博碩士論文 110453016 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊管理學系在職專班zh_TW
DC.creator王怡如zh_TW
DC.creatorYi-Ju Wangen_US
dc.date.accessioned2023-7-3T07:39:07Z
dc.date.available2023-7-3T07:39:07Z
dc.date.issued2023
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=110453016
dc.contributor.department資訊管理學系在職專班zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract本文以台灣地區上市公司為研究對象,採用 2008 年 1 月至 2023 年 4 月期間的類股指數丶期貨指數資料及金融股個股股價,建立股價預測模型。本研究希望可以先從類股中發掘相關的輪動趨勢,以此預測下一批資金將投入的類股,在產業類股起漲前,可以提前佈局。主要使用 Transformer 模型探討股價預測問題,希望藉由模型的預測,預先得知股票漲跌趨勢,提供投資人做為交易的參考,能夠讓投資人降低投資風險,增加投資報價率。除了使用 Transformer 模型之外,並嘗試將類股輪動因素加入實驗中,驗證是否可以有效的增進股價預測正確性。zh_TW
dc.description.abstractThis article focuses on listed companies in Taiwan and establishes a stock price prediction model using sector index data, futures index data, and financial stock prices from January 2008 to April 2023. The study aims to explore the relevant sector rotation trends first to predict which sectors will receive the next batch of funds. It′s possible to make advance arrangements before the stocks rise. The main model used in this study is the Transformer model to explore stock price prediction problems. Through the model′s predictions, investors can obtain advance knowledge of the stock′s trend, reduce investment risks, and increase investment returns. In addition to using the Transformer model, the study also attempts to incorporate sector rotation factors to verify whether they can effectively improve the accuracy of stock price predictions.en_US
DC.subject類股輪動zh_TW
DC.subject神經網路zh_TW
DC.subject股價預測zh_TW
DC.subjectSector rotationen_US
DC.subjectNeural networken_US
DC.subjectStock price predictionen_US
DC.title類股輪動為基礎之神經網路趨勢預測-以台灣市場金融股為例zh_TW
dc.language.isozh-TWzh-TW
DC.titleNeural Network Trend Forecasting Based on Stock Rotation - A Case Study on Financial Stocks in the Taiwan Marketen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明