Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2018 | OriginalPaper | Chapter

1. Introduction

Author : Michael I. C. Nwogugu

Published in: Indices, Index Funds And ETFs

Publisher: Palgrave Macmillan UK

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Indices, index funds and exchange-traded funds (ETFs) have become major asset classes in debt, equity, real estate, currency and commodity markets worldwide—and their management, maintenance and use often occurs within the context of human–computer interactions (HCI). As of 2018, there were more indices in the world than the number of exchange-traded companies. The relatively sudden and significant growth of indices, passive/active ETFs and index funds during 1995–2018 (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 failure 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 regard to the quality and valuation of the underlying companies and commodities. The net effects are that: (i) the companies and commodities in these indices are overvalued and enjoy artificial price support (from these ETFs and index funds); (ii) there is substantial over-investment and “Gambling” in the underlying companies and under-investment in non-listed, micro-cap, small-cap and emerging markets companies, which affects economic growth, development and capital mobility; and (iii) these indices, index funds and ETF and their component companies pose increasing systemic risk and financial instability threats.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter 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 Regret–Minimization Regimes
Footnotes
1
See: Authers, J. (January 22, 2018). “Number Of Stock Indices At 3 Million Dwarfs Tally Of Quoted Companies—Proliferation Reflects Investor Focus On ‘Top-Down’ Analysis Of Markets”. Financial Times (UK). https://​www.​ft.​com/​content/​9ad80998-fed5-11e7-9650-9c0ad2d7c5b5. According to a survey by the 14-member Index Industry Association, its members publish and calculate 3.28 million indices, of which 3.14 million are for stock markets, but the World Bank notes that there are only 43,192 public companies around the world, and ETFGI notes that there are only about 7,178 ETFs and other exchange-traded products globally. The breakdown of global equity indices is as follows: sector/industry (42.5%), total market cap (14.8%), small cap (13.6%), large cap (10.8%), mid-cap (10.7%), factor/Beta (5.6%), theme/other (1.5%), and environmental/social/governance (0.3%). Geographically, the 2018 statics are: global indices (29%), frontier and emerging markets (14%), and the US (9%).
 
2
See: Chaparro, F. (March 18, 2017). “There could be a US$3 trillion shift in investing, and it poses a huge problem for mutual funds”. http://​pulse.​ng/​bi/​finance/​finance-there-could-be-a-3-trillion-shift-in-investing-and-it-poses-a-huge-problem-for-mutual-funds-id6390294.​html. This article states that:
(i)
According to a March 2017 report by Morgan Stanley and Oliver Wyman, ETFs could gain an additional US$2 trillion to US$3 trillion in assets during 2017–2022.
 
(ii)
ETFs are deemed to be typically cheaper and more transparent than Mutual Funds, and Mutual Funds have struggled to achieve performance in recent years (in most instances, disclosed calculated fees don’t include non-disclosed “costs” such as re-balancing costs, monitoring costs of both ETF-managers and investors, and ETF-arbitrage costs). Investors moved money from Mutual Funds and into ETFs during 2014–2017 and that has forced Mutual Funds to reduce their fees. ETFs’ market share in the US market alone is likely to increase from 15% to 40–60% during 2017–2027. According to the Morgan Stanley and Oliver Wyman 2017 report, Mutual Funds are now using ETFs to reduce their own costs; and “Asset allocators such as Outsourced Chief Investment Officers (OCIO) and Wealth Managers will account for a large proportion of this incremental demand as they increasingly use ETFs at near zero cost to source Beta exposure, allowing them to focus their resources on high conviction managers or more complex alternative investments. However, looking beyond 2019, the emerging use of passive vehicles as an integral part of an active fund management strategy will be arguably the more significant dynamic. Currently, Mutual Funds have ~$0.5 trillion invested in ETFs, much of which is used for liquidity management. We estimate using ETFs rather than the traditional approach of holding individual stocks offers a cost advantage of 5–8 bps in large and mid-cap equities. As Asset Managers search for ways to deliver performance at lower costs, this may mean that mutual funds will find themselves among the largest investors in ETFs…”.
 
(iii)
According to Credit Suisse, the reduction of fees in the mutual fund industry will likely continue. Morgan Stanley estimates that fees charged by active managers could decrease by more than 33% in 2017.
 
 
3
On systemic risk/contagion, see: Bahmani-Oskooee and Saha (2016), Naresh et al. (2017), Aloui et al. (2017), Yarovaya et al. (2016), Goswami et al. (2012), Gajardo and Kristjanpoller (2017), Keylock (2018), Kristoufek (2010), Bhuiyan et al. (2017), Ahmad et al. (2018), Zhou and Chen (2016), Curcio et al. (2014), Bhattacharya and O’Hara (2016), Bouri et al. (2017), and Puy (2016).
 
4
On financial instability, see: Al-Khazali and Mirzaei (2017), Financial Stability Board (2011), U.S. Senate (October 2011), Kosev and Williams (2011), Ivanov and Lenkey (2014), Aldridge (2014, 2016), Linnertova (2015), Chen and Li (2014), Wang et al. (2016), Lobão and Pereira (2017), Charles et al. (2017), Prasanna and Menon (2013), Stošić et al. (2015), Ferreira et al. (2018), Gil-Alana et al. (2013), Rizvi and Arshad (2017), Ahmad et al. (2018), Abu-Alkheil et al. (2017), Singh et al. (2018), Donders et al. (2017), Nagayev et al. (2016), Chacko et al. (2016), Xu and Yin (2017), Chiu and Tsai (2017), Marszk and Lechman (2018), Dannhauser (2017), Lechman and Marszk (2015), and Deev and Linnertová (2014).
 
