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Title: | 上市個股預測台灣加權指數高頻趨勢 Prediction of Short-Term Trends of TAIEX via Its High-Frequency Constituent Share Prices |
Authors: | Yi-Ke Huang 黃奕軻 |
Advisor: | 呂育道(Yuh-Dauh Lyuu) |
Keyword: | 台灣證券市場,日內資料,資料探勘,神經網絡,股票預測, Taiwan Stock Market,Intraday trading,Data mining,Neural network,Stock prediction, |
Publication Year : | 2018 |
Degree: | 碩士 |
Abstract: | 股市指數通常由多家上市公司的股票組合而成。然而其價格決定於複雜的交易過程,並且資產間時常具有非線性的相依性。因此為了預測指數未來的短期走勢,我們考慮以卷積神經網路處理非線性多維時間序列。我們將模型用於預測指數的上漲或下跌,並實證模型的預測性。市場高頻資料 (每5秒) 包含了 2015 年至 2017 年日內的臺灣加權指數與個股價格。實驗結果顯示,模型使用在日內交易預測能得到良好的準確率與績效。 A stock index consists of a board selection of stocks. Stylized facts show non-linear correlations exist between stock prices. Therefore, predicting the direction of an index involves modeling and analyzing sophisticated multi-dimensional time series. We employ the convolutional neural network to forecast the intra-day price movement of the Taiwan Stock Exchange Weighted Index (TAIEX). Furthermore, we verify the prediction performance of our model on the high-frequency data (5 sec) of the TAIEX and its constituent share prices from 2015 to 2017. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69370 |
DOI: | 10.6342/NTU201801430 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊工程學系 |
Files in This Item:
File | Size | Format | |
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ntu-107-1.pdf Restricted Access | 1.47 MB | Adobe PDF |
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