Indices, index funds and exchange-traded funds (ETFs) have become major asset classes in debt, equity, real estate, derivatives, currency and commodity markets worldwideāand their management, maintenance and use often occurs within the context of humanācomputer interactions (HCI). As of 2018, there were more stock indices in the world than the number of exchange-traded companies.1 The significant growth of Indices, passive/active ETFs (exchange traded funds) and index funds during 1995ā20182 (combined with the Internet; increasing volume of cross-border transactions; and improved global settlement/clearing systems) have increased the potential for systemic risk, financial instability and the failures of regulations. The major problem is that more than US$3.5 trillion is invested in indices through ETFs, index funds and equity swaps, apparently without adequate consideration of risk, business quality and valuation. Some of the net effects are that (i) the companies in these indices are overvalued and enjoy artificial price support (from these ETFs and index funds); (ii) there is significant overinvestment in the underlying companies (of the Indices, ETFs and index funds) and underinvestment in non-listed, small-cap, startup, micro-cap and emerging markets companies, which affects economic growth, sustainability, development and capital mobility; and (iii) these indices, index funds and ETF and their component companies pose increasing sustainability (economic, social, environmental and urban sustainability), systemic risk3 and financial instability4 threats.
Given the pervasiveness of automated financial services and electronic trading, ETFs, indices and index funds function almost entirely within the context of humanācomputer interaction factors (i.e. cognition and perception, user-interfaces, human biases, contagion across social networks, noise, psychological effects, systems-reliability, etc.) and latent/inherent mathematical issues (e.g. nonlinearity, homomorphisms, biases, etc.) but the associated problems have not been fully analyzed in the literature. Most of these indices, index funds and ETFs are maintained and managed with huge reserves of computing power and technical staff (programmers, computer engineers, systems engineers, math specialists, servers, bandwidth, and network infrastructure); and are calculated and processed by automated software systems (that collect and use real-time data from markets).
As of 2018, there were more than 4779 ETFs worldwide whose total assets exceeded US$3.5 trillion; and more than 60% of the ETFs were based or listed in the US. Indices are the foundation and āCoreā for more than US$5 trillion that have been invested in index funds and index-based ETFs worldwide (and the equivalent of hundreds of billions, if not trillions, of dollars are invested in equity swaps, index options and index futures around the world). Stock indices are the basis for various types of international transactions (equity swaps, equity-linked debt, plain/currency-linked index options, executive compensation, competitive benchmarking, etc.) and foreign āpersonsā purchase index-based financial products. Thus the impact of financial indices on sustainability, the global economy and national economies is significant.
As of 2018, the major ETF providers were US-based companies such as Blackrock, State Street, Vanguard, Powershares, Charles Schwab, First Trust, Van Eck Vectors, Wisdom Tree, Guggenheim Investors and ProShares. Some of the top providers of Mutual Funds and index funds are Vanguard, Fidelity Investments, Capital Group, JP Morgan Chase, T. Rowe Price, Blackrock, Franklin Templeton Investments, PIMCO and Dimensional Fund Advisors.
In the US and some other countries, there are three principal types of index funds, which are open-end funds, unit investment trusts (UITs) and closed-end funds. Most US ETFs are exchange-traded open-end funds or unit investment trusts. Some index funds and ETFs are ātraditionalā (created with actual exchange-traded securities) while others are āsyntheticā (created with only swaps and/or futures). In most countries, investors in index funds and ETFs pay five types of fees, which are the management fee, shareholder transaction fees, distribution charges (sales loads and 12b-1 fees), securities transaction fees and fund services charges. Some of these expenses reduce the value of an investorās account while the fund pays other fees, which reduces its net asset value. See: Pozen and Hamacher (2015), Lemke et al. (2016), Fink (2011), and Investment Company Institute (2016). Although ETFs and index funds are more popular in the US, they are very likely to both gain a greater share of fund assets outside the US, and to increase in absolute volume due to concerns about fees charged by Mutual Funds; concerns about liquidity, transparency and pricing of funds; popularity of active and passive index investing; increased investor education; and the growth of capital markets in Japan, China, Mexico, India, Brazil, the MENA region, Thailand, Indonesia, Eastern Europe and Australia. Meziani (2016) provides a general overview of various types of ETFs and their uses in asset management, but that book is intended for a more general audience.
One major issue is the pervasive effects of the combinations of regulation (e.g. stop-trading orders by regulators, trading limitations, circuit-breakers, etc.), indices, index funds, ETF Arbitrage and greed. Given the discussions in Chaps. 3, 7 and 8 of this book, lawsuits against banks and securities brokerages in several countries for rigging of CDS markets and bond markets (and associated settlements), the prevalence of Index Arbitrage and ETF Arbitrage, and the heavy regulation of stock, commodity and bond markets, it appears that many of these markets are, or can be, rigged. It is now clear that āregulationā by itself can be a form of rigging, for example, where circuit breakers and/or trading rules in markets are unreasonable or, effectively, amount to collusion. See the comments in Diamond and Kuan (2018).
Indices, ETFs and index funds and their associated index methodologies and creation/rebalancing methods are essentially Algorithms, an interdisciplinary topic that has been analyzed mostly from mathematics (nonlinearity; dynamical systems),5 finance/economics,6 operations research,7 applied mathematics,8 statistics,9 computer science10 and physics11 (statistical physics, nonlinearity, complex systems) perspectives. However, most (if not all) of those articles, books and models are wrong or misleading because they donāt consider the biases, misrepresentations, conflicts of interest and āStructural Effectsā introduced in this book and in Nwogugu (2017a, b, c). Furthermore, these same biases, errors and āStructural Effectsā can have significant effects on sustainability (economic, social, environmental and urban sustainability) because they affect and/or determine worldwide capital allocation, expectations, inequality, noise, optimism, the mental states of humans, and the production activity and urban interactions that can reduce sustainability efforts (see Chap. 13). The term āStructural Effectsā refers to biases, errors and effects that arise solely from the nature/structure of the mathematical formulas for the index and/or the method of constructing ETFs/ETNs/Mutual-Funds (thus, Structural Effects are not caused by human psychology, trading patterns, trading rules, decimalization of prices, etc.).
Another important related result is that the theorems in Nwogugu (2013) and Chap. 4 in this book about the invalidity of the MeanāVariance Framework renders null and void most (if not all) analysis about:
- (1)Global Sens...