5
See: Duarte et al. (2010), Chen et al. (2017), Lee et al. (2017), Tsionas and Michaelides (2017), Bonanno et al. (2004), Harré and Bossomaier (2009), Kenett et al. (2012), Sornette (2003), and Keylock (2018).
 
6
See: Ozer and Ertokatli (2010), Sasikumar and Kamaiah (2014), Aldridge (2016), Broman (2016), Kaiser et al. (2018), Badshah et al. (2018), Wisniewski (2016), Jouini (2013), Abu-Alkheil et al. (2017), Vortelinos et al. (2018), Christou et al. (2017), Li et al. (2016), Li and Peng (2017), Ho and Huang (2015), Tang and Xu (2013), and Abhyankar et al. (1997).
 
7
See: Chen et al. (2010), Oztekin et al. (2016), Schellhorn (2011), Wang et al. (2012), Feuerriegel and Gordon (2018), and Chiang et al. (2016).
 
8
See: Avellaneda and Zhang (2010), Preis et al. (2011), and Lahmiri (2018).
 
9
See: White (2007), and Levell (2015).
 
10
See: Malagrino et al. (2018), Chen and Hao (2017), Cao et al. (2013), Anbalagan and Maheswari (2015), Ng et al. (2014), Arnoldi (2016), Shahzad et al. (2018), Chen et al. (2010), Nwogugu (2013), Martyn et al. (2012), Steel et al. (2007), Jacob et al. (2013), Cheng et al. (2010), Lee et al. (2006), Yu and Huarng (2008, 2010), and Roy and Sarkar (2011).
 
11
See: Wang et al. (2016), Reigneron et al. (2011), Nobi et al. (2014), Shi et al. (2016), Gong et al. (2016), Sandoval (2014), Shapira et al. (2009), Haluszczynski et al. (2017), Kwon and Yang (2008), and Gao et al. (2018).
 
12
See the AI and decision models in Castro and Parsons (2014), Domshlak et al. (2011), Grishina et al. (2017), Nadendla et al. (Oct. 2016), Song et al. (2017), Rekik et al. (2014), Correia et al., eds. (2014), and Marwala (2013).
 
13
Moore, S. (Aug 29, 2014). How Securities Lending Makes Some ETFs Free. https://​www.​forbes.​com/​sites/​simonmoore/​2014/​08/​29/​securities-lending-makes-some-etfs-free/​#66fd4f673d6f. This article stated in part: “The amount of securities lending profits that ETF sponsors pass onto ETF investors varies, but appears to be increasing. Blackrock was the subject of a lawsuit contesting their return of securities lending fees to investors, that case was dismissed last year. As of the start of this year Blackrock (the owner of iShares) returns 70%–75% of securities lending revenue to investors. Vanguard returns 100% of securities lending proceeds to investors after their costs. As Dave Nadig of ETF.com argues the difference isn’t just in the revenue/profit split which is calculated in a slightly different way by the different firms, it’s also in the policies for lending, Blackrock appears to lend more broadly, hence increasing potential lending returns, but also raising the risk level slightly, whereas Vanguard may be more selective in identifying lending opportunities with greater profitability.”
See: Blocher and Whaley (May 2016).
 
14
See: Christie and Schultz (1994), Khwaja and Mian (2005), Jiang et al. (2013), Aggarwal and Wu (2003), Lin (2017), Allen et al. (2006), and The Economist (Jan. 15, 1998).
 
15
See: Arnoldi (2016), Lee et al. (2013), Lin (2017), Cao et al. (2016), and Jiang et al. (2013).
 
16
See: Brennan, T. (Jan 26, 2011). Cramer: Case-Schiller Not a Good Index for Housing. CNBC. http://​www.​cnbc.​com/​id/​41274473. This article stated in part: “The Case-Shiller index measures only 20 U.S. markets. It’s nowhere near as representative as people make it out to be. Cramer thinks the Federal Housing Finance Agency’s index is far better. Its numbers are calculated by ZIP code, using the purchase prices of houses that back mortgages that have been sold to, or guaranteed by Fannie Mae or Freddie Mac. As Cramer said, ‘It’s a super-granular housing report based on a much larger data set than the 20 cities of the Case-Shiller.’ And what did the FHFA index say about home prices just yesterday? That they were unchanged from October to November on a seasonally adjusted basis… .The bottom line here is that investors should forget the negative press reports and the Case-Shiller index, and focus instead on the FHFA, the NAR and even the housing stocks for an accurate picture of what’s going on.”
See Ro, S. (March 29, 2012). S&P: We Know The Case-Shiller Home Price Index Has Problems But There’s Nothing We Can Do About It. http://​www.​businessinsider.​com/​sp-blitzer-case-shiller-home-price-index-2012-3
 
