Mann, AD;
Gorse, D;
(2017)
A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data.
In: Lintas, A and Rovetta, S and Verschure, PFMJ and Villa, AEP, (eds.)
Artificial Neural Networks and Machine Learning – ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part II.
(pp. pp. 495-502).
Springer: Cham, Switzerland.
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Abstract
Prediction of financial markets using neural networks and other techniques has predominately focused on the close price. Here, in contrast, the concept of a mid-price based on an Open, High, Low, Close (OHLC) data structure is proposed as a prediction target and shown to be a significantly easier target to forecast, suggesting previous works have attempted to extract predictive power from OHLC data in the wrong context. A prediction framework incorporating a factor discovery and mining process is developed using Randomised Decision Trees, with Long Short Term Memory Recurrent Neural Networks subsequently demonstrating remarkable predictive capabilities of up to 50.73% better than random (75.42% accuracy) on hourly data based on the FGBL German Bund futures contract, and 42.5% better than random (72.04% accuracy) on a comparison Bitcoin dataset.
| Type: | Proceedings paper |
|---|---|
| Title: | A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data |
| Event: | 26th International Conference on Artificial Neural Networks (ICANN) |
| Location: | Alghero, ITALY |
| Dates: | 11 September 2017 - 14 September 2017 |
| ISBN-13: | 978-3-319-68611-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-68612-7_56 |
| Publisher version: | https://doi.org/10.1007/978-3-319-68612-7_56 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Machine learning, LSTMs, Decision Trees, Factor Mining, OHLC data, Financial forecasting, Mid-price |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10063445 |
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