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Integration of a Predictive, Continuous Time Neural Network into Securities Market Trading Operations

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  • Christopher S Kirk

Abstract

This paper describes recent development and test implementation of a continuous time recurrent neural network that has been configured to predict rates of change in securities. It presents outcomes in the context of popular technical analysis indicators and highlights the potential impact of continuous predictive capability on securities market trading operations.

Suggested Citation

  • Christopher S Kirk, 2014. "Integration of a Predictive, Continuous Time Neural Network into Securities Market Trading Operations," Papers 1406.0968, arXiv.org.
    • Handle: RePEc:arx:papers:1406.0968
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    File URL: http://arxiv.org/pdf/1406.0968
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    1. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
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    Cited by:

    1. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.

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