臺灣博碩士論文加值系統

本文以美國銀行為研究對象，使用動態二階段GMM模型進行分析，探討金融創新與銀行績效的關係，樣本期間涵蓋2011年至2019年。本文使用USPTO資料庫的金融專利申請數量作為金融科技之衡量指標，並以文字過濾法將專利按照創新類別分類；而銀行績效則以CAMEL評等系統作為衡量指標。實證結果顯示金融創新對銀行績效有顯著正面的影響，而其中用作資料分析的創新技術於七個金融科技分類中最為有價值。在數位轉型的浪潮下，這些發現對銀行及金融業制定金融科技策略具有重要的意義及貢獻。

This study investigates the relationship between financial innovations and bank performance of the United States from 2011 to 2019. We use text-based filter analysis to identify fintech patent application filings from USPTO as proxy of financial innovation. Then we adopt bank rating system CAMEL to evaluate banks’ financial performance. Our estimated models are analyzed by the two-step generalized method of moments estimators. The empirical results indicate a positive significant association between fintech and bank performance and the fintech innovation of data analytics being the most valuable among seven main fintech categories. These findings have meaningful implications for banking and financial institutions to formulate fintech strategies under the new wave of digital transformation.

Abstract in Chinese i
Abstract in English ii
Table of Content iii-iv
Figures and Tables v
Section 1 Introduction
1.1 Research background and objective 1
1.2 Overview of U.S. fintech market 2
1.3 Challenges of traditional U.S. banks 3
1.4 The technology development of U.S banks by patents 5
1.5 Significances of the study 7
1.6 Research structure 8
Section 2 Literature review
2.1 The theoretical background of financial innovation 9
2.2 Fintech, banking industry and bank performance 9
2.3 Fintech, patents and classification of patents 10
Section 3 Data sources and methodology
3.1 Patent’s data 12
3.2 Identifying and classifying patent into fintech categories 12
3.3 Bank performance’s data 15
3.4 Empirical Methodology and variables 17
3.5 Descriptive Statistics 19

Section 4 Empirical results
4.1 The role of fintech to bank performance 21
4.2 The impact of fintech categories to bank performance 22
4.3 The impact of fintech categories to bank performance with lagged response 25
4.4 Interaction effect of bank size and fintech categories to bank performance 27
Section 5 Conclusion
5.1 Conclusion 30
5.2 Limitations 31
5.3 Recommendations 31
Reference 32
Appendix 1: Sample of U.S Banks 37
Appendix 2: Definition of Bank type 38
Appendix 3: Keywords of each fintech category 39

Figures and Tables

Figure 1: Total fintech investment activity in the United States 3
Figure 2: Value of Investment in fintech companies worldwide in 2020 3
Figure 3: Fintech adoption of the United States from 2015 to 2019 4
Figure 4: Number of Neobank account holders in the United States in 2020 5
Figure 5: Fintech classification development and patent (2011-2019) 6
Figure 6: Total global fintech investment activity on cybersecurity 7
Table 1: Overview of construction steps of patent data 12
Table 2: Description of innovation type 14
Table 3: Result of classification of U.S. bank’s fintech patents 15
Table 4: List of Variables, Definitions 18
Table 5: Descriptive Statistics 19
Table 6: The overall impacts of FinTech industry development on bank performance 21
Table 7: The impacts of fintech categories on bank performance 22
Table 8: Interaction effect of fintech categories on bank performance 25
Table 9: Interaction effect of fintech categories on bank performance 27

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