Literature
Abhyankar, A., Copeland, L., & Wong, W. (1997). Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100. Journal of Business & Economic Statistics, 15(1), 1–14.
Abu-Alkheil, A., Khan, W., et al. (2017). Dynamic co-integration and portfolio diversification of Islamic and conventional indices: Global evidence. The Quarterly Review of Economics and Finance, 66, 212–224.
Ahmad, W., Mishra, A., & Daly, K. (2018). Financial connectedness of BRICS and global sovereign bond markets. Emerging Markets Review, in press.
Al-Khazali, O., & Mirzaei, A. (2017). Stock market anomalies, market efficiency and the adaptive market hypothesis: Evidence from Islamic stock indices. Journal of International Financial Markets, Institutions and Money, 51, 190–208.
Aldridge, I. (2014). High-frequency runs and flash crash predictability. Journal of Portfolio Management, 40(3), 113–123.
Aldridge, A. (2016). ETFs, high-frequency trading, and flash crashes. Journal of Portfolio Management, 43(1), 17–28.
Allen, F., Litov, L., & Mei, J. (2006). Large investors, price manipulation, and limits to arbitrage: An anatomy of market corners. Review of Finance, 10(4), 645–693.
Aloui, C., Hkiri, H., Lau, M., & Yarovaya, L. (2017). Information transmission across stock indices and stock index futures: International evidence using wavelet framework. Research in International Business and Finance, in press.
Amenc, N., Goltz, F., & Le Sourd, V. (2009). The performance of characteristics-based indices. European Financial Management, 15(2), 241–278.
Anbalagan, T., & Maheswari, U. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214–221.
Angel, J., Broms, T., & Gastineau, G. (2016). ETF transaction costs are often higher than investors realize. Journal of Portfolio Management, 42(3), 65–75.
Arnoldi, J. (2016). Computer algorithms, market manipulation and the institutionalization of high frequency trading. Theory, Culture & Society, 33(1), 29–52.
Athma, P., & Kumar, R. K. (2011). ETF Vis-à-Vis index funds: An evaluation. Asia Pacific Journal of Research in Business Management, 2(1), 188–205.
Avellaneda, M., & Zhang, S. (2010). Path-dependence of leveraged ETF returns. SIAM Journal of Financial Mathematics, 1, 586–603.
Badshah, I., Bekiros, S., et al. (2018). Asymmetric linkages among the fear index and emerging market volatility indices. Emerging Markets Review, in press.
Bahmani-Oskooee, M., & Saha, S. (2016). Asymmetry cointegration between the value of the dollar and sectoral stock indices in the U.S. International Review of Economics & Finance, 46, 78–86.
Bansal, V., & Marshall, J. (2015). A tracking error approach to leveraged ETFs: Are they really that bad? Global Finance Journal, 26, 47–63.
Barnhart, S. W., & Rosenstein, S. (2010). Exchange-traded fund introductions and closed-end fund discounts and volume. Financial Review, 45(4), 973–994.
Ben-David, I., Franzoni, F., & Moussawi, R. (2014). Do ETFs increase volatility? (Working paper).
Bernard, C., & Ghossoub, M. (2010). Static portfolio choice under cumulative prospect theory. Mathematics and Financial Economics, 2, 77–306.
Bhattacharya, A., & O’Hara, M. (2016). Can ETFs increase market fragility? Effect of information linkages in ETF markets (Working paper). Cornell University.
Bhattacharya, D., & Sonaer, G. (2018). Herding by mutual funds: Impact on performance and investors’ response. The European Journal of Finance, 24(4), 283–299.
Bhuiyan, R., Rahman, M., Saiti, B., & Ghani, G. (2017). Financial integration between sukuk and bond indices of emerging markets: Insights from wavelet coherence and multivariate-GARCH analysis. Borsa Istanbul Review, in press.
Blake, D., Sarno, L., & Zinna, G. (2017). The market for lemmings: The herding behavior of pension funds. Journal of Financial Markets, 36(C), 17–39.
Bonanno, G., Caldarelli, G., et al. (2004). Networks of equities in financial markets. The European Physical Journal B-Condensed Matter & Complex Systems, 38(2), 363–371.
Bouri, E., Jain, A., et al. (2017). Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices. Resources Policy, 52, 201–206.
Broman, M. (2016). Liquidity, style investing and excess co-movement of exchange-traded fund returns. Journal of Financial Markets, 30, 27–53.
Caginalp, G., & DeSantis, M. (2017). Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs. Physica A: Statistical Mechanics and Its Applications, 467, 436–452.
Cao, D., & Yang, W. (2013). Sector indices correlation analysis in China’s stock market. Procedia Computer Science, 17, 1241–1249.
Cao, D., Long, W., & Yang, W. (2013). Sector indices correlation analysis in china’s stock market. Procedia Computer Science, 17, 1241–1249.
Cao, Y., Li, Y., et al. (2016). Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2351–2355.
Caraiani, P. (2012). Nonlinear dynamics in CEE stock markets indices. Economics Letters, 114(3), 329–331.
Castro, P., & Parsons, S. (2014). Modeling agent’s preferences based on prospect theory. Multidisciplinary Workshop on Advances in Preference Handling: Papers from the AAAI-14 Workshop.
Chacko, G., Das, S., & Fan, R. (2016). An index-based measure of liquidity. Journal of Banking & Finance, 68, 162–178.
Charles, A., Darné, O., & Kim, J. (2017). Adaptive markets hypothesis for Islamic stock indices: Evidence from Dow Jones size and sector-indices. International Economics, 151, 100–112.
Charteris, A., Chau, F., et al. (2014). Premiums, discounts and feedback trading: Evidence from emerging markets’ ETFs. International Review of Financial Analysis, 35, 80–89.
