TY - GEN

T1 - Designing short term trading systems with artificial neural networks

AU - Vanstone, Bruce

AU - Finnie, Gavin

AU - Hahn, Tobias

PY - 2009

Y1 - 2009

N2 - There is a long established history of applying Artificial Neural Networks (ANNs) to financial data sets. In this paper, the authors demonstrate the use of this methodology to develop a financially viable, short-term trading system. When developing short-term systems, the authors typically site the neural network within an already existing non-neural trading system. This paper briefly reviews an existing medium-term long-only trading system, and then works through the Vanstone and Finnie methodology to create a short-term focused ANN which will enhance this trading strategy. The initial trading strategy and the ANN enhanced trading strategy are comprehensively benchmarked both in-sample and out-of-sample, and the superiority of the resulting ANN enhanced system is demonstrated.

AB - There is a long established history of applying Artificial Neural Networks (ANNs) to financial data sets. In this paper, the authors demonstrate the use of this methodology to develop a financially viable, short-term trading system. When developing short-term systems, the authors typically site the neural network within an already existing non-neural trading system. This paper briefly reviews an existing medium-term long-only trading system, and then works through the Vanstone and Finnie methodology to create a short-term focused ANN which will enhance this trading strategy. The initial trading strategy and the ANN enhanced trading strategy are comprehensively benchmarked both in-sample and out-of-sample, and the superiority of the resulting ANN enhanced system is demonstrated.

U2 - 10.1007/978-90-481-2311-7_34

DO - 10.1007/978-90-481-2311-7_34

M3 - Conference contribution

SN - 9789048123100

VL - 39 LNEE

T3 - Lecture Notes in Electrical Engineering

SP - 401

EP - 409

BT - Advances in Electrical Engineering and Computational Science

ER -