Self-organizing maps
Convergence and ordering of Kohonen's batch map
Neural Computation
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Conformal self-organization for continuity on a feature map
Neural Networks
Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Statistical tools to assess the reliability of self-organizing maps
Neural Networks - New developments in self-organizing maps
Improving the Effectiveness of Self-Organizing Map Networks Using a Circular Kohonen Layer
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Advanced Technology Track - Volume 5
SOM-based algorithms for qualitative variables
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Spherical self-organizing map using efficient indexed geodesic data structure
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM)
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
The Concentration of Fractional Distances
IEEE Transactions on Knowledge and Data Engineering
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
On the impact of the metrics choice in SOM learning: some empirical results from financial data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
An intraday trading model based on Artificial Immune Systems
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Assessing the efficiency of health care providers: a SOM perspective
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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We describe a method to develop trading rules based on the responses of self-organizing maps (SOMs), trained under various distance metrics. The effectiveness of the procedure is examined on 5min data of S&P MIB financial index, and its performances compared with those of classical buy and hold trading technique. The noticeable innovations of the paper include the methodology itself, which brings SOMs into a decision making tool to operate into the market; the focus on intraday tradings bars; the systematic study of how changes in the distance metrics employed to train the maps may affect the overall performances, and, finally, the discussion of system performances both in the absence and in the presence of commission fees. At the current stage the results, evaluated with both financial and statistical indicators, bring us to the following conclusions: (a) self-organizing maps can be helpful to localize profitable intraday patterns, achieving more stable performances than common trading rules; (b) working with proper metrics may enhance the overall performances; (c) trading strategies based on unsupervised neural networks make exploitable with profits almost continuous trades, until commission fees maintain below suitable thresholds.