Automated Stock Trading Algorithm Using Neural Networks

  • Brett Taylor
  • Min Kim
  • Anthony Choi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)


One of many applications of artificial neural networks is discovering non-linear patterns in time series data. In this paper, analysis of the efficacy of applying an artificial neural network to the time series data produced by fluctuating stock prices is discussed in more detail. There are few current models that are capable of analyzing stocks but they lack in predicting effectively. Results of this neural network are examined through the generated return on investment. The network used for stock analysis is a four layer, feed-forward artificial neural network. The results of this network reveal that artificial neural networks are capable of performing technical analysis on stock prices. The return on investment ranged anywhere from 0.8 to 5.28 % per month or as extrapolated over a year, as high as 17 %.


Neural network Stock market Trading algorithm S&P 500 Autonomous investing 



This research was partially funded NASA. Authors would like to thank Roland Adams for his help with editing.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringMercer UniversityMaconUSA
  2. 2.Department of Government and SociologyGeorgia College and State UniversityMilledgevilleUSA

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