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.

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