博碩士論文 91423029 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊管理學系zh_TW
DC.creator林晏秀zh_TW
DC.creatorYan-Xiu Linen_US
dc.date.accessioned2004-6-13T07:39:07Z
dc.date.available2004-6-13T07:39:07Z
dc.date.issued2004
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=91423029
dc.contributor.department資訊管理學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract在股票投資中,反轉何時發生一直是許多投資者期望能夠獲得的一項資訊,因為如果能在股價跌至低點或是升至高點時做交易,其所獲得的利益是該投資期間內最大的;因此,本研究嘗試使用遺傳演算法,對於股票價格的反轉點以及反轉幅度進行預測,期望藉由資訊科技的輔助搜尋出反轉點的規則特徵,並利用找出的規則特徵進行交易,以了解是否能由找出的規則特徵來進出市場,獲得良好的獲利。 由研究結果顯示,反轉點出現前,的確有一些跡象可循,但要在前一天預測出來十分不易,有較大的機會是出現在兩天內。再對反轉幅度進行研究的議題上,我們也可了解,區間劃分愈細所找出的規則特徵愈難表達出其所代表的反轉現象,且找出的規則特徵較不易在測試期實現。zh_TW
dc.description.abstractInvestors always pay attention to the information in reverse point of stocks. If we can trade on the timing that the stock price falls down to lowermost point or rise to highest point respectively, the maximum profit will be obtained in the period. This research tries to use genetic algorithms to predict the reverse point of the stock price and the range of the reverse point. We expect to search the rule of the reverse point by information technology and use this rule to trade in the stocks. Further, the expectation is desired to realize that we can make a good profit through the rule we found. In this study, we find that some portents emerged prior to the reverse point happened, and the reverse point is difficult to predict in one day as well; sometimes it happens in two days. We also figure it out when the range of the reverse point was divided finely, the rule of the reverse point we found is more difficult to stand for the reverse phenomenon. Also, it is more hardly to carry out in the test period.en_US
DC.subject反轉點zh_TW
DC.subject遺傳演算法zh_TW
DC.subject擇時zh_TW
DC.subjectreverse pointen_US
DC.subjectgenetic algorithmen_US
DC.subjectTimingen_US
DC.title利用遺傳演算法對股價反轉點的預測zh_TW
dc.language.isozh-TWzh-TW
DC.titleUsing genetic algorithm to predict the reverse points of the stock priceen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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