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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

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Abstract

Risk-adjusted indices, index tracking funds and ETFs have grown in popularity but have many structural and tracking-error problems that raise actionable issues of “suitability” and “fraud” under securities laws. This chapter contributes to the existing literature by (a) introducing and characterizing the errors and biases inherent in “risk-adjusted” index weighting methods and the associated adverse effects; and (b) showing how these biases/effects inherent in index calculation methods can reduce social welfare, amplify financial instability, systemic risk and harmful arbitrage activities.

This chapter is an excerpt from Michael C. Nwogugu’s article that was published in Discrete Mathematics, Algorithms & Applications in 2013.

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  1. Enugu, Nigeria

    Michael I. C. Nwogugu

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  1. Michael I. C. Nwogugu
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Nwogugu, M.I.C. (2018). 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. In: Indices, Index Funds And ETFs. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-44701-2_5

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  • DOI: https://doi.org/10.1057/978-1-137-44701-2_5

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