Charupat, N., & Miu, P. (2011). The pricing and performance of leveraged exchange-traded funds. Journal of Banking & Finance, 35(4), 966–977.
Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340–355.
Chen, D., & Li, T. (2014). Financial crises, Asian stock indices, and current accounts: An Asian-U.S. comparative study. Journal of Asian Economics, 34, 66–78.
Chen, C., Hsin, P., & Wu, C. (2010). Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model. Expert Systems with Applications, 37(12), 7557–7562.
Chen, N., Li, M., et al. (2017). Applications of linear ordinary differential equations and dynamic system to economics – An example of Taiwan stock index TAIEX. International Journal of Dynamical Systems and Differential Equations, 7(2), 95–111.
Cheng, P., & Liu, Y. (2009). The efficiency of the market for single-family homes: A critique on Case and Shiller (1989). Available at: www.​ssrn.​com
Cheng, C., Chen, T., & Wei, L. (2010). A hybrid model based in rough sets theory and genetic algorithms for stock price forecasting. Information Sciences, 180, 1610–1629.
Chiang, W., Enke, D., et al. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195–207.
Chiu, J., & Tsai, K. (2017). Government interventions and equity liquidity in the sub-prime crisis period: Evidence from the ETF market. International Review of Economics & Finance, 47, 128–142.
Christou, C., Cunado, J., Gupt, R., & Hassapis, C. (2017). Economic policy uncertainty and stock market returns in Pacific Rim countries: Evidence based on a Bayesian panel VAR model. Journal of Multinational Financial Management, 40, 92–102.
Correia, L., Reis, L., & Cascalho, J. (Eds.). (2014). Progress in artificial intelligence. Heidelberg: Springer.
Cotti, C., & Simon, D. (2018). The impact of stock market fluctuations on the mental and physical well-being of children. Economic Inquiry, 56(2), 1007.
Cottie, C., Dunn, R., & Tefft, N. (2015). The Dow is killing me: Risky health behaviors and the stock market. Health Economics, 24(7), 803.
Cremers, M., & Petajisto, A. (2009). How active is your fund manager? A new measure that predicts performance. Review of Financial Studies, 22(9), 3329–3365.
Curcio, R. J., Anderson, R. I., Guirguis, H., & Boney, V. (2012). Have leveraged and traditional ETFs impacted the volatility of real estate stock prices? Applied Financial Economics, 22(9), 709–722.
Curcio, R., Anderson, R., & Guirguis, H. (2014). Stock price volatility of banks and other financials emanating from the inception of leveraged, inverse, and traditional ETFs. The Journal of Index Investing, 5(1), 12–31.
Da, Z., & Shive, S. (2016, March). Exchange traded funds and asset return correlations (Working paper). University of Notre Dame.
Dai, M., Hou, J., et al. (2016). Mixed multifractal analysis of China and US stock index series. Chaos, Solitons & Fractals, 87, 268–275.
Dannhauser, C. (2017). The impact of innovation: Evidence from corporate bond exchange-traded funds (ETFs). Journal of Financial Economics, 125(3), 537–560.
Deev, O., & Linnertová, D. (2014). The determinants of ETFs short selling activity. Procedia – Social and Behavioral Sciences, 109, 669–673.
Diamond, S., & Kuan, J. (2018). Are the stock markets “rigged”? An empirical analysis of regulatory change. International Review of Law and Economics, 55, 33–40.
Domshlak, C., Hullermeier, E., Kaci, S., & Prade, H. (2011). Preferences in AI: An overview. Artificial Intelligence, 17(7–8), 1037–1052.
Donders, P., Jara, M., & Wagner, R. (2017). How sensitive is corporate debt to swings in commodity prices? Journal of Financial Stability, in press.
Duarte, F., Tenreiro, J., et al. (2010). Dynamics of the Dow Jones and the NASDAQ stock indexes. Nonlinear Dynamics, 61(4), 691–705.
Fernandez, V. (2014). Linear and non-linear causality between price indices and commodity prices. Resources Policy, 41, 40–51.
Ferreira, P., Dionísio, A., et al. (2018). Non-linear dependencies in African stock markets: Was subprime crisis an important factor? Physica A: Statistical Mechanics and Its Applications, 505, 680–687.
Feuerriegel, S., & Gordon, J. (2018). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, 112, 88–97.
Financial Stability Board. (2011). Potential financial stability issues arising from recent trends in Exchange-Traded Funds (ETFs). Financial Stability Board Note, Financial Stability Board.
Fink, M. (2011). The rise of mutual funds: An insider’s view (2nd ed.). Oxford: Oxford University Press.
Frino, A., Gallagher, D., & Oetomo, T. (2005). The index tracking strategies of passive and enhanced index equity funds. Australian Journal of Management, 30, 23–55.
Gadzinski, G., Schuller, M., & Vacchino, A. (2018). The Global Capital Stock: Finding a Proxy for the Unobservable Global Market Portfolio. Journal of Portfolio Management, 44(7), 12–23.
Gajardo, G., & Kristjanpoller, W. (2017). Asymmetric multifractal cross-correlations and time varying features between Latin-American stock market indices and crude oil market. Chaos, Solitons & Fractals, 104, 121–128.
Gallagher, D. R., Harman, G., et al. (2016). Global equity fund performance: An attribution approach. Financial Analysts Journal, 73(1), 56–71.
Gao, H., Li, J., et al. (2018). The synchronicity between the stock and the stock index via information in market. Physica A: Statistical Mechanics and Its Applications, 492, 1382–1388.
