Advertisement

Introduction

  • Michael I. C. Nwogugu
Chapter

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.

Keywords

Indices Index funds Exchange-traded funds Debt Equity Risk management Systemic risk Portfolio management Allocation 

Bibliography

  1. 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.Google Scholar
  2. 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.Google Scholar
  3. Aggarwal, R., & Wu, G. (2003). Stock market manipulation—Theory and evidence (Working paper). https://pdfs.semanticscholar.org/9205/c49ccb627c311e810180d67ea438a46fa7fa.pdf
  4. Ahmad, W., Mishra, A., & Daly, K. (2018). Financial connectedness of BRICS and global sovereign bond markets. Emerging Markets Review, in press.Google Scholar
  5. 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.Google Scholar
  6. Aldridge, I. (2014). High-frequency runs and flash crash predictability. Journal of Portfolio Management, 40(3), 113–123.Google Scholar
  7. Aldridge, A. (2016). ETFs, high-frequency trading, and flash crashes. Journal of Portfolio Management, 43(1), 17–28.Google Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. Amenc, N., Goltz, F., & Le Sourd, V. (2009). The performance of characteristics-based indices. European Financial Management, 15(2), 241–278.Google Scholar
  11. Anbalagan, T., & Maheswari, U. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214–221.Google Scholar
  12. Angel, J., Broms, T., & Gastineau, G. (2016). ETF transaction costs are often higher than investors realize. Journal of Portfolio Management, 42(3), 65–75.Google Scholar
  13. Arnoldi, J. (2016). Computer algorithms, market manipulation and the institutionalization of high frequency trading. Theory, Culture & Society, 33(1), 29–52.Google Scholar
  14. 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.Google Scholar
  15. Avellaneda, M., & Zhang, S. (2010). Path-dependence of leveraged ETF returns. SIAM Journal of Financial Mathematics, 1, 586–603.Google Scholar
  16. Badshah, I., Bekiros, S., et al. (2018). Asymmetric linkages among the fear index and emerging market volatility indices. Emerging Markets Review, in press.Google Scholar
  17. 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.Google Scholar
  18. Bansal, V., & Marshall, J. (2015). A tracking error approach to leveraged ETFs: Are they really that bad? Global Finance Journal, 26, 47–63.Google Scholar
  19. Barnhart, S. W., & Rosenstein, S. (2010). Exchange-traded fund introductions and closed-end fund discounts and volume. Financial Review, 45(4), 973–994.Google Scholar
  20. Ben-David, I., Franzoni, F., & Moussawi, R. (2014). Do ETFs increase volatility? (Working paper).Google Scholar
  21. Bennett, G., Scharoun-Lee, M., & Tucker-Seeley, R. (2009). Will the public’s health fall victim to the home foreclosure epidemic? PLoS Medicine, 6(6), e1000087.  https://doi.org/10.1371/journal.pmed.1000087
  22. Bernard, C., & Ghossoub, M. (2010). Static portfolio choice under cumulative prospect theory. Mathematics and Financial Economics, 2, 77–306.Google Scholar
  23. Bhattacharya, A., & O’Hara, M. (2016). Can ETFs increase market fragility? Effect of information linkages in ETF markets (Working paper). Cornell University.Google Scholar
  24. Bhattacharya, D., & Sonaer, G. (2018). Herding by mutual funds: Impact on performance and investors’ response. The European Journal of Finance, 24(4), 283–299.Google Scholar
  25. 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.Google Scholar
  26. 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.Google Scholar
  27. Blocher, J., & Whaley, R. (2016). Two-sided markets in asset management: Exchange-traded funds and securities lending. https://westernfinance-portal.org/viewp.php?n=450128
  28. 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.Google Scholar
  29. 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.Google Scholar
  30. Broman, M. (2016). Liquidity, style investing and excess co-movement of exchange-traded fund returns. Journal of Financial Markets, 30, 27–53.Google Scholar
  31. 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.Google Scholar
  32. Cao, D., & Yang, W. (2013). Sector indices correlation analysis in China’s stock market. Procedia Computer Science, 17, 1241–1249.Google Scholar
  33. Cao, D., Long, W., & Yang, W. (2013). Sector indices correlation analysis in china’s stock market. Procedia Computer Science, 17, 1241–1249.Google Scholar
  34. 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.Google Scholar
  35. Caraiani, P. (2012). Nonlinear dynamics in CEE stock markets indices. Economics Letters, 114(3), 329–331.Google Scholar
  36. 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.Google Scholar
