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    Title: 應用巨量資料分析與改良粒子群演算法於股票交易策略
    Authors: 陳婷妤;劉文卿
    Contributors: 資管博七
    Keywords: PSO (Particle Swarm Optimization);Big Data Analytics;stock trading strategies;stock market forecast;stock market prediction
    Date: 2015-11
    Issue Date: 2016-06-14 11:52:44 (UTC+8)
    Abstract: 股票交易是一錯綜複雜的動態系統,影響交易決策因素多元,加深預測困難度。然股票交易中最重要的事是在交易風險較低的情況下獲利最多,如何降低風險增加獲利程度,一直是相當有趣且實用的議題,在過去文獻有很多學者進行研究。巨量的歷史執行資料常常蘊含著大量有價值的潛存資訊和知識,近年來由於巨量資料分析興起,帶來資料說話的全新思維解決問題方式,且過去鮮有學者以巨量資料分析進行股票交易策略研究。因此,本研究運用巨量資料分析的資料觀點、平台技術,結合知識發現方法,提出改良式粒子群最佳化演算法,透過股票交易資料、簡單移動平均線技術指標與其黃金死亡交叉決策準則,從中進行知識挖掘,找出股票交易策略新態樣、準則及知識,大幅提升股票投資報酬率,並優於其他策略績效。
    Relation: 2015高美第三屆學術與創新技術實務研討會論文集, 高美醫護管理專科學校, pp.1
    Data Type: conference
    Appears in Collections:[Department of MIS] Proceedings

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