Gelos, G. (2013). International mutual funds, capital flow volatility, and contagion – A Survey (IMF working paper 11/92). Washington, DC: International Monetary Fund.
Gil-Alana, L., Cunado, J., & Gracia, F. (2013). Salient features of dependence in daily US stock market indices. Physica A: Statistical Mechanics and Its Applications, 392(15), 3198–3212.
Gleason, K., Mathur, I., & Peterson, M. (2004). Analysis of intraday herding behavior among the sector ETFs. Journal of Empirical Finance, 11, 681–694.
Goltz, F., Martellini, L., & Vaissié, M. (2007). Hedge fund indices: Reconciling investability and representativity. European Financial Management, 13(2), 257–286.
Gong, C., Ji, S., et al. (2016). The lead–lag relationship between stock index and stock index futures: A thermal optimal path method. Physica A: Statistical Mechanics and Its Applications, 444, 63–72.
Goswami, B., Ambika, G., Marwan, N., & Kurths, J. (2012). On interrelations of recurrences and connectivity trends between stock indices. Physica A: Statistical Mechanics and Its Applications, 391(18), 4364–4376.
Grishina, N., Lucas, C., & Date, P. (2017). Prospect theory-based portfolio optimization: An empirical study and analysis using intelligent algorithms. Quantitative Finance, 17(3), 353–367.
Gündüz, G., & Gündüz, Y. (2010). Viscoelastic behavior of stock indices. Physica A: Statistical Mechanics and Its Applications, 389(24), 5776–5784.
Guojonsdottir, G., Kristjansson, M., & Olafsson, O. (2011). Immediate surge in female visits to the cardiac emergency department following the economic collapse in Iceland: An observational study. Emergency Medicine Journal, 29(9), 694.
Haizhen, Y., & Suxiao, L. (2017). Dynamic interactions between real exchange rate and international fund flows in China. African Journal of Business Management, 11(5), 94–101.
Halkos, G., & Papadamou, S. (2006). An investigation of bond term premia in international government bond indices. Research in International Business and Finance, 20(1), 45–61.
Haluszczynski, A., Laut, I., et al. (2017). Linear and nonlinear market correlations: Characterizing financial crises and portfolio optimization. Physics Review E, 96, 062315.
Harré, M., & Bossomaier, T. (2009). Phase-transition–like behaviour of information measures in financial markets. Europhysics Letters, 87(1), 18009.
Hilliard, J. (2014). Premiums and discounts in ETFs: An analysis of the arbitrage mechanism in domestic and international funds. Global Finance Journal, 25(2), 90–107.
Ho, L., & Huang, C. (2015). The nonlinear relationships between stock indexes and exchange rates. Japan and the World Economy, 33, 20–27.
Hongfei, T., & Xu, X. E. (2013). On the tracking performance and return deviation of real estate leveraged ETFs. The Journal of Alternative Investments, 15(4), 48–73.
Hsieh, M., et al. (2011). Evidence of herding and positive feedback trading for mutual funds in emerging Asian countries. Quantitative Finance, 11(3), 423–435.
Hu, H., et al. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188–195.
Hurlin, C., Iseli, G., Pérignon, C., & Yeung, S. (2014). The counterparty risk exposure of ETF investors (Working paper).
International Monetary Fund. (2015, April). The asset management industry and financial stability (Working paper). Washington, DC: IMF.
Israeli, D., Lee, C., & Sridharan, S. A. (2016). Is there a dark side to exchange traded funds (ETFs)? An information perspective (Working paper). Stanford University.
Jacob, R., Koschutzki, D., et al. (2013). Algorithms for centrality indices. In Network analysis (Lecture notes in computer science, Vol. 3418, pp. 62–82).
Jiang, Z., Xie, W., Xiong, X., et al. (2013). Trading networks, abnormal motifs and stock manipulation. Quantitative Finance Letters, 1(1), 1–8.
Jiang, L., Phillips, B., & Yu, J. (2015). New methodology for constructing real estate price indices applied to the Singapore residential market. Journal of Banking & Finance, 61, S121–S131.
Jouini, J. (2013). Stock markets in GCC countries and global factors: A further investigation. Economic Modelling, 31, 80–86.
Kaiser, L., Fleisch, M., & Salcher, L. (2018). Bias and misrepresentation revisited: Perspective on major equity indices. Finance Research Letters, in press.
Kearney, F., Cummins, M., & Murphy, F. (2014). Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias. Journal of Financial Markets, 19, 86–109.
Kenett, D., et al. (2012). Dependency network and node influence: Application to the study of financial markets. International Journal of Bifurcation & Chaos, 22, 1250181.
Keylock, C. (2018). Gradual multifractal reconstruction of time-series: Formulation of the method and an application to the coupling between stock market indices and their Hölder exponents. Physica D: Nonlinear Phenomena, 368, 1–9.
Khwaja, A., & Mian, A. (2005). Unchecked intermediaries: Price manipulation in an emerging stock market. Journal of Financial Economics, 78, 203–241.
Kleiner, K. (2015, June). Where Case-Shiller got it wrong: The effect of credit supply on price indices (Working paper). Indiana University.
Kopp, M., Stauder, A., et al. (2008). Work stress and mental health in a changing society. European Journal of Public Health, 18(3), 238–244.
Kosev, M., & Williams, T. (2011). Exchange-traded funds. Reserve Bank of Australia Bulletin, Reserve Bank of Australia.
Kostovetsky, L. (2003). Index mutual funds and exchange-traded funds. Journal of Portfolio Management, 29(4), 80–92.
Krause, T., Ehsani, S., & Lien, D. (2014). Exchange-traded funds, liquidity and volatility. Applied Financial Economics, 24(24), 1617–1630.
