003eCorollary-#15\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 5\u003c\/b\u003e. \u003cb\u003eDecision-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.\u003c\/b\u003e    \u003c\/p\u003e  \u003cp\u003e5.1. Existing Literature.   \u003c\/p\u003e  \u003cp\u003e5.2. The ICAPM\/CAPM is Inaccurate.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-1\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e5.3. For any investment horizon and any market, all risk-weighting methods distort the risk of constituent companies.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-4\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e5.4. The Risk-Adjusted Index Calculation Methods are Wrong. \u003c\/p\u003e  \u003cp\u003e5.4.1. Free-float Adjusted Indices. \u003c\/p\u003e  \u003cp\u003e5.4.2. Equal risk contribution (“ERC”) Indices.  \u003c\/p\u003e  \u003cp\u003e5.4.3. “Most-diversified” (“Diversity”) Indices. \u003c\/p\u003e  \u003cp\u003e5.4.4. “Minimum-Variance” Indices. \u003c\/p\u003e  \u003cp\u003e5.4.5. FTSE\/EDHEC Risk-adjusted Indices.  \u003c\/p\u003e  \u003cp\u003e5.4.6. The Hang Seng Risk-adjusted Indices.\u003c\/p\u003e  \u003cp\u003e5.4.7. The S\u0026amp;P Risk-control Index Series: S\u0026amp;P Developed Market Risk-control Index Series, S\u0026amp;P Emerging Market Risk-control Indices and S\u0026amp;P Global Thematic Risk-control Indices. \u003c\/p\u003e  \u003cp\u003e5.4.8. The Thomson Reuters Lipper Optimal Target Risk Indices.  \u003c\/p\u003e  \u003cp\u003e5.4.9. The Dow Jones Relative-risk Indices.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-5\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-6\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-7\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003eTheorem-8\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-9\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-10\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-11\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e5.4.10. The Dow Jones RPB Indices.  \u003c\/p\u003e  \u003cp\u003e5.4.11. The FTSE Stablerisk Index Series.   \u003c\/p\u003e  \u003cp\u003e5.4.12. The Minimum Correlation Indices.\u003c\/p\u003e  \u003cp\u003e5.4.13. Risk Parity (RP) Indices.\u003c\/p\u003e  \u003cp\u003e5.5. Conclusion.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 6\u003c\/b\u003e\u003cb\u003e. 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.\u003c\/b\u003e    \u003c\/p\u003e  \u003cp\u003e6.1. Existing Literature. \u003c\/p\u003e  \u003cp\u003e6.2. CAPM\/ICAPM\/IAPT Are Inaccurate\u003c\/p\u003e  \u003cp\u003e6.3. Inherent Biases And Effects That May Affect Performance Measures.\u003c\/p\u003e  \u003cp\u003e6.4. Effect Of The Investment Horizon. \u003c\/p\u003e  6.5. Critical Assumptions, Noise And Error.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.5.1. \u003ci\u003eError Assumption #1\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.2. \u003ci\u003eError Assumption #2\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.3. \u003ci\u003eError Assumption #3\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.4. \u003ci\u003eError Assumption #4\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.5. \u003ci\u003eError Assumption #5\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.6. \u003ci\u003eError Assumption #6\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.7. \u003ci\u003eError Assumption #7\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.8. \u003ci\u003eError Assumption #8\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.9. \u003ci\u003eError Assumption #9\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.10. \u003ci\u003eError Assumption #10\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.11. \u003ci\u003eError Assumption #11\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.12. \u003ci\u003eError Assumption #12\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.13. \u003ci\u003eError Assumption #13\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.14. \u003ci\u003eError Assumption #14\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.15. \u003ci\u003eError Assumption #15\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.16. \u003ci\u003eError Assumption #16\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.17. \u003ci\u003eError Assumption #17\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.18. \u003ci\u003eError Assumption #18\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.19. \u003ci\u003eError Assumption #19\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.20. \u003ci\u003eError Assumption #20\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.21. \u003ci\u003eError Assumption #21\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.22. \u003ci\u003eError Assumption -#22\u003c\/i\u003e\u003c\/p\u003e  6.5.23. Error \u003ci\u003eAssumption\u003c\/i\u003e -#23\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.5.24. Error \u003ci\u003eAssumption\u003c\/i\u003e -#24\u003c\/p\u003e  \u003cp\u003e6.5.25. \u003ci\u003eError Assumption -#25\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.26. Error \u003ci\u003eAssumption\u003c\/i\u003e-#26\u003c\/p\u003e  \u003cp\u003e6.5.27. \u003ci\u003eError Assumption-#27\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.5.28. Error \u003ci\u003eAssumption\u003c\/i\u003e-#28\u003c\/p\u003e  \u003cp\u003e6.5.29. Error \u003ci\u003eAssumption\u003c\/i\u003e-#29\u003c\/p\u003e  6.5.30. Error \u003ci\u003eAssumption\u003c\/i\u003e-#30\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e6.5.31. \u003ci\u003eError Assumption-#31\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e6.6. Properties of a Manipulation-Proof Performance Measurement System.\u003c\/p\u003e  \u003cp\u003e6.6.1. Goetzmann, Ingersoll, Spiegel \u0026amp; Welch (2007) – Properties of a “Manipulation proof Performance Measure” (“MPPM”)  \u003c\/p\u003e  \u003cp\u003e6.6.2. New Properties of a Manipulation-Proof Performance System (“MPPS”)    \u003c\/p\u003e  \u003cp\u003e6.7. Conclusion.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 7\u003c\/b\u003e. \u003cb\u003eAnomalies in Taylor-Series; and Tracking-Errors And Homomorphisms in the Returns of Leveraged\/Inverse ETFs and Synthetic ETFs\/Funds.\u003c\/b\u003e    \u003c\/p\u003e  \u003cp\u003e7.1. Inverse\/leveraged ETFs.\u003c\/p\u003e  \u003cp\u003e7.1.1. Existing Literature.\u003c\/p\u003e  \u003cp\u003e7.1.2. Some Biases and Problems Inherent in Leveraged ETFs and Inverse ETFs.\u003c\/p\u003e  \u003cp\u003e7.1.2.1. There cannot be an “Optimal” Degree Of Positive\/Negative Leverage for Leveraged\/Inverse ETFs.\u003c\/p\u003e  \u003cp\u003e7.1.2.2 The Hill \u0026amp; Foster (2009) Study is Misleading and Inaccurate.\u003c\/p\u003e  \u003cp\u003e7.1.2.3. Compounding Has A Significant Effect on Leveraged\/Inverse ETFs. \u003c\/p\u003e  \u003cp\u003e7.1.2.4. Intra-Day Volatility is Irrelevant and Only End-of Day Prices Matter; The Co \u0026amp; Labuszewski (July 2012) Study Is Inaccurate; And Volatility Has Minimal Effects On The \u003ci\u003eDownward Returns Bias\u003c\/i\u003e. \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-1\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e7.1.2.5. Portfolio Re-Balancing by Investors That Own Leveraged\/Inverse ETFs Is Not Always Feasible. \u003c\/p\u003e  \u003cp\u003e7.1.2.6. The Effect of Underlying Indices.