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    Title: 應用深度學習預測銀行盈利能力
    Application of deep learning methods for predicting bank profitability
    Authors: 段晧偉
    Duan, Hao-Wei
    Contributors: 陳心蘋
    Chen, Hsin-Ping
    段晧偉
    Duan, Hao-Wei
    Keywords: 深度學習
    時間序列
    銀行
    盈利能力
    Deep Learning
    Time Series
    Banks
    Profitability
    Date: 2024
    Issue Date: 2024-11-01 11:35:37 (UTC+8)
    Abstract: 預測銀行盈利能力一直是一個重要議題,本研究旨在通過深度學習技術—長短期記憶模型(LSTM)和傳統時間序列—向量自我迴歸模型(VAR),對台灣五家商業銀行盈利能力進行預測並比較LSTM與VAR模型的預測準確度,其中銀行盈利能力指標為資產報酬率(ROA)和權益報酬率(ROE)。在本研究的樣本中結果顯示LSTM模型捕捉時間序列數據中的非線性關係和長期依賴性方面優於VAR模型。其次,在樣本中引入總體經濟變數後,LSTM模型的預測準確性度會更進一步提升。這些結果強調了深度學習模型在金融預測中的潛力和應用價值,為銀行管理者、投資者和政策制定者提供了有價值的參考依據,有助於更好地管理銀行風險。
    Predicting bank profitability has always been a crucial topic. This study aims to predict the profitability of five Taiwanese commercial banks using deep learning technology—Long Short-Term Memory (LSTM) and traditional time series models—Vector Auto Regression (VAR). The indicators of bank profitability are Return on Assets (ROA) and Return on Equity (ROE). Results show that the LSTM model outperforms VAR in capturing nonlinear relationships and long-term dependencies in time-series data. Furthermore, incorporating macroeconomic variables into the sample further improves LSTM’s predictive accuracy, highlighting its potential and value in financial forecasting, offering valuable insights for bank managers, investors, and policymakers to better manage bank risks.
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    Description: 碩士
    國立政治大學
    經濟學系
    110258043
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110258043
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

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