  37. Chacko, G., Das, S., & Fan, R. (2016). An index-based measure of liquidity. Journal of Banking & Finance, 68, 162–178.Google Scholar
  38. 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.Google Scholar
  39. 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.Google Scholar
  40. Charupat, N., & Miu, P. (2011). The pricing and performance of leveraged exchange-traded funds. Journal of Banking & Finance, 35(4), 966–977.Google Scholar
  41. 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.Google Scholar
  42. 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.Google Scholar
  43. 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.Google Scholar
  44. 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.Google Scholar
  45. 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
  46. 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.Google Scholar
  47. Chiang, W., Enke, D., et al. (2016). An adaptive stock index trading decision support system. Expert Systems with Applications, 59, 195–207.Google Scholar
  48. 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.Google Scholar
  49. Christie, W., & Schultz, P. (1994). Why do NASDAQ market makers avoid odd-eighth quotes. Journal of Finance, 49(5), 1813–1833. http://regulation.fidessa.com/wp-content/uploads/2012/05/Christie-Schultz-1994.pdf
  50. 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.Google Scholar
  51. Correia, L., Reis, L., & Cascalho, J. (Eds.). (2014). Progress in artificial intelligence. Heidelberg: Springer.Google Scholar
  52. 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.Google Scholar
  53. Cottie, C., Dunn, R., & Tefft, N. (2015). The Dow is killing me: Risky health behaviors and the stock market. Health Economics, 24(7), 803.Google Scholar
  54. 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.Google Scholar
  55. 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.Google Scholar
  56. 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.Google Scholar
  57. Da, Z., & Shive, S. (2016, March). Exchange traded funds and asset return correlations (Working paper). University of Notre Dame.Google Scholar
  58. Dai, M., Hou, J., et al. (2016). Mixed multifractal analysis of China and US stock index series. Chaos, Solitons & Fractals, 87, 268–275.Google Scholar
  59. Dannhauser, C. (2017). The impact of innovation: Evidence from corporate bond exchange-traded funds (ETFs). Journal of Financial Economics, 125(3), 537–560.Google Scholar
  60. Deev, O., & Linnertová, D. (2014). The determinants of ETFs short selling activity. Procedia – Social and Behavioral Sciences, 109, 669–673.Google Scholar
  61. 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.Google Scholar
  62. Dobi, D., & Avellaneda, M. (2012). Structural slippage of leveraged ETFs (Working paper). New York University. https://www.math.nyu.edu/faculty/avellane/LETF_Dobi_Avellaneda_Sept2012.pdf
  63. Domshlak, C., Hullermeier, E., Kaci, S., & Prade, H. (2011). Preferences in AI: An overview. Artificial Intelligence, 17(7–8), 1037–1052.Google Scholar
  64. Donders, P., Jara, M., & Wagner, R. (2017). How sensitive is corporate debt to swings in commodity prices? Journal of Financial Stability, in press.Google Scholar
  65. Duarte, F., Tenreiro, J., et al. (2010). Dynamics of the Dow Jones and the NASDAQ stock indexes. Nonlinear Dynamics, 61(4), 691–705.Google Scholar
  66. Fernandez, V. (2014). Linear and non-linear causality between price indices and commodity prices. Resources Policy, 41, 40–51.Google Scholar
  67. 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.Google Scholar
  68. Feuerriegel, S., & Gordon, J. (2018). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, 112, 88–97.Google Scholar
  69. Financial Stability Board. (2011). Potential financial stability issues arising from recent trends in Exchange-Traded Funds (ETFs). Financial Stability Board Note, Financial Stability Board.Google Scholar
  70. Fink, M. (2011). The rise of mutual funds: An insider’s view (2nd ed.). Oxford: Oxford University Press.Google Scholar
  71. 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.Google Scholar
  72. 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.Google Scholar
  73. 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.Google Scholar
  74. Gallagher, D. R., Harman, G., et al. (2016). Global equity fund performance: An attribution approach. Financial Analysts Journal, 73(1), 56–71.Google Scholar
  75. 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.Google Scholar
  76. Gelos, G. (2013). International mutual funds, capital flow volatility, and contagion – A Survey (IMF working paper 11/92). Washington, DC: International Monetary Fund.Google Scholar
  77. 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.Google Scholar
