論文提要內容：

台灣金融控股公司的運營效率對確保穩定的金融體系和促進整體經濟發展至關重要。動態網絡數據包絡分析（以下簡稱DNDEA）模型已廣泛用於評估金融機構的表現。本研究採用並行兩階段DNDEA模型，評估2017年至2022年間台灣金融控股公司的運營、管理效率和市場能力。通過確定表現優秀的公司並提供有價值的見解來增強效率，這項研究有助於實證研究台灣金融控股公司的運營管理效率和市場能力，為相關利益相關者的決策和政策制定提供有價值的參考。
本研究利用公開的台灣金融控股公司的財務指標和比率來評估它們在運營階段（營業費用、營業收入）、管理階段（資本負債比率、信用違約率、股東權益報酬率）和市場能力階段（長期投資、市值、每股收益）的表現。採用動態網絡分析方法來探索運營效率、管理效果和市場能力的趨勢。
研究結果顯示，在六年的研究期間內，有五家金融控股公司的運營效率指標低於1，顯示需要改善運營效率。在管理階段，有四家公司在某些年份達到1的效率得分，而其他公司的平均值低於1，表明有提升管理效率的空間。此外，六家金融控股公司的市場能力效率平均值低於1，顯示需要加強市場能力。儘管台灣的金融控股公司在運營效率方面有所改善，但仍需要進一步提高。建議包括加強企業治理和風險管理以提高內部管理效率，並從投資者的角度關注現金流量監控。此外，提高信息透明度，包括收入更新和未來增長前景，對於政府利益相關者至關重要。
Ⅲ

根據實證結果，得出以下發現： （1）在台灣的16家金融控股公司中，整體效率得分為0.6031。管理階段的效率超過運營階段的效率，而市場能力階段的效率表明管理階段是影響整體效率的重要因素。（2）總要素生產率（TFP）變動（滾動基期）的評估顯示，2017年至2022年的TFP變動為1.3109。管理階段的生產率變動高於運營階段的生產率變動。第二階段的市場能力自2017年至2022年有明顯改善。根據波士頓（BCG）矩陣分析，有七家公司表現出良好的整體效率和TFP表現，但有四家大型公司和三家分割公司需要提高其運營效率。此外，還有兩家公司需要改善運營、管理和市場能力。
這些研究結果和建議有助於提高台灣金融控股公司的整體效率和效能，為相關利益相關者的決策和政策制定提供有價值的見解。這項研究有望在確保台灣金融體系穩定和促進可持續經濟發展方面發揮重要作用。

Abstract:

The operational efficiency of Taiwan's financial holding companies is crucial for ensuring a stable financial system and promoting overall economic development. The dynamic network data envelopment analysis (The following is abbreviated as DNDEA.) model has been widely used to assess the performance of financial institutions. This study adopts the parallel two-stage DNDEA model to evaluate the operational, managerial efficiency, and market capabilities of Taiwan's financial holding companies from 2017 to 2022. By identifying top-performing companies and providing valuable insights to enhance efficiency, this research contributes to empirical studies on the operational management efficiency and market capabilities of Taiwan's financial holding companies, offering valuable reference for relevant stakeholders in decision-making and policy formulation.
In this study, publicly available financial indicators and ratios of Taiwan's financial holding companies are utilized to evaluate their performance in the operational stage (operating expenses,
Ⅴ
operating income), the management stage (debt-to-equity ratio, credit default ratio, return on
equity), and the market capabilities stage (long-term investments, market value, earnings per share). The dynamic network analysis method is employed to explore trends in operational efficiency, managerial effectiveness, and market capabilities.
The findings reveal that five financial holding companies demonstrated operational efficiency data below 1 during the six-year study period, suggesting the need for improvements in operational efficiency. In the management stage, four companies achieved an efficiency score of 1 in some years, while the average value of others remained below 1, indicating scope for enhancing managerial efficiency. Additionally, the average market capabilities efficiency of six financial holding companies was below 1, signifying the need to strengthen market capabilities. Although Taiwan's financial holding companies have shown improvement in operational efficiency, further enhancements are warranted. Recommendations include reinforcing corporate governance and risk management to improve internal management efficiency and focusing on cash flow monitoring from an investor perspective. Moreover, increasing information transparency, including revenue updates and future growth prospects, is essential for government stakeholders.

