博碩士論文 etd-0728120-104849 詳細資訊


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姓名 劉懿(Liu Yi) 電子郵件信箱 E-mail 資料不公開
畢業系所 財務管理學系研究所(Department of Finance)
畢業學位 碩士(Master) 畢業時期 108學年第2學期
論文名稱(中) 智能行為財務模型-以行為財務觀點建構機器學習台股投資策略
論文名稱(英) Application of Machine Learning in Behavior Finance and Trading Strategy in Taiwan Stock Market
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    紙本論文:5 年後公開 (2025-08-28 公開)

    電子論文:使用者自訂權限:校內 5 年後、校外 5 年後公開

    論文語文/頁數 英文/76
    統計 本論文已被瀏覽 5594 次,被下載 0 次
    摘要(中) 本篇論文試圖以行為財務學的視角建構台股週沖交易策略,考量到人類的
    行為偏誤難以被完整的量化,且市場心理對於價量資訊間複雜的影響,因此本
    研究採用結合極限梯度提升樹 (XGBOOST) 演算法結合行為財務特徵變數分別建
    構預測未來五日報酬率之迴歸樹模型與預測未來五日漲跌機率的分類樹模型,
    交叉比對結合成選股模型以應用到台灣股票市場。
    在透過行為財務變數與 XGBoost 演算法模型所建構出的策略,於 2017 年至
    2020 年 3 月底的報酬率可達到約 70%,年化報酬率 22%,並幾乎於全回測期間
    內打贏大盤指數。
    摘要(英) This paper attempts to construct a weekly stock trading strategy for Taiwan stocks from
    the perspective of behavioral finance. Considering that behavior biases are too difficult to
    be quantified and the complex impact of market psychology on price-volume information,
    this paper uses eXtreme Gradient Boosting algorithm combines behavioral financial
    characteristic variables to construct two models : a regression tree model that predicts the
    future five-day return and a classification tree model that predicts the future five-day
    probability of stock prices rising, to construct a stock selection model for application to the
    Taiwan stock market.
    The strategy constructed through behavioral financial variables and the eXtreme
    Gradient Boosting algorithm model can achieve a return rate of about 70% from 2017 to the
    end of March 2020, an annualized return rate of 22%. This performance beats market
    benchmarks almost in the full period.
    關鍵字(中)
  • 台灣股票市場
  • 籌碼面
  • 機器學習
  • 投資人情緒
  • 行為財務
  • 關鍵字(英)
  • Machine Learning
  • Investor Sentiment
  • Behavioral Finance
  • Institutional Investors
  • Taiwan Stock Market
  • 論文目次 Content
    論文審定書 i
    摘要 ii
    Abstract iii
    Chapter 1. Introduction 1
    Chapter 2. Literature Review 3
    Chapter 3. Methodology 15
    3.1 Experimental Structure 15
    3.2 Data Collection and Features Construction 17
    3.3 Model Features 20
    3.4 Model Architecture 40
    Chapter 4. Empirical Results 53
    4.1 Model sensitivity analysis 54
    4.2 Model Architecture Backtesting Performance 58
    Chapter 5. Conclusion and Suggestions 63
    5.1 Conclusion 63
    5.2 Suggestion 64
    Reference 65
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    口試委員
  • 陳勤明 - 召集委員
  • 吳錦文 - 委員
  • 陳昇鴻 - 委員
  • 黃振聰 - 委員
  • 王昭文 - 指導教授
  • 口試日期 2020-06-18 繳交日期 2020-08-28

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