\u003c\/p\u003e  \u003cp\u003e7.1.2.7. Leveraged\/Inverse ETFs Are Highly Sensitive To Manipulation Of End-Of-Day Prices and to the Calculation of End-Of-Day Prices.\u003c\/p\u003e  \u003cp\u003e7.1.2.8. Changing Margin Requirements Will Not Be Very Helpful.  \u003c\/p\u003e  \u003cp\u003e7.1.2.9. Leveraged\/Inverse ETFs Are Gambling Tools.\u003c\/p\u003e  \u003cp\u003e7.1.2.10. There Are No Basis for Comparisons of Leverage\/Inverse ETFs to Leveraged Companies (or Leveraged Mutual Funds).    \u003c\/p\u003e  \u003cp\u003e7.1.2.11. Implied Portfolio Weights.   \u003c\/p\u003e  \u003cp\u003e7.1.2.12. The Inaccuracy of the Put Call Parity Theorem, the Early Exercise Premia and the Structure of Leveraged\/Inverse ETFs.  \u003c\/p\u003e  7.1.2.13. Investors Can Replicate the Leverage\/Inverse Effects More Cheaply And More Efficiently By Themselves.   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e7.1.2.14. Risk Return Tradeoff.  \u003c\/p\u003e  \u003cp\u003e7.1.2.15. Suitability \u0026amp; Disclosure.\u003c\/p\u003e  \u003cp\u003e7.1.2.16. Manager-Risk Inherent in Leveraged\/Inverse ETFs.  \u003c\/p\u003e  \u003cp\u003e7.2. Synthetic ETFs and Synthetic Funds.  \u003c\/p\u003e  \u003cp\u003e7.2.1. Existing Literature\u003c\/p\u003e  \u003cp\u003e7.2.2. Synthetic ETFs and Synthetic Index Funds.\u003c\/p\u003e  \u003cp\u003e7.2.2.1. The Inaccuracy of the Put Call Parity Theorem, the Early Exercise Premia and the Structure Of Synthetic Funds\/ ETFs.  \u003c\/p\u003e  \u003cp\u003e7.2.2.2. Implied Portfolio Weights.   \u003c\/p\u003e  \u003cp\u003e7.2.2.3. Some Investors Can Create the Same Economic Effects\/Benefits Of Synthetic Funds\/ETFs More Cheaply and More Efficiently By Themselves.   \u003c\/p\u003e  \u003cp\u003e7.2.2.4. Investment Horizon.\u003c\/p\u003e  \u003cp\u003e7.2.2.5. Counter-Party Credit Risk.\u003c\/p\u003e  \u003cp\u003e7.2.2.6. Tracking Errors and Compounding And Their Effects On Synthetic Funds\/ETFs. \u003c\/p\u003e  \u003cp\u003e7.2.2.7. Changing Margin Requirements Will Not Be Very Helpful.  \u003c\/p\u003e  7.2.2.8. Intra-Day Volatility is Irrelevant and Only End-of Day Prices Matter; The Co \u0026amp; Labuszewski (July 2012) Study is also Inaccurate; and Volatility Has Minimal Effects on The \u003ci\u003eDownward Returns Bias\u003c\/i\u003e. \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e7.2.2.9. The Effect of Underlying Indices.\u003c\/p\u003e  \u003cp\u003e7.2.2.10. Synthetic Funds\/ETFs Are Highly Sensitive to Manipulation of End-Of-Day Prices and to the Calculation of End-Of-Day Prices.\u003c\/p\u003e  \u003cp\u003e7.2.2.11. Manager-Risk Inherent in Synthetic Funds\/ETFs.  \u003c\/p\u003e  \u003cp\u003e7.3. Conclusion.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 8\u003c\/b\u003e.\u003ci\u003e \u003c\/i\u003e\u003cb\u003eSpatio-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.\u003c\/b\u003e \u003c\/p\u003e  8.1. Existing Literature.  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e8.2. Critique of Calculation Methods for Options-Based Indices.\u003c\/p\u003e  \u003cp\u003e8.2.1. Buy-Write Indices.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003ci\u003eTheorem-1\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e8.2.2. The CBOE Put-Write Indices.   \u003c\/p\u003e  \u003cp\u003e\u003ci\u003eTheorem-2\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e8.2.3. The Thomson Reuters “Realized Volatility Index”.\u003c\/p\u003e  \u003cp\u003e8.2.4. VIX Volatility Index. \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e\u003ci\u003eTheorem-3\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e8.2.5. Other Options-Based Indices.     \u003c\/p\u003e  8.3. Conclusion.  