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Trading Strategy Mining with Gene Expression Programming

In the thesis, we apply the gene expression programming (GEP) to training profitable trading strategies. We propose a model which utilizes several historical periods that are highly related to the current template period, and the best trading strategies of the historical periods generate the trading signals. To keep stability of our model, we proposed the trading decision mechanism based on simple majority vote in our model. The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is selected as our investment target and the trading period starts from 2000/9/14 to 2012/1/17, approximately twelve years. In our experiments, the lengths of our training period are 60, 90, 120, 180, and 270 trading days, respectively. We observe that the model with higher voting threshold usually can make profitable trading decisions. The best cumulative return 236.25\% and the best annualized cumulative return 10.63\% occur when the 180-day training models pairs with available threshold 0.21 and voting threshold 0.88, which are higher than the cumulative return 0.96\% and annualized cumulative return 0.08\% of the buy-and-hold strategy.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0912112-113643
Date12 September 2012
CreatorsHuang, Chang-Hao
ContributorsChia-Ping Chen, Chang-Biau Yang, Hsing-Yen Ann, Kuo-Si Huang
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0912112-113643
Rightsuser_define, Copyright information available at source archive

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