Taiwan Stock Investment with Gene Expression Programming

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  title =        "Taiwan Stock Investment with Gene Expression
  author =       "Cheng-Han Lee and Chang-Biau Yang and Hung-Hsin Chen",
  booktitle =    "18th International Conference in Knowledge Based and
                 Intelligent Information and Engineering Systems, {KES}
                 2014, Gdynia, Poland, 15-17 September 2014",
  publisher =    "Elsevier",
  year =         "2014",
  volume =       "35",
  editor =       "Piotr Jedrzejowicz and Lakhmi C. Jain and 
                 Robert J. Howlett and Ireneusz Czarnowski",
  pages =        "137--146",
  series =       "Procedia Computer Science",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, stock investment, majority
                 vote, technical indicator, strategy pool",
  bibdate =      "2014-10-12",
  bibsource =    "DBLP,
  URL =          "http://www.sciencedirect.com/science/journal/18770509/35",
  DOI =          "doi:10.1016/j.procs.2014.08.093",
  abstract =     "In this paper, we first find out some good trading
                 strategies from the historical series and apply them in
                 the future. The profitable strategies are trained out
                 by the gene expression programming (GEP), which
                 involves some well-known stock technical indicators as
                 features. Our data set collects the 100 stocks with the
                 top capital from the listed companies in the Taiwan
                 stock market. Accordingly, we build a new series called
                 portfolio index as the investment target. For each
                 trading day, we search for some similar template
                 intervals from the historical data and pick out the
                 pertained trading strategies from the strategy pool.
                 These strategies are validated by the return during a
                 few days before the trading day to check whether each
                 of them is suitable or not. Then these suitable
                 strategies decide the buying or selling consensus
                 signal with the majority vote on the trading day. The
                 training period is from 1996/1/6 to 2012/12/28, and the
                 testing period is from 2000/1/4 to 2012/12/28. Two
                 simulation experiments are performed. In experiment 1,
                 the best average accumulated return is 548.97percent
                 (average annualised return is 15.47percent). In
                 experiment 2, we increase the diversity of trading
                 strategies with more training. The best average
                 accumulated return is increased to 685.31percent
                 (average annualized return is 17.18percent). These two
                 results are much better than that of the buy-and-hold
                 strategy, whose return is 287.00percent.",

Genetic Programming entries for Cheng-Han Lee Chang-biau Yang Hung-Hsin Chen