Abstract
Intraday trading has some appealing characteristics. For example, overnight gap risks are greatly reduced. Intraday trading strategies tend to achieve better risk adjusted returns. However, academic literature on intraday trading strategies is relatively scarce compared to a significant amount of literature based on daily closing data. This may be partly related to the increased difficulty of dealing with intraday data. In the present paper we expand on a novel approach that builds an intraday trading strategy on open-high-low-close (OHLC) data. OHLC data is easily available from most database vendors. We use OHLC data to train neural networks that forecast the day’s high and low of liquid US stocks and ETFs. The resulting long-short strategy tries to take advantage of the daily trading range of a security and exits all positions at the close. A volatility filter further improves risk-adjusted returns.
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
Preview
Unable to display preview. Download preview PDF.
References
Dunis, C., Laws, J., Rudy, J.: Profitable mean reversion after large price drops: A story of day and night in the s&p 500, 400 midcap and 600 smallcap indices. Journal of Asset Management 12, 185–202 (2011)
Martinez, L.C., da Hora, D.N., de M. Palotti, J.R., Meira Jr., W., Pappa, G.L.: From an artificial neural network to a stock market day-trading system: A case study on the bmf bovespa. In: Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19 (2009)
Gomide, P., Milidiu, R.: Assessing stock market time series predictors quality through a pairs trading system. In: 2010 Eleventh Brazilian Symposium on Neural Networks (SBRN), pp. 133–139 (2010)
Zimmermann, H.G.: Forecasting the Dow Jones with historical consistent neural networks. In: Dunis, C., Dempster, M., Terraza, V. (eds.) Proceedings of the 16th International Conference on Forecasting Financial Markets: Advances for Exchange Rates, Interest Rates and Asset Management, Luxembourg, May 27-29 (2009)
Zimmermann, H.G.: Advanced forecasting with neural networks. In: Dunis, C., Dempster, M., Breitner, M.H., Rösch, D., von Mettenheim, H.J. (eds.) Proceedings of the 17th International Conference on Forecasting Financial Markets: Advances for Exchange Rates, Interest Rates and Asset Management, Hannover, May 26-28 (2010)
von Mettenheim, H.J., Breitner, M.H.: Robust forecasts with shared layer perceptrons. In: Dunis, C., Dempster, M., Breitner, M.H., Rösch, D., von Mettenheim, H.J. (eds.) Proceedings of the 17th International Conference on Forecasting Financial Markets: Advances for Exchange Rates, Interest Rates and Asset Management, Hannover, May 26-28 (2010)
von Mettenheim, H.J., Breitner, M.H.: Neural network model building: A practical approach. In: Dunis, C., Dempster, M., Girardin, E., Péguin-Feissolle, A. (eds.) Proceedings of the 18th International Conference on Forecasting Financial Markets: Advances for Exchange Rates, Interest Rates and Asset Management, Marseille, May 25-27 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
von Mettenheim, HJ., Breitner, M.H. (2012). Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-32909-8_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
eBook Packages: Computer ScienceComputer Science (R0)