本研究探討美國《晶片與科學法案》（CHIPS and Science Act）作為一項重大的地緣政治政策，對台灣半導體產業所產生的影響。本文採用創新性的研究方法，結合事件研究法（event study）與GARCH波動模型，分析該法案宣示與落實過程中三個關鍵事件日所引發的股市反應。此一方法能同時捕捉短期異常報酬與報酬波動的變化，從而全面呈現地緣政治不確定性下的市場動態。
此外，研究進一步進行橫斷面迴歸分析，以探討企業層級的財務特徵如何影響市場反應，分析焦點包括：研發強度（R&D intensity）、資本支出（capital expenditures）、外資持股比率（QFII holdings），以及對中國市場的盈餘曝險（China EPS contribution）。實證結果顯示，《晶片與科學法案》相關事件引發台灣半導體企業顯著的短期市場波動，且在累積異常報酬（CARs）方面呈現明顯差異。GARCH 模型估計結果指出，各事件日前後股票報酬波動顯著上升，反映整個事件投資人呈現樂觀態度，但依然會因恐慌和不確定做出不理智選擇。
進一步的橫斷面分析結果指出，研發強度較高的公司，恐在事件中期面臨負向異常報酬，顯示其資訊階層低但依然影響。外資持股比例較高的公司於事件前為正向異常報酬，但會在當天轉為負向；顯示其市場整體對事件的正向態度，卻依然會因為羊群效應而在事件當天轉變態度。整體而言，本研究結果凸顯企業特有財務特徵在調節地緣政治風險衝擊中的關鍵角色，並驗證結合事件研究法與GARCH模型之方法，在分析政策驅動型市場事件時的應用價值與有效性

This study examines the impact of the U.S. CHIPS and Science Act—a major geopolitical policy—on Taiwan’s semiconductor industry. Using an innovative combination of event study and GARCH modeling, we analyze stock market reactions across three key event dates associated with the Act’s announcement and implementation. This approach captures both immediate abnormal stock returns and changes in return volatility, providing a comprehensive view of market dynamics under geopolitical uncertainty. In addition, we conduct cross-sectional regression analysis to investigate how firm-level financial characteristics shape these responses..
The empirical results reveal that the CHIPS Act events elicited significant short-term market volatility and distinct patterns in cumulative abnormal returns (CARs) for Taiwanese semiconductor firms. GARCH-based volatility estimates indicate heightened stock return fluctuations around each event date, reflecting increased investor uncertainty. Pre-event CARs are mildly negative, but event-day returns turn significantly positive, lifting the overall CAR to +4.3 % by day +15 and signaling renewed investor optimism after the Act passed. Cross-sectional analysis shows that companies with higher R&D intensity often have negative abnormal returns in the middle of the event period, even if they have less information. Firms with more foreign ownership have positive abnormal returns before the event, but these become negative on the event day, likely due to herding behavior. These results highlight the key role of firm financial characteristics in handling geopolitical risks and show that using event study and GARCH together is valuable for analyzing policy impacts.

