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Tensor representation in high-frequency financial data for price change prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationIEEE Symposium Series on Computational Intelligence (SSCI), 2017
Subtitle of host publicationNov. 27-Dec. 1, 2017, Hawaii, USA
PublisherIEEE
Number of pages7
ISBN (Electronic)978-1-5386-2726-6
DOIs
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventIEEE Symposium Series on Computational Intelligence -
Duration: 1 Jan 1900 → …

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
Abbreviated titleIEEE SSCI
Period1/01/00 → …

Abstract

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.

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