Tensor representation in high-frequency financial data for price change prediction
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | IEEE Symposium Series on Computational Intelligence (SSCI), 2017 |
| Subtitle of host publication | Nov. 27-Dec. 1, 2017, Hawaii, USA |
| Publisher | IEEE |
| Number of pages | 7 |
| ISBN (Electronic) | 978-1-5386-2726-6 |
| DOIs | |
| Publication status | Published - 2017 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE Symposium Series on Computational Intelligence - Duration: 1 Jan 1900 → … |
Conference
| Conference | IEEE Symposium Series on Computational Intelligence |
|---|---|
| Abbreviated title | IEEE SSCI |
| Period | 1/01/00 → … |
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.