Kreiger, J., & Higgins, D. (2002). Housing and health: Time again for public health action. American Journal of Public Health, 92(5), 758–768.
Kristoufek, L. (2010). On spurious anti-persistence in the US stock indices. Chaos, Solitons & Fractals, 43(1–12), 68–78.
Kwon, O., & Yang, J. (2008). Information flow between stock indices. Europhysics Letters, 82(6), 68003.
Lahmiri, S. (2018). Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Applied Mathematics and Computation, 320, 444–451.
Lechman, E., & Marszk, A. (2015). ICT technologies and financial innovations: The case of exchange traded funds in Brazil, Japan, Mexico, South Korea and the United States. Technological Forecasting and Social Change, 99, 355–376.
Ledgerwood, S., & Carpenter, P. (2012). A framework for the analysis of market manipulation. Review of Law & Economics, 8(1), 253–295.
Lee, L., Liu, A., & Chen, W. (2006). Pattern discovery of fuzzy time-series for financial prediction. IEEE Transactions on Knowledge and Data Engineering, 18(5), 613–625.
Lee, E., Eom, K., & Park, K. (2013). Microstructure-based manipulation: Strategic behavior and performance of spoofing traders. Journal of Financial Markets, 16(2), 227–252.
Lee, M., Song, J., et al. (2017). Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA. Chaos, Solitons & Fractals, 97, 28–38.
Lemke, T., Lins, G., & Smith, T. (2016). Regulation of investment companies. Matthew Bender. ISBN 978-0-8205-2005-6.
Levell, P. (2015). Is the Carli index flawed? Assessing the case for the new retail price index RPIJ. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178, 303–336.
Li, X., & Peng, L. (2017). US economic policy uncertainty and co-movements between Chinese and US stock markets. Economic Modelling, 61, 27–39.
Li, X., Balcilar, M., Gupta, M., & Chang, T. (2016). The causal relationship between economic policy uncertainty and stock returns in China and India: Evidence from a bootstrap rolling-window approach. Emerging Markets Finance and Trade, 52(3), 674–689.
Li, S., de Haan, J., & Scholtens, B. (2018). Surges of international fund flows. Journal of International Money and Finance, 82, 97–119.
Lin, T. (2017). The new market manipulation. Emory Law Journal, 66, 1253–1263.
Lin, C. C., & Chiang, M. H. (2005). Volatility effect of ETFs on the constituents of the underlying Taiwan 50 index. Applied Financial Economics, 15, 1315–1322.
Lin, H., Zhang, Y., et al. (2013). Large daily stock variation is associated with cardiovascular mortality in two cities of Guangdong, China. PLoS One, 8(7), e68417.
Linnertova, D. (2015). Network structures of the US market with ETFs. Procedia Economics and Finance, 23, 899–904.
Lo, A. (2012). Adaptive markets and the new world order. Financial Analysts Journal, 68(2), 18–29.
Lo, A. (2016). What is an index? Journal of Portfolio Management, 42(2), 21–36.
Lobão, J., & Pereira, C. (2017). Psychological barriers in stock market indices: Evidence from four southern European countries. Cuadernos de Economía, 40(114), 268–278.
Ma, W., Chen, H., Jiang, L., et al. (2011). Stock volatility as a risk factor for coronary heart disease death. European Heart Journal, 32(8), 1006–1011.
Machado, J., Duarte, F., & Duarte, G. (2011). Analysis of stock market indices through multidimensional scaling. Communications in Nonlinear Science and Numerical Simulation, 16(12), 4610–4618.
Madhavan, A., Sobczyk, A., & Ang, A. (2018). What’s in your benchmark? A factor analysis of major market indexes. The Journal of Index Investing, 9(2), 66–79.
Malagrino, L., Roman, N., & Monteiro, A. (2018). Forecasting stock market index daily direction: A Bayesian network approach. Expert Systems with Applications, in press.
March-Dallas, S., Daigler, R., et al. (2018). Exchange traded funds: Leverage and liquidity. Applied Economics, 50(37), 4054–4073.
Marshall, B., Nguyen, N., & Visaltanachoti, N. (2013). ETF arbitrage: Intraday evidence. Journal of Banking & Finance, 37, 3486–3498.
Marszk, A., & Lechman, E. (2018). Tracing financial innovation diffusion and substitution trajectories. Recent evidence on exchange-traded funds in Japan and South Korea. Technological Forecasting and Social Change, in press.
Marwala, T. (2013). Economic modeling using artificial intelligence methods. Heidelberg: Springer.
Meziani, A. (2001). Along came a SPDR: How tax efficient are Standard & Poor’s depository receipts? A guide to exchange-traded funds (a joint special issue of The Journal of Portfolio Management and The Journal of Investing), Institutional Investor.
Meziani, A. (2005). Application of the Wash-Sale rules to exchange-traded funds. Practical Tax Strategies, 74, 272–280.
Meziani, S. (2016). Exchange-traded funds: Investment practices and tactical approaches. London: Palgrave Macmillan.
Meziani, A., & Yang, J. (2001). Fresh alternative to mutual funds offers tax benefit. Practical Tax Strategies, 67(2), 100–108.
Meziani, A., & Yang, J. (2011). Assessing the value of loss harvesting using ETFs: Is it always a beneficial tax strategy? International Journal of Applied Accounting and Finance, 2(1), 15–22.
Meziani, A., & Yang, J. (2012). Assessing the value of tax efficient rebalancing using ETFs: Is it always better than a tax deferred strategy? International Research Journal of Applied Finance, III(9), 55–65.
Murray, C., Vos, T., Lozano, R., et al. (2013). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380(9859), 2197–2223.
