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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A tick is the minimum movement in a price series, which for the FGBL futures contract is equivalent to 10 EUR.
- 2.
Gradient calculations in layers further from the output accumulate progressively more fractional derivative factors, which results in weight changes tending to zero in lower layers and thus vanishing.
References
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Marshall, B., Young, M., Rose, L.: Candlestick technical trading strategies: can they create value for investors. J. Bank. Financ. 30, 2303–2323 (2005)
Horton, M.: Stars, crows, and doji: the use of candlesticks in stock selection. Q. Rev. Econ. Financ. 49, 283–294 (2009)
Fock, J., Klein, C., Zwergel, B.: Performance of candlestick analysis on intraday futures data. J. Deriv. 13(1), 28–40 (2005)
Xie, H., Zhao, X., Wang, S.: A comprehensive look at the predictive information in Japanese candlesticks. In: International Conference on Computational Science (2012)
Lu, T., Chen, Y., Hsu, Y.: Trend definition or holding strategy: what determines the profitability of candlestick charting. J. Bank. Financ. 61, 172–183 (2015)
Lu, T.: The profitability of candlestick charting in the Taiwan stock market. Pac.-Basin Financ. J. 26, 65–78 (2014)
Breiman, L., Friedman, R.A., Olshen, R.A., Stone, C.G.: Classification and Regression Trees. Wadsworth, Pacific Grove (1984)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks, pp. 586–591 (1993)
Smeeton, N.C.: Early history of the kappa statistic. Biometrics 41, 795 (1985). JSTOR 2531300
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mann, A.D., Gorse, D. (2017). A New Methodology to Exploit Predictive Power in (Open, High, Low, Close) Data. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-68612-7_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68611-0
Online ISBN: 978-3-319-68612-7
eBook Packages: Computer ScienceComputer Science (R0)