  78. Gleason, K., Mathur, I., & Peterson, M. (2004). Analysis of intraday herding behavior among the sector ETFs. Journal of Empirical Finance, 11, 681–694.Google Scholar
  79. Glosten, L., Nallareddy, S., & Zou, Y. (2015). ETF trading and informational efficiency of underlying securities (Working paper). Columbia University. www.rhsmith.umd.edu/files/Documents/Departments/Finance/fall2015/glosten.pdf
  80. Goltz, F., Martellini, L., & Vaissié, M. (2007). Hedge fund indices: Reconciling investability and representativity. European Financial Management, 13(2), 257–286.Google Scholar
  81. 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.Google Scholar
  82. 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.Google Scholar
  83. 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.Google Scholar
  84. Gündüz, G., & Gündüz, Y. (2010). Viscoelastic behavior of stock indices. Physica A: Statistical Mechanics and Its Applications, 389(24), 5776–5784.Google Scholar
  85. 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.Google Scholar
  86. 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.Google Scholar
  87. 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.Google Scholar
  88. Haluszczynski, A., Laut, I., et al. (2017). Linear and nonlinear market correlations: Characterizing financial crises and portfolio optimization. Physics Review E, 96, 062315.Google Scholar
  89. Harré, M., & Bossomaier, T. (2009). Phase-transition–like behaviour of information measures in financial markets. Europhysics Letters, 87(1), 18009.Google Scholar
  90. 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.Google Scholar
  91. Ho, L., & Huang, C. (2015). The nonlinear relationships between stock indexes and exchange rates. Japan and the World Economy, 33, 20–27.Google Scholar
  92. 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.Google Scholar
  93. 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.Google Scholar
  94. Hu, H., et al. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188–195.Google Scholar
  95. Hurlin, C., Iseli, G., Pérignon, C., & Yeung, S. (2014). The counterparty risk exposure of ETF investors (Working paper).Google Scholar
  96. International Monetary Fund. (2015, April). The asset management industry and financial stability (Working paper). Washington, DC: IMF.Google Scholar
  97. Investment Company Institute. (2016). 2016 investment company fact book. http://www.icifactbook.org/
  98. 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.Google Scholar
  99. Ivanov, I. T., & Lenkey, S. L. (2014). Are concerns about leveraged ETFs overblown? (Working paper). Federal Reserve Board, Washington, DC. www.federalreserve.gov/econresdata/feds/2014/files/2014106pap.pdf
  100. Jacob, R., Koschutzki, D., et al. (2013). Algorithms for centrality indices. In Network analysis (Lecture notes in computer science, Vol. 3418, pp. 62–82).Google Scholar
  101. Jiang, Z., Xie, W., Xiong, X., et al. (2013). Trading networks, abnormal motifs and stock manipulation. Quantitative Finance Letters, 1(1), 1–8.Google Scholar
  102. 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.Google Scholar
  103. Jouini, J. (2013). Stock markets in GCC countries and global factors: A further investigation. Economic Modelling, 31, 80–86.Google Scholar
  104. Kaiser, L., Fleisch, M., & Salcher, L. (2018). Bias and misrepresentation revisited: Perspective on major equity indices. Finance Research Letters, in press.Google Scholar
  105. 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.Google Scholar
  106. Kenett, D., et al. (2012). Dependency network and node influence: Application to the study of financial markets. International Journal of Bifurcation & Chaos, 22, 1250181.Google Scholar
  107. 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.Google Scholar
  108. Khwaja, A., & Mian, A. (2005). Unchecked intermediaries: Price manipulation in an emerging stock market. Journal of Financial Economics, 78, 203–241.Google Scholar
  109. Kleiner, K. (2015, June). Where Case-Shiller got it wrong: The effect of credit supply on price indices (Working paper). Indiana University.Google Scholar
  110. 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.Google Scholar
  111. Kosev, M., & Williams, T. (2011). Exchange-traded funds. Reserve Bank of Australia Bulletin, Reserve Bank of Australia.Google Scholar
  112. Kostovetsky, L. (2003). Index mutual funds and exchange-traded funds. Journal of Portfolio Management, 29(4), 80–92.Google Scholar
  113. Krause, T., Ehsani, S., & Lien, D. (2014). Exchange-traded funds, liquidity and volatility. Applied Financial Economics, 24(24), 1617–1630.Google Scholar
  114. Kreiger, J., & Higgins, D. (2002). Housing and health: Time again for public health action. American Journal of Public Health, 92(5), 758–768.Google Scholar