Nagayev, R., Disli, D., et al. (2016). On the dynamic links between commodities and Islamic equity. Energy Economics, 58, 125–140.
Nandi, A., et al. (2012). Economic conditions and suicide rates in New York City. American Journal of Epidemiology, 175(6), 527–535.
Naresh, G., Vasudevan, G., Mahalakshmi, S., & Thiyagarajan, S. (2017). Spillover effect of US dollar on the stock indices of BRICS. Research in International Business and Finance, in press, corrected proof.
Nettleton, S. (1998). Losing homes through mortgage possession: A ‘new’ public health issue. Critical Public Health, 8(1).
Ng, W., Liang, X., et al. (2014). LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model. Neurocomputing, 146, 104–112.
Nobi, A., Lee, S., et al. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482–2489.
Nwogugu, M. (2005a). Towards multifactor models of decision making and risk: Critique of prospect theory and related approaches, part one. Journal of Risk Finance, 6(2), 150–162.
Nwogugu, M. (2005b). Towards multifactor models of decision making and risk: Critique of prospect theory and related approaches, part two. Journal of Risk Finance, 6(2), 163–173.
Nwogugu, M. (2006a). A further critique of cumulative prospect theory and related approaches. Applied Mathematics and Computation, 179, 451–465.
Nwogugu, M. (2006b). Regret minimization, willingness-to-accept-losses and framing. Applied Mathematics and Computation, 179(2), 440–450.
Nwogugu, M. (2012). Risk in the global real estate markets. Wiley.
Nwogugu, M. (2013). Decision-making, sub-additive recursive “matching” noise and biases in risk-weighted index calculation methods in in-complete markets with partially observable multi-attribute preferences. Discrete Mathematics, Algorithms and Applications, 5, 1350020. https://​doi.​org/​10.​1142/​S179383091350020​1
Nwogugu, M. (2015). The “popular-index ecosystem”: Managerial psychology, corporate governance and risk (Working paper).
Nwogugu, M. (2017a). Some biases and evolutionary homomorphisms implicit in the calculation of returns. In M. Nwogugu, Anomalies in net present value, returns and polynomials, and regret theory in decision making (Chapter 8). London: Palgrave Macmillan.
Nwogugu, M. (2017b). Spatio-temporal framing anomalies in the NPV-MIRR-IRR model and related approaches; and regret theory. In M. Nwogugu, Anomalies in net present value, returns and polynomials, and regret theory in decision making (Chapter 2). London: Palgrave Macmillan.
Nwogugu, M. (2017c). The historical and current concepts of “plain” interest rates, forward rates and discount rates can be misleading. In M. Nwogugu, Anomalies in net present value, returns and polynomials, and regret theory in decision making (Chapter 6). London: Palgrave Macmillan.
Nwogugu, M. (2017d). Regret theory and asset pricing anomalies in incomplete markets with dynamic un-aggregated preferences. In M. Nwogugu, Anomalies in net present value, returns and polynomials, and regret theory in decision making (Chapter 3). London: Palgrave Macmillan.
OECD. (2018). Survey of investment regulation of pension funds. Paris: OECD.
Oliveira, O., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125–144.
Ozer, G., & Ertokatli, C. (2010). Chaotic processes of common stock index returns: An empirical examination on Istanbul Stock Exchange (ISE) market. African Journal of Business Management, 4(6), 1140–1148.
Oztekin, A., Kizilaslan, R., et al. (2016). A data analytic approach to forecasting daily stock returns in an emerging market. European Journal of Operational Research, 253(3), 697–710.
Phillippas, N., Economou, F., Babalos, V., & Kostakis, A. (2013). Herding behavior in REITs: Novel tests and the role of financial crisis. International Review of Financial Analysis, 26, 166–174.
Pozen, R., & Hamacher, T. (2015). The fund industry: How your money is managed (2nd ed.). Hoboken: Wiley Finance.
Prasanna, P., & Menon, A. (2013). Speed of information adjustment in Indian stock indices. IIMB Management Review, 25(3), 150–159.
Preis, T., Schneider, J., & Stanley, H. (2011). Switching processes in financial markets. Proceedings of the National Academy of Sciences (USA), 108(19), 7674–7678.
Puy, D. (2016). Mutual funds flows and the geography of contagion. Journal of International Money and Finance, 60, 73–93.
Raddatz, C., & Schmukler, S. (2013). Deconstructing herding: Evidence from pension fund investment behavior. Journal of Financial Services Research, 43(1), 99–126.
Ratcliffe, A., & Taylor, K. (2015). Who cares about stock market booms and busts? Evidence from data on mental health. Oxford Economic Papers, 67(3), 826–845.
Reigneron, P. A., Allez, R., & Bouchaud, J. P. (2011). Principal regression analysis and the index leverage effect. Physica A, 390, 3026–3035.
Rekik, Y., Hachicha, W., & Boujelbene, Y. (2014). Agent-based modeling and investors’ behavior explanation of asset price dynamics on artificial financial markets. Procedia Economics and Finance, 13, 30–46.
Rizvi, S., & Arshad, S. (2017). Understanding time-varying systematic risks in Islamic and conventional sectoral indices. Economic Modelling, in press.
Rompotis, G. G. (2008). Interfamily competition on index tracking: The case of the Vanguard ETFs and Index funds. ETFs & Indexing, 1, 111–123.
Rompotis, G. G. (2011). ETFs vs. mutual funds: Evidence from the Greek market. South-Eastern Europe Journal of Economics, 9(1), 27–43.
Rompotis, G. (2013). ETFs vs. Index funds in the Greek market before and during the crisis. The Journal of Index Investing, 4(3), 42–49.