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 9.\u003c\/b\u003e\u003cb\u003e Investors’ Preferences, Human-Computer Interaction and Non-Legislative Approaches for Eliminating Index Arbitrage and ETF Arbitrage\u003c\/b\u003e.    \u003c\/p\u003e  \u003cp\u003e9.1. Existing Literature.  \u003c\/p\u003e  \u003cp\u003e9.2. Investor Preferences and Transferable Utilities.  \u003c\/p\u003e  \u003cp\u003e9.2.1. The Chiappori (2007) Conditions. \u003c\/p\u003e  \u003cp\u003e9.3. Optimal Conditions for Reducing\/Eliminating Index Arbitrage and ETF Arbitrage.\u003c\/p\u003e  \u003cp\u003e9.4. The Industry’s Responses to Index Arbitrage and ETF Arbitrage; and Why Index Arbitrage Has Not Been Criminalized. \u003c\/p\u003e  \u003cp\u003e9.4.1. Why Index Arbitrage Has Not Been Criminalized To Date.\u003c\/p\u003e  9.5. New Methods for Eliminating Index Arbitrage.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.5.1. Elimination of Popular Metrics.\u003c\/p\u003e  \u003cp\u003e9.5.2. Delayed Announcement of Index Weights; or Non-Disclosure of Details of Index Revisions.\u003c\/p\u003e  \u003cp\u003e9.5.3. Dynamic Index Revision Dates (Composite Conditional Change).  \u003c\/p\u003e  \u003cp\u003e9.5.4. Change The Structure of Index Futures Contracts.  \u003c\/p\u003e  9.5.5. Change The Structure of Swap Contracts.     \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.5.6. Trading Volume Multiplier.  \u003c\/p\u003e  \u003cp\u003e9.5.7. Implement a Trading Price Multiplier.   \u003c\/p\u003e  \u003cp\u003e9.5.8. Combined Trading Price and Trading Volume Multiplier.  \u003c\/p\u003e  \u003cp\u003e9.5.9. Index-Futures Trading-Volume Multiplier.  \u003c\/p\u003e  \u003cp\u003e9.6. New Methods For Eliminating ETF Arbitrage. \u003c\/p\u003e  \u003cp\u003e9.6.1. Non-Disclosure Of Methodology Of Calculating ETF Portfolio Weights. \u003c\/p\u003e  \u003cp\u003e9.6.2. Eliminate “Popular Metrics” in Indices. \u003c\/p\u003e  \u003cp\u003e9.6.3. Dynamic Conditional Re-Balancing of The ETF. \u003c\/p\u003e  \u003cp\u003e9.6.4. There Should Not Be Any Exchange of the ETF’s Creation Units - the Creation and Redemption Processes for Traditional ETFs are Flawed\u003c\/p\u003e  9.6.5. The Implicit Interest Rates for Shorting ETF Shares Should Be Increased.  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.6.6. “\u003ci\u003eState Contingent\u003c\/i\u003e” ETF Shares.  \u003c\/p\u003e  \u003cp\u003e9.6.7. \u003ci\u003eVolume-Contingent\u003c\/i\u003e Dissolution of ETFs.   \u003c\/p\u003e  \u003cp\u003e9.6.8. Index Futures–Contingent Dissolution or Re-Creation of ETF.  \u003c\/p\u003e  9.6.9. Money Supply Linked ETF.  \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e9.7. The Economic Rationale for Making Index Arbitrage and ETF Arbitrage Illegal; and New Theories of Liability Against Perpetrators of Index Arbitrage and ETF Arbitrage.  \u003c\/p\u003e  \u003cp\u003e9.8. Conclusion.  \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 10. Eliciting Investors’ Preferences: Some New Index-Calculation Methods and their Mathematical Properties.\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e10.1. Existing Literature.\u003c\/p\u003e  \u003cp\u003e10.2. Investor Preferences, Transferable Utilities and Optimal Conditions for Indices.\u003c\/p\u003e  \u003cp\u003e10.3. New Index Calculation\/Weighting Methods.  \u003c\/p\u003e  \u003cp\u003e10.3.1. Broad Market Index-1™.\u003c\/p\u003e  10.3.2. Broad Market Index-2™.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e10.3.3. Broad Market Index-3™.\u003c\/p\u003e  \u003cp\u003e10.3.4. Broad Market Index-4™.\u003c\/p\u003e  \u003cp\u003e            10.3.5. Broad Market Index-5™.\u003c\/p\u003e  \u003cp\u003e            10.3.6. Broad Market Index-6™.  \u003c\/p\u003e  \u003cp\u003e            10.3.7. Broad Market Index-7™.  \u003c\/p\u003e  \u003cp\u003e            10.3.8. Broad Market Index-8™.\u003c\/p\u003e  \u003cp\u003e            10.3.9. Broad Market Index-9™.\u003c\/p\u003e  \u003cp\u003e            10.3.10. Broad Market Index-10™.  \u003c\/p\u003e  \u003cp\u003e            10.3.11. Broad Market Index-11™.  \u003c\/p\u003e  \u003cp\u003e            10.3.12. Broad Market Index-12™.\u003c\/p\u003e  \u003cp\u003e            10.3.13. Broad Market Index-13™.\u003c\/p\u003e  \u003cp\u003e            10.3.14. Broad Market Index-14™.\u003c\/p\u003e  \u003cp\u003e            10.3.15. Broad Market Index-15™.\u003c\/p\u003e  \u003cp\u003e            10.3.16. Broad Market Index-16™.\u003c\/p\u003e  \u003cp\u003e            10.3.17. Broad Market Index-17™.\u003c\/p\u003e  \u003cp\u003e            10.3.18. Broad Market Index-18™.\u003c\/p\u003e  \u003cp\u003e            10.3.19. Broad Market Index-19™.\u003c\/p\u003e  \u003cp\u003e            10.3.20. Broad Market Index-20™.\u003c\/p\u003e  \u003cp\u003e            10.3.21. Broad Market Index-21™.\u003c\/p\u003e  \u003cp\u003e            10.3.22. Broad Market Index-22™.\u003c\/p\u003e  \u003cp\u003e            10.3.23. Broad Market Index-23™.\u003c\/p\u003e  \u003cp\u003e            10.3.24. Broad Market Index-24™.\u003c\/p\u003e  \u003cp\u003e            10.3.25. Broad Market Index-25™.\u003c\/p\u003e  \u003cp\u003e            10.3.26. Broad Market Index-26™.\u003c\/p\u003e  \u003cp\u003e            10.3.27. Broad Market Index-27™.\u003c\/p\u003e  \u003cp\u003e            10.3.28. Broad Market Index-28™.\u003c\/p\u003e              10.3.29. Broad Market Index-29™.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e            10.3.30. Factor Index-1 (Operational Risk).\u003c\/p\u003e  \u003cp\u003e10.3.31. Factor Index-2: Value.  \u003c\/p\u003e  \u003cp\u003e            10.3.32. Factor Index-3: Value.\u003c\/p\u003e  10.4. Conclusion.     \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e  \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 11. \u003c\/b\u003e\u003cb\u003eStock-Indices and Strategic Alliances Invalidate Third-Generation Prospect Theory, Related Approaches and Intertemporal Asset Pricing Theory: HCI and Three New Decision Models.      \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e11.1. Existing Literature.          \u003c\/p\u003e  \u003cp\u003e11.2. 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.            \u003c\/p\u003e  \u003cp\u003e11.3. RAIs, Fundamental Indices and Game Theory.  \u003c\/p\u003e  \u003cp\u003e11.4. RAIs and Options Based Indices (OIs) Can Cause Systemic Risk.          \u003c\/p\u003e  \u003cp\u003e11.5. Errors in Some Studies Of CPT\/PT\/PT\u003csup\u003e3\u003c\/sup\u003e in the Context of Financial Decisions.\u003c\/p\u003e  \u003cp\u003e11.6. The Invalidity Of PT\/CPT\/PT\u003csup\u003e3\u003c\/sup\u003e and Related Approaches.\u003c\/p\u003e  \u003cp\u003e11.7. PT-Portfolios, CPT-Portfolios and PT\u003csup\u003e3\u003c\/sup\u003e Portfolios (and Related Portfolios) Can Cause Substantial Systemic-Risk\/Contagion and Financial Instability.         \u003c\/p\u003e  \u003cp\u003e11.8. Intertemporal Strategic Alliances (ITSA) and Joint Ventures (ITJV) as Elements Of Regulation; and as Evidence of the Invalidity of the Intertemporal Asset Pricing Models.\u003c\/p\u003e  \u003cp\u003e11.9. RAIs, Fundamental Indices and Options-Based Indices as Asset Pricing Models That Contravene Most Theories of Intertemporal Asset Pricing.            \u003c\/p\u003e  \u003cp\u003e11.10. Three New Models of Decision-Making That Are Derived From the Structure of Indices and Associated Investor Preferences.     \u003c\/p\u003e  \u003cp\u003e11.10.1. The \u003ci\u003eMN Type-I Decision Model\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e11.10.2. The \u003ci\u003eMN Type-II Decision Model\u003c\/i\u003e.            \u003c\/p\u003e  \u003cp\u003e11.10.3. The \u003ci\u003eMN Type-III Decision Model\u003c\/i\u003e.           \u003c\/p\u003e  \u003cp\u003e11.11. Conclusion.      \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 12\u003c\/b\u003e.\u003cb\u003e Economic Policy and “\u003ci\u003ePopular-Index Ecosystems\u003c\/i\u003e”: Managerial Psychology, Human-Computer Interaction, Corporate Governance and Risk Effects.  \u003c\/b\u003e  \u003c\/p\u003e  \u003cp\u003e12.1. Introduction.  \u003c\/p\u003e  \u003cp\u003e12.2. Existing Literature.  \u003c\/p\u003e  \u003cp\u003e12.3. 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.\u003c\/p\u003e  \u003cp\u003e            12.3.1. The \u003ci\u003ePopular-Index Ecosystems\u003c\/i\u003e increase Systemic Risk and Financial Instability.\u003c\/p\u003e  \u003cp\u003e            12.3.2. Increased “Herding” Behavior.\u003c\/p\u003e  \u003cp\u003e12.3.3. Over-Investment in Popular-Indices, and the Resulting Under-Investment in Other Companies Around the World; and Increased Systemic Risk and Financial Instability.\u003c\/p\u003e  \u003cp\u003e12.4. Characterization of The \u003ci\u003ePopular-Index Ecosystems\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.4.1. Operational Contagion and Corporate Governance Contagion.\u003c\/p\u003e  \u003cp\u003e            12.4.2. Prioritization of Stakeholders.\u003c\/p\u003e  \u003cp\u003e            12.4.3. Self-Propagation.\u003c\/p\u003e  \u003cp\u003e            12.4.4. Self-replication.\u003c\/p\u003e  \u003cp\u003e            12.4.5. Short term focus.  \u003c\/p\u003e  \u003cp\u003e            12.4.6. \u003ci\u003eSuper-Additive Group Information Dominance Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.4.7. \u003ci\u003eInformation Chain Alliance Volatility\u003c\/i\u003e \u003ci\u003eTheory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.4.8. \u003ci\u003eInformation-Chain Execution Gaps Theory\u003c\/i\u003e.\u003c\/p\u003e              12.4.9. Information Production Capabilities.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e            12.4.10. Low Merger Activity.\u003c\/p\u003e  \u003cp\u003e            12.4.11. Under-Investment in technology Portfolios.\u003c\/p\u003e  \u003cp\u003e            12.4.12. Share Repurchases.  \u003c\/p\u003e              12.4.13. Exploration and “Exploitation Activities”.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e            12.4.14. Congruence Between Corporate Strategies And Financial Management.  \u003c\/p\u003e  \u003cp\u003e            12.4.15. Un-Intended Wealth Transfers.\u003c\/p\u003e  \u003cp\u003e            12.4.16. Managerial Entrenchment.\u003c\/p\u003e  \u003cp\u003e12.5. Other Problems Inherent in the Popular-Index Ecosystems.\u003c\/p\u003e  \u003cp\u003e12.5.1. The Possible Effects of the Popular-Index Ecosystems On Organizational Behavior and Group Decisions.  \u003c\/p\u003e  \u003cp\u003e            12.5.1.1. Inclusion Pressure.\u003c\/p\u003e  \u003cp\u003e            12.5.1.2. Deletion Pressure.  \u003c\/p\u003e  \u003cp\u003e            12.5.1.3. Corporate Governance Contagion.\u003c\/p\u003e  \u003cp\u003e            12.5.1.4. Human Capital Contagion.\u003c\/p\u003e  \u003cp\u003e            12.5.1.5. Excessive Managerial Risk-Taking.\u003c\/p\u003e  \u003cp\u003e            12.5.1.6. Distorted Incentives.\u003c\/p\u003e  \u003cp\u003e            12.5.1.7. Aggregate Super-Additivity.