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Information fusion-based meta-classification predictive modeling for ETF performance

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  • Asil Oztekin

    (University of Massachusetts)

Abstract

This study is aimed at determining the future share net inflows and outflows of Exchange Traded Funds (ETFs). The relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. The net flows for Exchange Traded Funds are expected to be less related to overall fund performance, but rather based on the characteristics of the fund that make it attractive to an individual investor. In order to explore the relationship between investor’s perception of ETFs and subsequent net flows, this study is designed to shed light on the multifaceted linkages between fund characteristics and net flows. A meta-classification predictive modeling approach is designed for the use of large data sets. Then its implementation and results are discussed. A thorough selection of fifteen attributes from each fund, which are the most likely contributors to fund inflows and outflows, is deployed in the analyses. The large data set calls for the use of a robust systematic approach to identifying the attributes of the funds that best predict future inflows and outflows of the fund. The predictive performance of the proposed decision analytic methodology was assessed via the 10-fold cross validation, which yielded very promising results.

Suggested Citation

  • Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:2:d:10.1007_s10796-016-9704-4
    DOI: 10.1007/s10796-016-9704-4
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    1. Cici, Gjergji, 2012. "The Prevalence of the Disposition Effect in Mutual Funds’ Trades," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(4), pages 795-820, August.
    2. Alexander Kempf & Stefan Ruenzi, 2008. "Family Matters: Rankings Within Fund Families and Fund Inflows," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(1‐2), pages 177-199, January.
    3. Erik R. Sirri & Peter Tufano, 1998. "Costly Search and Mutual Fund Flows," Journal of Finance, American Finance Association, vol. 53(5), pages 1589-1622, October.
    4. Mark Grinblatt & Matti Keloharju, 2001. "What Makes Investors Trade?," Journal of Finance, American Finance Association, vol. 56(2), pages 589-616, April.
    5. Dejaeger, Karel & Goethals, Frank & Giangreco, Antonio & Mola, Lapo & Baesens, Bart, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," European Journal of Operational Research, Elsevier, vol. 218(2), pages 548-562.
    6. Jonathan B. Berk & Richard C. Green, 2004. "Mutual Fund Flows and Performance in Rational Markets," Journal of Political Economy, University of Chicago Press, vol. 112(6), pages 1269-1295, December.
    7. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    8. Heung-Joo Cha & Jaebeom Kim, 2010. "Stock returns and aggregate mutual fund flows: a system approach," Applied Financial Economics, Taylor & Francis Journals, vol. 20(19), pages 1493-1498.
    9. Susan E. K. Christoffersen & Richard Evans & David K. Musto, 2013. "What Do Consumers’ Fund Flows Maximize? Evidence from Their Brokers’ Incentives," Journal of Finance, American Finance Association, vol. 68(1), pages 201-235, February.
    10. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, June.
    11. George D. Cashman & Daniel N. Deli & Federico Nardari & Sriram Villupuram, 2012. "Investors Do Respond to Poor Mutual Fund Performance: Evidence from Inflows and Outflows," The Financial Review, Eastern Finance Association, vol. 47(4), pages 719-739, November.
    12. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    13. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    14. Rakowski, David, 2010. "Fund Flow Volatility and Performance," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(1), pages 223-237, February.
    15. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    16. Ruiz, Edilberto & Nieto, Fabio H., 2000. "A note on linear combination of predictors," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 351-356, May.
    17. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    18. Michael J. Cooper & Huseyin Gulen & P. Raghavendra Rau, 2005. "Changing Names with Style: Mutual Fund Name Changes and Their Effects on Fund Flows," Journal of Finance, American Finance Association, vol. 60(6), pages 2825-2858, December.
    19. Alexander Kempf & Stefan Ruenzi, 2008. "Family Matters: Rankings Within Fund Families and Fund Inflows," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(1‐2), pages 177-199, January.
    20. Brad M. Barber & Terrance Odean & Lu Zheng, 2005. "Out of Sight, Out of Mind: The Effects of Expenses on Mutual Fund Flows," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2095-2120, November.
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