Roy, R., & Sarkar, U. (2011). Identifying influential stock indices from global stock markets: A social network analysis approach. Procedia Computer Science, 5, 442–449.
Sandoval, L. (2014). To lag or not to lag? How to compare indices of stock markets that operate on different times. Physica A: Statistical Mechanics and Its Applications, 403, 227–243.
Schellhorn, H. (2011). A trading mechanism contingent on several indices. European Journal of Operational Research, 213(3), 551–558.
Shahzad, S., Hernandez, J., et al. (2018). A global network topology of stock markets: Transmitters and receivers of spillover effects. Physica A: Statistical Mechanics and Its Applications, 492, 2136–2153.
Shapira, Y., Kenett, D., & Ben-Jacob, E. (2009). Index cohesive effect on the market. European Physical Journal B, 72(4), 657–669.
Sharifzadeh, M., & Hojat, S. (2012). An analytical performance comparison of exchange-traded funds with index funds: 2002–2010. Journal of Asset Management, 13, 196–209.
Shen, W., Guo, X., et al. (2011). Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowledge-Based Systems, 24(3), 378–385.
Shi, W., Shang, P., et al. (2016). The coupling analysis between stock market indices based on permutation measures. Physica A: Statistical Mechanics and Its Applications, 447, 222–231.
Sichert, T., & Meyer-Cirkel, A. (2016). Calculating the global market portfolio (Working paper-SAALT).
Singh, J., Ahmad, W., & Mishra, A. (2018). Coherence, connectedness and dynamic hedging effectiveness between emerging markets equities and commodity index funds. Resources Policy, in press.
Sornette, D. (2003). A complex system view of why stock markets crash. Princeton: Princeton University Press.
Steel, M., Mimoto, A., & Mooers, A. (2007). Hedging our bets: The expected contribution of species to future phylogenetic diversity. Evolutionary Bioinformatics, 3, 237–244.
Stošić, D., Stošić, D., et al. (2015). Multifractal properties of price change and volume change of stock market indices. Physica A: Statistical Mechanics and Its Applications, 428, 46–51.
Tang, H., & Xu, X. E. (2013). Solving the return deviation conundrum of leveraged exchange traded Funds. Journal of Financial and Quantitative Analysis, 48(1), 309–342.
Tseng, T., Lee, C., & Chen, M. (2015). Volatility forecast of country ETF: The sequential information arrival hypothesis. Economic Modelling, 47, 228–234.
Tsionas, M., & Michaelides, P. (2017). Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system. Physica A: Statistical Mechanics and Its Applications, 482, 95–107.
Vortelinos, D., Gkillas, K., et al. (2018). Asymmetric and nonlinear inter-relations of US stock indices. International Journal of Managerial Finance, 14(1), 78–129.
Wagalath, L. (2014). Modelling the rebalancing slippage of leveraged exchange-traded funds. Quantitative Finance, 14(9), 1503–1511.
Wang, J., Wang, J., et al. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346–14355.
Wang, J., Wang, J., et al. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766.
Wang, H., Shang, P., & Xia, J. (2016). Compositional segmentation and complexity measurement in stock indices. Physica A: Statistical Mechanics and Its Applications, 442, 67–73.
Wermers, R. (1999). Mutual fund herding and the impact on stock prices. Journal of Finance, 54, 581–622.
White, A. (2007). Biases in consumer price indexes. International Statistical Review, 67(3).
Wisniewski, T. (2016). Is there a link between politics and stock returns? A literature survey. International Review of Financial Analysis, 47, 15–23.
Wu, J., Deng, Y., & Liu, H. (2014). House price index construction in the nascent housing market: the case of China. The Journal of Real Estate Finance & Economics, 48, 522–545.
Xiao, J. (2015). Domestic and foreign mutual funds in Mexico: Do they behave differently? IMF Working Papers, 15(104), 1.
Xu, L., & Yin, X. (2017). Does ETF trading affect the efficiency of the underlying index? International Review of Financial Analysis, 51, 82–101.
Yang, J., & Meziani, A. (2005). Use exchange traded fund to harvest tax loss. Practical Tax Strategies, 74, 272–280.
Yang, J., & Meziani, A. (2012). Break-even point between short-term and long-term capital gain (loss) investment strategies. Journal of Investing, 21(4), 115–126.
Yang, J., Cabrera, J., & Wang, T. (2010). Nonlinearity, data-snooping, and stock index ETF return predictability. European Journal of Operational Research, 200(2), 498–507.
Yarovaya, L., Brzeszczyński, J., & Lau, M. (2016). Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96–114.
Yu, H., & Huarng, K. (2008). A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications, 34, 2945–2952.
Yu, T., & Huarng, K. (2010). A neural network-based fuzzy time series model to improve forecasting. Expert Systems with Applications, 37(4), 3366–3372.
Zheng, D., Li, H., & Zhu, X. (2015). Herding behavior in institutional investors: Evidence from China’s stock market. Journal of Multinational Financial Management, 32–33, 59–76.
Zhou, Y., & Chen, S. (2016). Cross-correlation analysis between Chinese TF contracts and treasury ETF based on high-frequency data. Physica A: Statistical Mechanics and Its Applications, 443, 117–127.
Metadata
Title
Introduction
Author
Michael I. C. Nwogugu
Copyright Year
2018
Book
Indices, Index Funds And ETFs

Print ISBN: 978-1-137-44700-5

Electronic ISBN: 978-1-137-44701-2

Copyright Year: 2018

DOI
https://doi.org/10.1057/978-1-137-44701-2_1