Content
論文審定書 i
致謝 ii
Acknowledgements iii
摘要 iv
Abstract v
List of Equation, Figures and Tables xi
1. Introduction 1
1.1 Global Context and Strategic Importance of Semiconductors 1
1.2 The CHIPS and Science Act as a Geopolitical Policy Tool 1
1.3 Taiwan’s Semiconductor Industry: Strategic Asset or Vulnerability? 2
1.4 USA-Sino Trade War and Technological Competition 2
1.5 Problem Statement and Research Gap 3
1.6 Research Objectives and Key Questions 3
1.7 Thesis Structure 4
2. Literature Review 5
2.1 The Global Semiconductor Industry and Taiwan’s Strategic Role 5
2.1.1 Global Semiconductor Industry Overview and Value Chain Structure 5
2.1.2 Key Characteristics of Taiwan's Semiconductor Industry 6
2.1.3 Industrial Clusters, Government-Industry Collaboration, and Taiwan’s Strategic Position 9
2.2 Geopolitics and Industrial Policy: Theoretical Framework 11
2.2.1 The definition of geopolitics and geo-economics 11
2.2.2 Historical Perspectives and Strategic Technology Policy Frameworks 13
2.2.3 The Geopolitical Impact of the CHIPS Act on the Semiconductor Industry 14
2.3 Political Risk and Stock Market Reactions: Empirical Evidence 17
2.3.1 Political Risk 17
2.3.2 Event Study Methodologies 20
2.3.3 Transmission Mechanisms of Political Risk, Policy Uncertainty, and Market Volatility 23
2.4 Firm-Specific Financial Indicators and Their Moderating Effects 25
2.4.1 The Relationship Between Financial Ratios and Stock Abnormal Returns 25
2.4.2 The Impact of Capital Expenditure, R&D Intensity, EPS Foreign Exposure, and QFII Holdings 28
2.5. Research Gap and Contribution 30
2.5.1 Research Gap 30
2.5.2 Research Contribution 31
3. Research Method 32
3.1 Data and Sample Selection 32
3.1.1 Description of Data Sources 32
3.1.2 Justification for Using the Taiwan All-Market Semiconductor Index 33
3.1.3 Firm Selection Criteria 34
3.1.4 Data Completeness and Final Sample 34
3.2 Event Study Method 35
3.2.1 Overview of Event Study Method 35
3.2.2 Selection and Justification of Event Dates 36
3.2.3 Definition of Event Windows 37
3.2.3 Expected Returns Model and Abnormal Return Calculation 37
3.3 Volatility Modeling (GARCH Framework) 39
3.3.1 Rationale for Incorporating GARCH Models 39
3.3.2 Potential GARCH Specifications and Selection Criteria 40
3.3.3 How Volatility Modeling Complements Event Study Analysis 43
3.4 Statistical Significance Tests 44
3.4.1 Cross-sectional Test Statistic 44
3.4.2 Sign Test Statistic 45
3.4.3 Generalized Sign Test Statistic 46
3.4.4 Interpretation and Implications 47
3.5 Regression Design and Hypothesis Development 48
3.5.1 Dependent Variable 49
3.5.2 Independent Variables 49
3.5.3 Control Variables 52
3.5.4 Regression Model Specification 52
4. Result 54
4.1 Event Study Results 54
4.1.1 Descriptive Statistics and Event Window Analysis 54
4.1.2 Statistical Significance and Abnormal Returns Analysis 54
4.1.3 Summary and Selection of Key Event Date 56
4.2 Volatility Analysis from GARCH Models 57
4.2.1 GARCH Model Selection and Parameter Estimation 58
4.2.2 EGARCH(2,1) Estimation Results and Interpretation 59
4.2.3 Volatility Clustering Visualization 60
4.2.4 Model Diagnostics and Robustness 60
4.2.5 Summary of Volatility Analysis Findings 61
4.3 Regression Results and Interpretation 62
4.3.1 Operational Performance Variables (Capex, R&D Intensity, EPS China Exposure) 63
4.3.2 Financial Structure and Foreign Exposure (QFII Holdings and LTD Ratio) 64
4.3.3 Firm Size and Overall Model Evaluation 65
5. Conclusion 67
5.1 Summary of Key Findings 67
5.1.1 Event Study Results and Their Significance 67
5.1.3 Role of Firm-level Financial Indicators 71
5.2 Policy Recommendations for Taiwan and Industry Stakeholders 76
5.2.1 Recommendations for Taiwanese Policymakers 76
5.2.2 Guidance for Semiconductor Firms 77
5.3 Contributions, Limitations, and Directions for Future Research 80
5.3.1 Contributions to Literature 80
5.3.2 Limitations of the Study 81
5.3.3 Directions for Future Research 84
6. Reference 89
7. Appendix 97
Appendix A: All company samples and Dataset 97
Appendix B: The table of 06/23,07/29 and 08/09 Abnormal Return 98
Appendix C: Variable Definition and Calculation Formula 104

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