  115. Kristoufek, L. (2010). On spurious anti-persistence in the US stock indices. Chaos, Solitons & Fractals, 43(1–12), 68–78.Google Scholar
  116. Kwon, O., & Yang, J. (2008). Information flow between stock indices. Europhysics Letters, 82(6), 68003.Google Scholar
  117. Lahmiri, S. (2018). Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Applied Mathematics and Computation, 320, 444–451.Google Scholar
  118. 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.Google Scholar
  119. Ledgerwood, S., & Carpenter, P. (2012). A framework for the analysis of market manipulation. Review of Law & Economics, 8(1), 253–295.Google Scholar
  120. 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.Google Scholar
  121. 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.Google Scholar
  122. 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.Google Scholar
  123. Lemke, T., Lins, G., & Smith, T. (2016). Regulation of investment companies. Matthew Bender. ISBN 978-0-8205-2005-6.Google Scholar
  124. 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.Google Scholar
  125. Li, X., & Peng, L. (2017). US economic policy uncertainty and co-movements between Chinese and US stock markets. Economic Modelling, 61, 27–39.Google Scholar
  126. 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.Google Scholar
  127. Li, S., de Haan, J., & Scholtens, B. (2018). Surges of international fund flows. Journal of International Money and Finance, 82, 97–119.Google Scholar
  128. Lin, T. (2017). The new market manipulation. Emory Law Journal, 66, 1253–1263.Google Scholar
  129. 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.Google Scholar
  130. 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.Google Scholar
  131. Linnertova, D. (2015). Network structures of the US market with ETFs. Procedia Economics and Finance, 23, 899–904.Google Scholar
  132. Liu, C. (2015). How does the stock market affect investor sentiment? – Evidence from antidepressant usage. Available at SSRN: https://ssrn.com/abstract=2691824 or  https://doi.org/10.2139/ssrn.2691824
  133. Lo, A. (2012). Adaptive markets and the new world order. Financial Analysts Journal, 68(2), 18–29.Google Scholar
  134. Lo, A. (2016). What is an index? Journal of Portfolio Management, 42(2), 21–36.Google Scholar
  135. Lobão, J., & Pereira, C. (2017). Psychological barriers in stock market indices: Evidence from four southern European countries. Cuadernos de Economía, 40(114), 268–278.Google Scholar
  136. 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.Google Scholar
  137. 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.Google Scholar
  138. 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.Google Scholar
  139. Malagrino, L., Roman, N., & Monteiro, A. (2018). Forecasting stock market index daily direction: A Bayesian network approach. Expert Systems with Applications, in press.Google Scholar
  140. March-Dallas, S., Daigler, R., et al. (2018). Exchange traded funds: Leverage and liquidity. Applied Economics, 50(37), 4054–4073.Google Scholar
  141. Marshall, B., Nguyen, N., & Visaltanachoti, N. (2013). ETF arbitrage: Intraday evidence. Journal of Banking & Finance, 37, 3486–3498.Google Scholar
  142. 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.Google Scholar
  143. Martyn, I., Kuhn, T., et al. (2012). Computing evolutionary distinctiveness indices in large scale analysis. Algorithms for Molecular Biology, 7(6).  https://doi.org/10.1186/1748-7188-7-6
  144. Marwala, T. (2013). Economic modeling using artificial intelligence methods. Heidelberg: Springer.Google Scholar
  145. 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.Google Scholar
  146. Meziani, A. (2005). Application of the Wash-Sale rules to exchange-traded funds. Practical Tax Strategies, 74, 272–280.Google Scholar
  147. Meziani, S. (2016). Exchange-traded funds: Investment practices and tactical approaches. London: Palgrave Macmillan.Google Scholar
  148. Meziani, A., & Yang, J. (2001). Fresh alternative to mutual funds offers tax benefit. Practical Tax Strategies, 67(2), 100–108.Google Scholar
  149. 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.Google Scholar
  150. 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.Google Scholar
  151. Moloughney, B. (2004). Housing and population health: The state of current research knowledge. Prepared for the Canadian Population Health Initiative, Part of the Canadian Institute for Health Information, Canada Mortgage and Housing Corporation. https://secure.cihi.ca/free_products/HousingPopHealth_e.pdf
  152. 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.Google Scholar
  153. Nadendla, S., Brahma, S., & Varshney, P. (2016). Towards the design of prospect-theory based human decision rules for hypothesis testing. https://arxiv.org/abs/1610.01085
