應用深度學習於財經新聞來源對股價趨勢預測之研究

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Title:	應用深度學習於財經新聞來源對股價趨勢預測之研究
Other Titles:	A study of deep learning with different finance news providers for forecasting stock price trends
Authors:	李嘉洲;Lee, Chia-Chou
Contributors:	淡江大學資訊管理學系碩士在職專班戴敏育
Keywords:	文字探勘;股價趨勢預測;深度學習;財經新聞;異常報酬;text mining;Stock Price Trends Forecasting;Deep Learning;Finance News;Abnormal Returns
Date:	2016
Issue Date:	2017-08-24 23:45:52 (UTC+8)
Abstract:	對股價趨勢進行預測一直是投資人非常感興趣的議題，隨著電子媒體快速發展，每日有數以百計與個股相關的財經新聞於不同的電子媒體發佈。以往也有許多相關的研究，運用文字探勘技術與機器學習方式試著對股價趨勢進行預測，以探討是否能藉此獲得異常報酬，然而，卻顯少有研究探討於不同的新聞媒體是否影響預測的結果。本研究聚焦在不同的財經新聞來源對投資決策的影響，並運用深度學習以提高預測能力。實證結果顯示不同的財經新聞來源對投資人的投資決策影響明顯不同，且深度學習能夠提高新聞分類的準確性。 Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. There were many previous related researches discussed whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. This research focuses on the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning.
Appears in Collections:	[Graduate Institute & Department of Information Management] Thesis

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