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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/125528
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/125528


    Title: 基於多任務遷移學習之上市公司財報基本面與產業表現關聯股價預測
    Stock Price Prediction based on Financial Statement and Industry Status using Multi-task Transfer Learning
    Authors: 古昊中
    Ku, Hao-Chung
    Contributors: 姜國輝
    Chiang, Kuo-Huie
    古昊中
    Ku, Hao-Chung
    Keywords: 機器學習
    類神經網路
    長短期記憶網路
    遷移學習
    多任務學習
    股市預測
    財務報表
    Machine Learning
    Neural Network
    Long Short-Term Memory
    Transfer Learning
    Multi-task Learning
    Stock Market Forecasting
    Financial Statement
    Date: 2019
    Issue Date: 2019-09-05 15:44:32 (UTC+8)
    Abstract: 隨著資訊科技快速的發展,許多新的科技技術與創新應用不斷地出現,並受惠於硬體技術的大幅進步,在這資訊爆炸的年代,電腦能夠負擔技術上以及應用上的需求,為社會提供許多的便利、可靠性。同時提供給業界各個領域多元的解決方案與近破壞式的創新,讓商業不斷地進化、革新。以金融產業來說,金融業因涉及資金的流通,必須兼顧信用、安全、精準等,對於改變以及創新往往趨於保守,但因人工智慧的興起,看到了技術所帶來的好處並為上述的顧慮提供保證,開始帶動了金融科技的革命,為金融業服務提供有別於一般所設想的模式,並帶來可觀的成本降低以及獲益增加,使各個公司紛紛擁抱技術,享受技術所帶來的優勢與效益。
    本研究以半導體產業之上市公司為例,利用公司之資產負債表、損益表、現金流量表內會計項目作為公司股價預測之依據,藉由選取財務報表中的會計項目進行公司基本面資訊之取得。在模型方面,本研究採用類神經網路並結合長短期記憶網路作為預測之技術,並透過多任務學習的方式萃取產業基本面特徵與股價指數的潛在結構,將之應用於特定公司,以取得相應的市場價值,建構出合適於相同產業別多家上市公司之股價預測模型,實現單一模型具備多家公司預測之能力。藉由公司之間資料的輔助學習,以及公司與產業之間之鏈結,減少因資料涵蓋面不足所限制的預測效果以及增加現實中公司與產業間氣氛、趨勢實際的互動與牽引。研究結果顯示,本模型之預測具備一定泛化能力,能降低模型發生之誤差,不會因特定公司資訊稀少,導致預測效果特別不佳。此外,本研究樣本資料期間為十一年,結果顯示模型預測效果對於近期的預測有顯著且穩定的效果。
    With the rapid development of technology, new technologies are bringing into solutions and disruptive innovation to our society. Take financial industry as an example, the financial industry’s services and products are usually related to the circulation of funds. It results in the change and innovation tending to be conservative. However, due to the rise of artificial intelligence, companies recognized the benefits, safety and promise of technologies, and started to embrace those technologies which can provide new business model and bring benefits.
    This study retrieves the information from published financial statements of listed semiconductor industry in Taiwan as the basis of evaluation the stock prices. This study combines long short-term memory and neural network with multi-task learning to extract the hidden structure of industrial basic features and stock index, which then applies to specific company and gets the potential market value in proportion. The results show the capability of generalizing the prediction to other similar companies which might lack for complete financial metrics. Over a span of eleven years collected data, the results also present significant and stable performance especially for the prediction of the recent years.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    106356016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356016
    Data Type: thesis
    DOI: 10.6814/NCCU201900737
    Appears in Collections:[資訊管理學系] 學位論文

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