Based on empirical results, the following findings are observed:
(1) Among 16 financial holding companies in Taiwan, the overall efficiency score is 0.6031. The managerial stage efficiency exceeds the operational stage efficiency, and the market capability stage efficiency indicates that the managerial stage is a significant factor influencing overall efficiency.(2) The assessment of Total Factor Productivity (TFP) variation (rolling base period) shows that the TFP variation from 2017 to 2022 is 1.3109. The managerial stage productivity variation is higher than the operational stage productivity variation. The second-stage market capability demonstrates a noticeable improvement since 2017 to 2022.
Based on BCG matrix analysis, seven companies exhibit good overall efficiency and TFP performance, but four large-scale companies and three segmented companies need to enhance their operational efficiency. Additionally, two companies require improvement in operational, managerial, and market capabilities.
VI

The research findings and recommendations are instrumental in enhancing the overall efficiency and effectiveness of Taiwan's financial holding companies, providing valuable insights for decision-making and policy formulation for relevant stakeholders. This study is expected to play a significant role in ensuring the stability of Taiwan's financial system and promoting sustainable economic development.

Table of Contents
Letter of thanks..........................................................................................I
Abstract…………………………………………………………………..………... Ⅲ
Table of Contents…………………………………………………..……………VIII
List of Figures……………………………………………….…………………….. X
List of Tables…………………………………………………..…….…XII
Chapter 1: Introduction ..............................................................................1
Section1: Research Background...........................................................................1
Section2: Research Motivation............................................................................2
Section3: Research Methodology.........................................................................5
Section4: Research Objectives.............................................................................7
Section5: Scope of the Study..............................................................................10
Section6: The scope of this study encompasses.................................................11
Chapter 2: Literature Review ..............................................................................12
     Section1: Regarding the application of DEA…………………………………..12
    Section2: Regarding the operational management efficiency and corporate governance of financial institutions…………………………………………….19
Chapter 3: Research Methodology......................................................................24
Section1: Data Envelopment Analysis Data Envelopment Analysis….............24
Section2: Network DEA Model Theory..................................................................38
Section3: Dynamic Network Data Envelopment Analysis Models.…………...44
Section4: Productivity Index Model Theory..........................................................53
Chapter 4: Empirical　Results and Analysis……………………....…..57
Section1: Data Sources, Model, and Variable Explanations..............................57
Section2: Empirical Conclusion Analysis.…………………………….............62
Section3: Empirical Results of Malmquist Index .............................................72
Section4: Boston Consulting Group……………………………………………….91
Chapter5: Conclusion and Recommendations..........................................96
Section1: Research Findings..............................................................................96
Section2:. Research Recommendations...........................................................101
Section3: Summary…………………………………………...……...……....102
References.............................................................................................................104

Appendix 1: Abbreviations and Stock Ticker Symbols of Financial Holding Companies ...............................................................................117
List of Figures

Figure 3-1 Technical efficiency and allocation to efficiency (input- oriented)………28

figure 3-2 illustrates the relationship between technical efficiency and allocative efficiency (output-oriented)………………………………………….…………….…30

Figure 3-3 Measurement of technical efficiency and production scale in input-oriented and output-oriented approaches………………………………………………………31

Figure 3-4: illustrates the fundamental model of Network DEA………...……………40

Figure 3-5: Separable Model of Network DEA…………………………………....…42

Figure 3-6: illustrates the vertical integration model of Network DEA…...…………44

Figure 3-7: Dynamic DEA Model ……………….…………………….…………….47

Figure 4-1: Research Method Variables and Model……………….…...……….……60

Figure 4-2: BCG Matrix Diagram………………………………….……...………....94

Figure 5-1: BCG matrix chart of average overall efficiency and total factor productivity (rolling base period) from 2017 to 2022………………………………….……….…100
List of Tables

Table1-1: List of market value of financial holding companies……………………..10

Table 4-1: Definitions of Input and Output Variables……………………………………………….61

Table 4-2: Ranking Table of Operational Efficiency for Each DMU from 2017 to 2022
………………………………………………………..………………………………62

Table 4-3: Ranking Table of Management Efficiency for Each DMU from 2017 to 2022
……………………………………………………………………………………..…65

Table 4-4: Market Capability Ranking of Each DMU from 2017 to 2022……...……68

Table 4-5: Ranking of Overall Efficiency for Taiwanese Financial Holding Companies from 2017 to 2022. …………………………………………………...………………70

Table 4-6: Ranking of Dynamic Total Factor Productivity Change (Malmquist Index Efficiency) for Taiwan Financial Holding Companies from 2017 to 2022……………73
Stage1: Operational capability.

Table 4-7: Efficiency Ranking of Dynamic Total Factor Productivity (Malmquist Index) of Taiwan Financial Holding Companies from 2017 to 2022.…………………..……78

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