  154. Nagayev, R., Disli, D., et al. (2016). On the dynamic links between commodities and Islamic equity. Energy Economics, 58, 125–140.Google Scholar
  155. Nandi, A., et al. (2012). Economic conditions and suicide rates in New York City. American Journal of Epidemiology, 175(6), 527–535.Google Scholar
  156. 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.Google Scholar
  157. Nettleton, S. (1998). Losing homes through mortgage possession: A ‘new’ public health issue. Critical Public Health, 8(1).Google Scholar
  158. 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.Google Scholar
  159. Nobi, A., Lee, S., et al. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482–2489.Google Scholar
  160. 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.Google Scholar
  161. 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.Google Scholar
  162. Nwogugu, M. (2006a). A further critique of cumulative prospect theory and related approaches. Applied Mathematics and Computation, 179, 451–465.Google Scholar
  163. Nwogugu, M. (2006b). Regret minimization, willingness-to-accept-losses and framing. Applied Mathematics and Computation, 179(2), 440–450.Google Scholar
  164. Nwogugu, M. (2012). Risk in the global real estate markets. Wiley.Google Scholar
  165. 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/S1793830913500201
  166. Nwogugu, M. (2015). The “popular-index ecosystem”: Managerial psychology, corporate governance and risk (Working paper).Google Scholar
  167. 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.Google Scholar
  168. 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.Google Scholar
  169. 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.Google Scholar
  170. 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.Google Scholar
  171. OECD. (2018). Survey of investment regulation of pension funds. Paris: OECD.Google Scholar
  172. 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.Google Scholar
  173. 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.Google Scholar
  174. 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.Google Scholar
  175. 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.Google Scholar
  176. Pictet Alternative Investments. (2011). Hedge fund indices: How representative are they? https://perspectives.pictet.com/wp-content/uploads/2011/01/Hedge-Fund-Indices-how-representative-are-they.pdf
  177. Pozen, R., & Hamacher, T. (2015). The fund industry: How your money is managed (2nd ed.). Hoboken: Wiley Finance.Google Scholar
  178. Prasanna, P., & Menon, A. (2013). Speed of information adjustment in Indian stock indices. IIMB Management Review, 25(3), 150–159.Google Scholar
  179. Preis, T., Schneider, J., & Stanley, H. (2011). Switching processes in financial markets. Proceedings of the National Academy of Sciences (USA), 108(19), 7674–7678.Google Scholar
  180. Puy, D. (2016). Mutual funds flows and the geography of contagion. Journal of International Money and Finance, 60, 73–93.Google Scholar
  181. Raddatz, C., & Schmukler, S. (2013). Deconstructing herding: Evidence from pension fund investment behavior. Journal of Financial Services Research, 43(1), 99–126.Google Scholar
  182. Ramaswamy, S. (2011). Market structures and systemic risks of exchange-traded funds (BIS working paper no. 343). www.bis.org/publ/work343.pdf
  183. 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.Google Scholar
  184. Reigneron, P. A., Allez, R., & Bouchaud, J. P. (2011). Principal regression analysis and the index leverage effect. Physica A, 390, 3026–3035.Google Scholar
  185. 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.Google Scholar
  186. Rizvi, S., & Arshad, S. (2017). Understanding time-varying systematic risks in Islamic and conventional sectoral indices. Economic Modelling, in press.Google Scholar
  187. Rompotis, G. G. (2008). Interfamily competition on index tracking: The case of the Vanguard ETFs and Index funds. ETFs & Indexing, 1, 111–123.Google Scholar
  188. Rompotis, G. G. (2011). ETFs vs. mutual funds: Evidence from the Greek market. South-Eastern Europe Journal of Economics, 9(1), 27–43.Google Scholar
  189. 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.Google Scholar
  190. Roy, R., & Sarkar, U. (2011). Identifying influential stock indices from global stock markets: A social network analysis approach. Procedia Computer Science, 5, 442–449.Google Scholar
  191. 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.Google Scholar
  192. Sasikumar, A., & Kamaiah, B. (2014). A complex dynamical analysis of the Indian stock market. Economics Research International, 2014, 807580.  https://doi.org/10.1155/2014/807580
