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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81146

    Title: 金融商品走勢預測;Financial Product Tendency Prediction
    Authors: 林昇暉;Lin, Sheng-Hui
    Contributors: 資訊工程學系在職專班
    Keywords: 金融商品;類神經網路;Financial;LSTM
    Date: 2019-07-30
    Issue Date: 2019-09-03 15:37:00 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 資本主義社會,投資是人們累積財富的路徑之一,市場推出各種金融商品讓投資人操作,投資人藉由經驗累積佐以經濟學理論,發展出多種面向的投資策略,例如技術面、基本面、籌碼面以及消息面。其中技術面是透過分析每日股價以及成交量來預測未來走勢,取得適當買賣點進行操作。但是金融商品數量繁多,非專職投資人無法在短暫的時間內消化這麼多數據,本論文研究透過深度學習方法,佐以技術分析理論對金融數據進行訓練,得出模型,用以預測未來走勢,並能夠快速地在大量金融商品中找出理想投資標的。;In Capitalism, investment is a method to earn more money. There are lots of financial products to let humans invest. By financial theory, people create couples strategy to make invest decision. Including technical analysis, fundamental analysis, chip analysis and news analysis. The technical analysis is based on daily price and volume to predict future tendency to figure out appropriate buy or sell timing. There are a large amount of financial products. People who are not an expert can’t analyze all products in a short time. This paper is focus on training models through deep learning method with technical analysis theory. By those models to predict price, we can easily get excellent products in a amount of stocks.
    Appears in Collections:[資訊工程學系碩士在職專班 ] 博碩士論文

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