Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach

Asil Oztekin, Kyle Best, Dursun Delen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study is aimed at determining the future share net inflows and outflows by using the characteristics of Exchange Traded Funds (ETF) as variables in a data mining based analytic methodology. The relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. 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. An international selection of 222 ETFs from one of the top three ETF providers is used in this study, of which fifteen attributes from each fund are used because they are likely to be contributors to fund inflows and outflows. Cross-Industry Standard Process for Data Mining (CRISP-DM) is used in this study accompanied with machine learning tools to develop a neural network which will forecast a positive or negative flow of net assets for ETFs.

Original languageEnglish
Title of host publicationProceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014
PublisherIEEE Computer Society
Pages779-788
Number of pages10
ISBN (Print)9781479925049
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes
Event47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, HI, United States
Duration: 6 Jan 20149 Jan 2014

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference47th Hawaii International Conference on System Sciences, HICSS 2014
CountryUnited States
CityWaikoloa, HI
Period6/01/149/01/14

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Data mining
Learning systems
Neural networks
Industry

Cite this

Oztekin, A., Best, K., & Delen, D. (2014). Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014 (pp. 779-788). [6758700] (Proceedings of the Annual Hawaii International Conference on System Sciences). IEEE Computer Society. https://doi.org/10.1109/HICSS.2014.104
Oztekin, Asil ; Best, Kyle ; Delen, Dursun. / Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach. Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society, 2014. pp. 779-788 (Proceedings of the Annual Hawaii International Conference on System Sciences).
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Oztekin, A, Best, K & Delen, D 2014, Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach. in Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014., 6758700, Proceedings of the Annual Hawaii International Conference on System Sciences, IEEE Computer Society, pp. 779-788, 47th Hawaii International Conference on System Sciences, HICSS 2014, Waikoloa, HI, United States, 6/01/14. https://doi.org/10.1109/HICSS.2014.104

Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach. / Oztekin, Asil; Best, Kyle; Delen, Dursun.

Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society, 2014. p. 779-788 6758700 (Proceedings of the Annual Hawaii International Conference on System Sciences).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Oztekin A, Best K, Delen D. Analyzing the predictability of exchange traded funds characteristics in the mutual fund market on the flow of shares using a data mining approach. In Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014. IEEE Computer Society. 2014. p. 779-788. 6758700. (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10.1109/HICSS.2014.104