Prediction of Short-Term Trends of TAIEX via Its High-Frequency Constituent Share Prices
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === 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...
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Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2018
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Online Access: | http://ndltd.ncl.edu.tw/handle/3jp762 |
Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === 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.
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