Modeling the behavior of the stock market with an Artificial Immune System

M. Butler, D. Kazakov

Research output: Contribution to conferencePaperpeer-review


This study analyzes the effectiveness of an Artificial Immune System (AIS) to model and predict the movements of the stock market. To aid in this research the AIS models are compared with a k-Nearest Neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. The analysis shows that the AIS produced overall accuracy results of 67% over a 20 year test period and that the increased complexity of the model was warranted by the statistically significant superior results when compared to the simpler instance-based approach of kNN. The accuracy results were comparable to those obtained from training the ANN and the trading results outperformed the market benchmark, providing evidence that the stock market had a degree of predictability during the time period of 1989-2008. In general the practice of using the natural immune system to inspire a learning algorithm has been established as a viable alternative to modeling the stock market when implementing a supervised learning approach.
Original languageEnglish
Pages1 -8
Publication statusPublished - 1 Jul 2010


  • AIS model
  • artificial immune system
  • artificial neural network
  • benchmark market portfolio
  • k-nearest neighbors algorithm
  • learning algorithm
  • natural immune system
  • stock market prediction
  • supervised learning approach
  • artificial immune systems
  • benchmark testing
  • economic forecasting
  • stock markets

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