\u003c\/p\u003e  \u003cp\u003e            12.5.1.8. Managers’ Homomorphic Utility Functions.  \u003c\/p\u003e  \u003cp\u003e            12.5.1.9. Asymmetric Risk Reactions.   \u003c\/p\u003e  \u003cp\u003e            12.5.1.10. Contingent Renegotiation-Proofness.\u003c\/p\u003e  \u003cp\u003e            12.5.1.11. Sequential Bargaining.\u003c\/p\u003e  \u003cp\u003e            12.5.1.12. The Cumulative Non-Separability of aggregated managers’ utility-functions.\u003c\/p\u003e  \u003cp\u003e            12.5.1.13. The “Long-Memory” component of managers’ capital allocation decisions.\u003c\/p\u003e  \u003cp\u003e            12.5.1.14. Contingent Aggregate Rationality of Managers.\u003c\/p\u003e  \u003cp\u003e            12.5.1.15. Managerial Manipulation.\u003c\/p\u003e  \u003cp\u003e            12.5.2.16. Preference Matching.\u003c\/p\u003e  \u003cp\u003e            12.5.2.17. Substitutability of Managers.\u003c\/p\u003e  \u003cp\u003e            12.5.1.18. Substitutability of Managerial Compensation.\u003c\/p\u003e  \u003cp\u003e            12.5.1.19. Managers’ Willingness to Accept Losses (WTAL).\u003c\/p\u003e  \u003cp\u003e            12.5.1.20. Self-Insurance.\u003c\/p\u003e  \u003cp\u003e            12.5.1.21. The Monotonicity of Managerial “Compliance Functions”.  \u003c\/p\u003e  \u003cp\u003e12.6. 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.    \u003c\/p\u003e  \u003cp\u003e            12.6.1. Significant Tax-Evasion by Fortune-500 Companies.   \u003c\/p\u003e  \u003cp\u003e12.6.2. The Periodic Changes in the Cash Balances and Cash-Equivalents of S\u0026amp;P-500 Companies Didn’t Match Changes in their Real Earnings.  \u003c\/p\u003e  \u003cp\u003e12.6.3. Many S\u0026amp;P 500 Companies Didn’t Provide Adequate Disclosure About Their Accelerated Share Repurchase Program (ASR) and ASRs Are,or May Be Illegal.\u003c\/p\u003e  \u003cp\u003e12.6.4. Many S\u0026amp;P 500 Companies Didn’t Provide Sufficient Disclosures About Their Dividend Equivalent Rights (“DERs”); and DERs are or Maybe Illegal.  \u003c\/p\u003e  \u003cp\u003e            12.6.5. Option-Grant Backdating.\u003c\/p\u003e  \u003cp\u003e12.6.6. Earnings Management and Asset-Quality Management by Other Popular-Index Companies In Europe, Asia And Latin-America During 2000-2017.\u003c\/p\u003e  \u003cp\u003e12.7. Human Behavior Issues.\u003c\/p\u003e  \u003cp\u003e            12.7.1. Evidence; And Theories of Corporate Governance and Organizational Psychology.  \u003c\/p\u003e  \u003cp\u003e            12.7.1.1. \u003ci\u003eStandardization Illusions Bias\u003c\/i\u003e.  \u003c\/p\u003e  \u003cp\u003e            12.7.1.2. \u003ci\u003eRisk-Horizon Contingent Cognition Theory\u003c\/i\u003e (Group Cognition Dissonance).\u003c\/p\u003e  \u003cp\u003e            12.7.1.3. \u003ci\u003eUniformity Inertia Bias\u003c\/i\u003e.\u003c\/p\u003e              12.7.1.4. \u003ci\u003eIncentive Neutrality Theory\u003c\/i\u003e.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e            12.7.1.5. \u003ci\u003eSalary \u0026amp; Tenure Neutrality Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.6. \u003ci\u003eReversibility Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.7. The \u003ci\u003eDynamic Reference-Points Bias\u003c\/i\u003e.  \u003c\/p\u003e  \u003cp\u003e            12.7.1.8. \u003ci\u003eTemporal Disassociation Theory\u003c\/i\u003e and \u003ci\u003eTemporal Cohesion Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.9. \u003ci\u003eSub-Additive Group-Regret\u003c\/i\u003e And \u003ci\u003eSuper-Additive Group-Regret\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.10. Preference For Declining Or Constant Returns To Losses.  \u003c\/p\u003e  \u003cp\u003e            12.7.1.11. Event Driven Over-dependence Theory.