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Idiosyncrasies and challenges of data driven learning in electronic trading

Vangelis Bacoyannis, Vacslav Glukhov, Tom Jin, Jonathan Kochems and Doo Re Song

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Abstract: We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.

Date: 2018-11, Revised 2018-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mst
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Handle: RePEc:arx:papers:1811.09549