利用混頻資料與機器學習方法對台灣半導體產業獲利之即時預測;Panel Data Nowcasting : The Case of Profitability in Taiwan′s Semiconductor Industry

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题名:	利用混頻資料與機器學習方法對台灣半導體產業獲利之即時預測;Panel Data Nowcasting : The Case of Profitability in Taiwan′s Semiconductor Industry
作者:	劉景翔;Liu, Ching-Shiang
贡献者:	經濟學系
关键词:	機器學習;即時預測
日期:	2025-06-30
上传时间:	2025-10-17 12:26:04 (UTC+8)
出版者:	國立中央大學
摘要:	本研究旨在運用混頻面板資料（Mixed-frequency Panel Data）與結構化機器學習方法，探討台灣半導體產業每股盈餘（EPS）與毛利率（Gross Margin）之即時預測（Nowcasting）問題。隨著全球經濟與政治環境快速變化，半導體產業作為數位經濟核心，其財務表現對市場投資與政策制定具有高度影響力。然而，傳統財務預測方法多依賴低頻歷史財報資訊，難以及時反映當期經濟環境與產業動態，亟需具備即時性與高預測準確度之方法加以改進。本研究運用稀疏群組正則化技術，處理高維結構化的混頻資料，並納入台灣上市企業特有之月營收資料，結合宏觀經濟變數，包括利率、匯率、通膨率與工業生產指數等，建構可即時預測 EPS 與毛利率之模型。月營收資料作為台灣企業每月強制公告之高頻營運資訊，能有效補強傳統財務報表資訊之時效性不足，提升模型對企業營運動態之即時掌握能力。實證部分以台灣半導體產業為研究對象，評估不同變數組合對 EPS 與毛利率預測效果之影響，並與傳統 AR(1) 基準模型進行比較，檢驗所提模型之預測效能。研究結果顯示，納入月營收資料與總體經濟變數後，能顯著提升 EPS 與毛利率預測準確度，提供更具時效性與實務參考價值之預測資訊。整體成果除具學術創新意涵，亦可協助投資人、企業管理者及政策制定者在快速變動的市場環境中，做出更為前瞻且有效之決策。;This study applies mixed-frequency panel data and structured machine learning techniques to nowcast two key financial indicators—earnings per share (EPS) and gross margin—for Taiwan’s semiconductor industry. In an era of rapidly changing global economic and political environments, the financial performance of the semiconductor sector, a cornerstone of the digital economy, plays a critical role in shaping investment strategies and policy decisions. However, conventional forecasting methods relying on low-frequency historical financial reports often fail to capture real-time economic shifts and industry dynamics, highlighting the need for more timely and accurate predictive models. This research leverages sparse-group regularization techniques to handle high dimensional, structured mixed-frequency data. In particular, it incorporates the unique monthly revenue disclosures mandated for listed companies in Taiwan, combined with key macroeconomic variables such as interest rates, exchange rates, inflation rates, and industrial production indices, to construct models for real-time EPS and gross margin forecasting. Monthly revenue data, as high-frequency operational information, effectively enhances the model’s ability to track current business performance and respond to market changes in a timely manner. An empirical analysis is conducted on Taiwan’s semiconductor industry to evaluate the impact of different variable combinations on the predictive performance for EPS and gross margin. The proposed models are also benchmarked against traditional AR(1) models to assess forecasting accuracy. The results demonstrate that incorporating monthly revenue data and macroeconomic indicators significantly improves predictive accuracy, providing more timely and practical insights. The findings offer theoretical contributions to the academic literature and valuable guidance for investors, corporate managers, and policymakers in making forward-looking and effective decisions in a dynamic market environment.
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