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Author: 方豪
Hao Fang
Thesis Title: 使用金融科技作法應用於金融市場中股票市場及銀行部門的研究
The Application Study of Using Fintech Approaches to the Stock Market and Banking Sector of Financial Markets
Advisor: 謝劍平
Joseph C. P. Shieh
Committee: 盧陽正
Yang-Cheng Lu
Chien-Fu Jeff Lin
Chun-Nan Chen
Chien-Chung Nieh
Tsung-Chi Cheng
Joseph C.P. Shieh
Degree: 博士
Department: 管理學院 - 財務金融研究所
Graduate Institute of Finance
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 101
Keywords (in Chinese): 金融科技文本探勘網路爬蟲投資人情緒股票報酬政黨傾向
Keywords (in other languages): Fintech, Text Mining, Web Crawler, Investors’ Sentiments, Stock Returns, Political Party Tendency
Reference times: Clicks: 543Downloads: 1
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This research applies fintech ways, comprising distributed web crawler and text mining, to establish information variables, to design model and to explore empirical results of the effect of “the information of investor sentiments on stock returns” and the effect of “the information of firm party tendency on bank loan contracts”. First, this study adopts distributed web crawler to largely crawl text from network news and uses text mining to establish positive and negative sentiment variables to analyze the impacts of investor sentiments on stock returns. Specifically, the effects of investors’ optimistic and pessimistic sentiments on individual stocks’ returns and their volatility are explored. Meanwhile, the impacts of the investors’ large sentiments on stock returns are further investigated. Second, this study uses the linguistic text mining method to construct two time-varying PC indexes to measure the strength of a firm’s political tendencies toward the ruling and opposite parties. Specifically, whether the varying PC firms connected to the ruling party obtain more preferential loan terms than those connected to the opposition party are examined. Moreover, whether the fixed PC firms and lower loyal firms of supporting two parties obtain fewer financial rewards in terms of loan terms than the varying PC firms are investigated. Furthermore, whether the PC effect with varying political tendencies for government-owned banks is stronger than for privately-owned banks is examined.
With regard to the effects of investors’ sentiments on stock returns, this study first finds that the stock returns of the companies which have investors’ optimism are evidently higher in that month, but those which have pessimism turn to evidently contrary directions. Then, investors’ optimism evidently promotes stock volatility close to a quarter, whereas their pessimism turns to the contrary directions. Moreover, investors’ largely optimistic sentiments more evidently and persistently promotes stock volatility than their general optimistic sentiments, whereas the negative impacts of their largely pessimistic sentiments on volatility turn to be smaller and the persistence is shorter compared with general pessimistic sentiments.
In terms of the impact of firm’s party tendencies on loan contracts, this study first finds that firms are more likely to increase their PC tendencies toward the expected winning party during election years. By contrast, firms that support the opposition party show or hide their PC tendencies over the years. The PC firms connected to the ruling party have lower loan rates and preferential non-rate loan terms, whereas those associated to the opposition party do not. Fixed PC firms connected to the ruling party do not always gain benefits on loan terms, whereas fixed PC firms connected to the opposition party may even receive preferential loan contracts. Moreover, PC firms connected to the two parties do not receive preferential loan contracts. Finally, the PC effect with varying political tendencies is stronger for government-owned than privately-owned banks.

Table of Contents Page 摘要 I Abstract III Acknowledgement V Table of Contents VII List of Figures X List of Tables XI 1. Introduction 1 1.1 Background and motivation 1 1.1.1 To explore the impacts of investor sentiment on stock returns by using the fintech approach 1 1.1.2 To explore the impacts of firms’ party tendency on loan contracts by using the fintech approach 3 1.2 Research goals 5 1.2.1 To explore the impacts of investor sentiment on stock returns by using fintech approach 5 1.2.2 To explore the impactws of firms’ party tendency on loan contracts by using the fintech approach 5 1.3 Summary of content and plan of this study 6 2. Literature Review 9 2.1 Introduction to Fintech 9 2.2 Traditional studies to explore the effects of investor sentiments on returns 11 2.3 Studies using text mining to explore the effects of investor sentiments on stock returns 12 2.4 Definition of PC and Fixed PC firms and loan contracts 14 2.4.1 Definition of political connection and political background in Taiwan 14 2.4.2 Fixed PC firms and loan contracts 14 2.5 PC firms and loan contracts during presidential election & for GOB and POB 15 2.5.1 PC firms and loan contracts during presidential election 15 2.5.2 PC firms and loan contracts for GOB and POB 16 3. Methodology 17 3.1 Constructions of the Information Variables 17 3.1.1 Construction of investor sentiment tendencies 17 3.1.2 Construction of firm’s political party tendencies 20 3.2 Econometric Method 24 3.2.1 Regression results – the impacts of investor sentiment on stock returns 24 3.2.2 Regression model – the effect of firm’s party tendency on loan contracts 26 3.3 Data Scope and Source 28 3.3.1 The effect of investor sentiment on stock returns 28 3.3.2 The effect of firm’s party tendency on loan contracts 29 4. Empirical Results 30 4.1 The Effect of Investor Sentiment on Stock Returns 30 4.1.1 The scores for optimistic and pessimistic texts originally crawled in news, these texts and their weights 30 4.1.2 Representative optimistic and pessimistic texts and their weights 31 4.1.4 The analysis of descriptive statistics 34 4.1.5 Regression results 34 4.2 The effect of firm’s party tendency on loan contracts 42 4.2.1 The representative characteristic texts and their weights 42 4.2.2 Basic statistics 43 4.2.3 Regression results 50 5. Conclusion 61 5.1 The advantages of fintech approaches 61 5.2 The conclusion to the empirical results 61 5.3 The contributions 63 5.4 Limitations 64 5.5 Future Researches 64 Reference 64

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