\u003c\/p\u003e  \u003cp\u003e            12.7.1.12. High Error-Sensitivity And \u003ci\u003eNegative-Information Sensitivity Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.13. \u003ci\u003eKnowledge-mediated Splits Theory\u003c\/i\u003e.\u003c\/p\u003e              12.7.1.14. \u003ci\u003eTime-Consistent Preferences Bias\u003c\/i\u003e.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e            12.7.1.15. \u003ci\u003eWillingness To Accept Losses\u003c\/i\u003e (WTAL).\u003c\/p\u003e  \u003cp\u003e            12.7.1.16. Disappointment Aversion.\u003c\/p\u003e  \u003cp\u003e            12.7.1.17. Framing Effects and or Static Risk Management.\u003c\/p\u003e  \u003cp\u003e            12.7.1.18. \u003ci\u003eCoalition Formation Synthesis Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.19. \u003ci\u003eSub-Additive Loss Internalization Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.20. \u003ci\u003eSelective Risk Tolerance Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.21. Complex “higher-order behaviors”.\u003c\/p\u003e  \u003cp\u003e12.7.1.22. Corporate Governance Statutes And Corporations’ Strategies\/Mechanisms\/Alliances As Non-Public Goods (that may be created, diminished or amplified by Political Influence And Lobbying).\u003c\/p\u003e  \u003cp\u003e            12.7.1.23. Enforcement Leakages.\u003c\/p\u003e  \u003cp\u003e            12.7.1.24. \u003ci\u003eThe Sub-optimal Investment Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.25. \u003ci\u003eStrategy Permeation Deficits Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.26. Deadweight Losses.\u003c\/p\u003e  \u003cp\u003e            12.7.1.27. Entrenchment.\u003c\/p\u003e  \u003cp\u003e            12.7.1.28. Selective Concern for Social Welfare.\u003c\/p\u003e  \u003cp\u003e            12.7.1.29. The\u003ci\u003e Policy-Dampening Alliance Theory\u003c\/i\u003e.\u003c\/p\u003e  \u003cp\u003e            12.7.1.30. The \u003ci\u003eDynamic Coordination-Gaps Theory\u003c\/i\u003e.  \u003c\/p\u003e  \u003cp\u003e            12.7.1.31. Resource Allocation Efficiency Deficits.\u003c\/p\u003e  \u003cp\u003e            12.7.1.32. The Sub-optimally Exercised Time-Varying Asymmetric Power Theory.\u003c\/p\u003e  \u003cp\u003e12.7.1.33. Regulatory Failure (that may be caused or amplified by Political Influence and Lobbying).\u003c\/p\u003e  \u003cp\u003e12.8. Conclusion. \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 13. Conclusion: Implications for Decision Theory, Enforcement, Financial Stability and Systemic Risk. \u003c\/b\u003e      \u003c\/p\u003e  13.1. Misrepresentation and Implications for Legislation and Enforcement.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e13.2. Implications for Decision Theory (Cumulative Prospect Theory and Third-Generation-Prospect-Theory (PT\u003csup\u003e3\u003c\/sup\u003e)).  \u003c\/p\u003e  \u003cp\u003e13.3. Implications for Game Theory.\u003c\/p\u003e  \u003cp\u003e13.4. Implications of Indices and Index Funds for Nonlinear Systemic Risk and Nonlinear Financial Instability.\u003c\/p\u003e  \u003cp\u003e13.5. Implications of ETFs for Nonlinear Systemic Risk and Nonlinear Financial Instability.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 14. Bibliography.   \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Palgrave Macmillan","offers":[{"title":"Default Title","offer_id":51019441865047,"sku":"9781137447005","price":80.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781137447005.jpg?v=1750780281","url":"https:\/\/bookcurl.com\/products\/indices-index-funds-and-etfs-exploring-hci-nonlinear-risk-and-homomorphisms-9781137447005","provider":"Book Curl","version":"1.0","type":"link"}