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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124718
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124718

    Title: 應用卷積神經網路於ETF漲跌之研究
    The study of application of Convolutional Neural Networks to exchange Traded Funds trend
    Authors: 蘇彥昀
    Su, Yen-Yun
    Contributors: 杜雨儒

    Tu, Yu-Ju
    Liu, Wen-Qing

    Su, Yen-Yun
    Keywords: 人工智慧
    Artificial Intelligence
    Convolutional Neural Network
    Technical Analysis
    Date: 2019
    Issue Date: 2019-08-07 16:08:30 (UTC+8)
    Abstract: 本研究使用深度學習中的卷積神經網路,針對美國資產規模前十大ETF來做漲跌趨勢的預測,建立卷積神經網路架構,再利用收集的2000年至2018年的歷史資料來做資料的前處理、訓練模型,把得到的模型進一步去預測未來的漲跌情況。

    With the growth of computer hardware speed, artificial intelligence, which requires a lot of computing technology, is popular again. Due to the progress of GPU performance, effective parallel computing accelerates the operations required by the algorithm, allows these artificial intelligence technology being more convenient and effective in different fields. Deep learning is one of the artificial intelligence that has been discussed by many people in recent years.

    This study is based on the convolutional neural network in deep learning, and forecasts the ups and downs of the US Top 10 ETF. First, the convolutional neural network architecture of the each ETF is established. Then, the historical data from 2000 to 2018 will be preprocessed to train the model, and further, the model will be used to predict the future trend.

    In addition to ETF historical data, this study selected multi-oriented technical analyses, including simple moving average, relative strength index and other related technical analyses. Groupthink is a psychological phenomenon that occurs within a group of people in which the desire for harmony or conformity in the group results in an irrational or dysfunctional decision-making outcome. In this study, groupthink means the reaction of the stockholder in anticipation of future market changes that is represented the VIX index (commonly known as Volatility Index). This study provides multiple sets of parameters for predicting ETF ups and downs in convolutional neural network models. The experimental performance also proves that this method can obtain a good return rate (25.58%) and provide investors as a reference in the future.
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    [2] 吳哲緯,(2017)。使用深度學習卷積神經網路預測股票買賣策略之分類研究,國立中山大學,資訊管理學系研究所,高雄。
    [3] 林婉茹,(2004)。類神經網路於台灣50指數ETF價格預測與交易策略之應用,輔仁大學,金融研究所,台北。
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356037
    Data Type: thesis
    DOI: 10.6814/NCCU201900552
    Appears in Collections:[資訊管理學系] 學位論文

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