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A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data

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. Green open access

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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 > Provost and Vice Provost Offices
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: http://discovery.ucl.ac.uk/id/eprint/10063445
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