A model for unpacking big data analytics in high-frequency trading

Abstract : This study develops a conceptual model of the 7 V′s of big data analytics to gain a deeper understanding of the strategies and practices of high-frequency trading (HFT) in financial markets. HFT is computerized trading using proprietary algorithms. Empirical data collected from HFT firms and regulators in the US and UK reveals competitive asymmetries between HFTs and low-frequency traders (LFTs) operating more traditional forms of market trading. These findings show that HFT gains extensive market advantages over LFT due to significant investment in advanced technological architecture. Regulators are challenged to keep pace with HFT as different priorities to the 7 V′s are given in pursuit of a short term market strategy. This research has implications for regulators, financial practitioners and investors as the technological arms race is fundamentally changing the nature of global financial markets.
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Journal of Business Research, Elsevier, 2017, 70, pp.300 - 307. 〈10.1016/j.jbusres.2016.08.003〉
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Soumis le : lundi 28 novembre 2016 - 15:40:38
Dernière modification le : mardi 29 novembre 2016 - 01:04:50

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Jonathan J.J.M. Seddon, Wendy L. Currie. A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, Elsevier, 2017, 70, pp.300 - 307. 〈10.1016/j.jbusres.2016.08.003〉. 〈hal-01404316〉

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