  193. Schellhorn, H. (2011). A trading mechanism contingent on several indices. European Journal of Operational Research, 213(3), 551–558.Google Scholar
  194. 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.Google Scholar
  195. Shapira, Y., Kenett, D., & Ben-Jacob, E. (2009). Index cohesive effect on the market. European Physical Journal B, 72(4), 657–669.Google Scholar
  196. 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.Google Scholar
  197. 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.Google Scholar
  198. 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.Google Scholar
  199. Sichert, T., & Meyer-Cirkel, A. (2016). Calculating the global market portfolio (Working paper-SAALT).Google Scholar
  200. 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.Google Scholar
  201. Song, Y., Yao, H., et al. (2017). Risky multicriteria group decision making based on cloud prospect theory and regret feedback. Mathematical Problems in Engineering, 2017, 9646303.  https://doi.org/10.1155/2017/9646303
  202. Sornette, D. (2003). A complex system view of why stock markets crash. Princeton: Princeton University Press.Google Scholar
  203. Steel, M., Mimoto, A., & Mooers, A. (2007). Hedging our bets: The expected contribution of species to future phylogenetic diversity. Evolutionary Bioinformatics, 3, 237–244.Google Scholar
  204. 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.Google Scholar
  205. 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.Google Scholar
  206. The Economist. (1998, January). Collusion in the stock market – Now that its price-fixing scandal has been laid to rest, has NASDAQ become a more efficient equity market? https://www.economist.com/finance-and-economics/1998/01/15/collusion-in-the-stockmarket
  207. Tseng, T., Lee, C., & Chen, M. (2015). Volatility forecast of country ETF: The sequential information arrival hypothesis. Economic Modelling, 47, 228–234.Google Scholar
  208. 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.Google Scholar
  209. U.S. Senate. (2011, October 19). Market micro-structure: Examination of Exchange-Traded Funds (ETFs). US Senate Committee on Banking Hearing. Available at: http://www.banking.senate.gov/public/index.cfm?FuseAction=Hearings.Hearing&Hearing_ID=ad4fdfb9-d589-4ac9-8829-0edf1ad8dc8d
  210. 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.Google Scholar
  211. Wagalath, L. (2014). Modelling the rebalancing slippage of leveraged exchange-traded funds. Quantitative Finance, 14(9), 1503–1511.Google Scholar
  212. Wang, J., Wang, J., et al. (2011). Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38(11), 14346–14355.Google Scholar
  213. Wang, J., Wang, J., et al. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766.Google Scholar
  214. 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.Google Scholar
  215. Wermers, R. (1999). Mutual fund herding and the impact on stock prices. Journal of Finance, 54, 581–622.Google Scholar
  216. White, A. (2007). Biases in consumer price indexes. International Statistical Review, 67(3).Google Scholar
  217. Whiteford, H., Ferrari, A., et al. (2015). The global burden of mental, neurological and substance use disorders: An analysis from the Global Burden of Disease Study 2010. PLoS One, 10(2), e0116820. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4320057/
  218. Wisniewski, T. (2016). Is there a link between politics and stock returns? A literature survey. International Review of Financial Analysis, 47, 15–23.Google Scholar
  219. 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.Google Scholar
  220. Xiao, J. (2015). Domestic and foreign mutual funds in Mexico: Do they behave differently? IMF Working Papers, 15(104), 1.Google Scholar
  221. Xu, L., & Yin, X. (2017). Does ETF trading affect the efficiency of the underlying index? International Review of Financial Analysis, 51, 82–101.Google Scholar
  222. Yang, J., & Meziani, A. (2005). Use exchange traded fund to harvest tax loss. Practical Tax Strategies, 74, 272–280.Google Scholar
  223. 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.Google Scholar
  224. 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.Google Scholar
  225. 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.Google Scholar
  226. Yu, H., & Huarng, K. (2008). A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications, 34, 2945–2952.Google Scholar
  227. Yu, T., & Huarng, K. (2010). A neural network-based fuzzy time series model to improve forecasting. Expert Systems with Applications, 37(4), 3366–3372.Google Scholar
  228. 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.Google Scholar
  229. 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.Google Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Michael I. C. Nwogugu
    • 1
  1. 1.EnuguNigeria

Personalised recommendations