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dc.contributor.author林益民en_US
dc.contributor.authorLin, Yi-Minen_US
dc.contributor.author陳安斌en_US
dc.contributor.authorChen, An-Pinen_US
dc.date.accessioned2014-12-12T01:50:57Z-
dc.date.available2014-12-12T01:50:57Z-
dc.date.issued2010en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079834507en_US
dc.identifier.urihttp://hdl.handle.net/11536/47912-
dc.description.abstract臺灣交易市場為提供投資人更即時資訊並符合市場需求,臺灣證券交易所於民國100年元月17日將指數與成交及委託統計資訊揭露頻率由現行的1分鐘縮短為15秒,與亞洲地區交易所接軌。 本研究提出以倒傳遞類神經網路架構,搭配技術分析指標,學習臺灣加權指數期貨日內極短線的趨勢行為,嘗試找出市場的知識規則,建立極短線趨勢預測模型。在極短線下,交易時常處於盤整期或是微小跳動,本研究為了克服此問題,提出資料平滑化和過濾盤整期資料兩個資料處理的方法,嘗試增進倒傳遞類神經網路的學習效果。 由實驗結果得知,在極短線時資料蘊含的知識量不足,若輔以的額外的資料前處理方法,資料平滑化和過濾盤整期資料,可以得到較高的準確率和獲利能力。zh_TW
dc.description.abstractIn order to provide timeliness information and meet the market demand, Taiwan Stock Exchange (TWSE) has increased the frequency of statistical information disclosure on TSEC weighted index from originally 1 minute to 15 seconds on January 17, which is now synchronized with Asia exchanges. In this study, we propose a neural network architecture utilizing technical analysis indicators, attempting to find the knowledge rules from TAIEX Futures’ intraday trading data and construct a TAIEX Futures’ short-term time interval predicting model. In short-term time interval, the transaction data often changes in a small range; we propose two data pre-processing techniques to overcome this problem and improve neural network’s learning ability. The results of experiments indicates that although the transaction data contains insufficient information and knowledge in a short-term time interval, it is possible to achieve better accuracy and profitability through data smoothing and data filtering.en_US
dc.language.isozh_TWen_US
dc.subject日內交易zh_TW
dc.subject臺灣指數期貨zh_TW
dc.subject技術分析zh_TW
dc.subject極短線zh_TW
dc.subjectIntraday Tradingen_US
dc.subjectTAIEX Futuresen_US
dc.subjectTechnical Analysisen_US
dc.subjectShort-Term Tradingen_US
dc.title應用類神經網路於臺灣期貨指數極短線走勢行為知識發現zh_TW
dc.titleAplying Neural Network on Short Term Intraday Trading of Taiwan Index Futures Marketen_US
dc.typeThesisen_US
dc.contributor.department資訊管理研究所zh_TW
Appears in Collections:Thesis