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2016 | OriginalPaper | Chapter

Price Direction Prediction on High Frequency Data Using Deep Belief Networks

Authors : Jaime Humberto Niño-Peña, Germán Jairo Hernández-Pérez

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

This paper presents the use of Deep Belief Networks (DBN) for direction forecasting on financial time series, particularly those associated to the High Frequency Domain. The paper introduces some of the key concepts of the DBN, presents the methodology, results and its discussion. DBNs achieves better performance for particular configurations and training times were acceptable, however if they want to be pursued in real applications, windows sizes should be evaluated.

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Footnotes
1
It means five sell intentions and five buy intentions, recorded every five seconds. If something changed in the LOB within the 5 s interval it is not registered.
 
2
Candlestick is an ancient representation from rice Japanese traders in the 1700’s. Widely used by technical traders to analyze price formations (Luca, 2000).
 
3
Traded quantity of Facebook stock for each 15-seconds interval.
 
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Metadata
Title
Price Direction Prediction on High Frequency Data Using Deep Belief Networks
Authors
Jaime Humberto Niño-Peña
Germán Jairo Hernández-Pérez
Copyright Year
2016
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-319-50879-5

Electronic ISBN: 978-3-319-50880-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50880-1_7

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