本文應用金融科技的的做法，包括網路爬蟲及語意文本探勘，來建立信息變數，以設計模型並探討“投資人情緒信息對股票報酬的影響”及“廠商政黨傾向信息對銀行貸款合約的影響”。首先、本文採用分布式架構的網路爬蟲以大幅爬網路媒體新聞的文本，並使用語意文本探勘以創造正向及負向情緒變數，藉此探討投資人情緒對股票報酬的影響。也就是說，本研究探討投資人正向及負向情緒對個股的報酬及報酬波動性的同時影響。同時，本文進一步調查投資人一般情緒和大幅情緒對股票報酬的可能差異。第二、本文使用語意文本探勘法建構兩個時間變異的政治關聯(PC)指標，以衡量廠商朝向執政及反對黨的政治傾向強度。具體而言，本文檢測和執政黨聯結的變異PC廠商是否相較和反對黨聯結的那些廠商能獲得更多偏好的貸款條件。再者，本文調查固定PC廠商及支持兩政黨的低忠誠廠商是否相較變異PC廠商獲得較少貸款條件的財務報酬。進而，本文檢測對公營銀行有變異政黨傾向的PC效應是否相較對民營銀行的該效應更為強烈。
就投資人情緒對股票報酬影響的結果，本研究首先發現投資人樂觀情緒的廠商在當月有顯著較高的股票報酬，然而投資人悲觀情緒的廠商則有顯著反向效應。投資人樂觀及悲觀情緒對股票報酬的影響後續會反轉。接續、投資人大幅悲觀情緒對股票報酬的負向影響在一季之內相較於其大幅樂觀情緒的正向影響為大。其次、投資人樂觀情緒會顯著提升股票報酬波動達接近一季，但其悲觀情緒則有相反影響。進而、投資人高的樂觀情緒相較其一般樂觀情緒會更顯著且持續地提升股票報酬波動性，但其高的悲觀情緒對波動性的負向影響相較一般悲觀情緒變為較小且持續性較短。
就廠商政黨傾向對貸款合約影響的結果，本研究首先發現在選舉年廠商更可能對預期贏的政黨增加其PC傾向。相反地、支持反對黨的廠商會逐年顯示或隱藏其PC傾向。和執政黨聯結的PC廠商有較低的貸款利率及偏好的非利率貸款條件(如較長貸款期間或較大貸款量等)，然而和反對黨相關的廠商則沒有。和執政黨聯結的固定PC廠商不總是獲得貸款條件的好處，然而和反對黨聯結的固定PC廠商可能甚至得到偏好的貸款合約。再者，和兩個政黨聯結的PC廠商不會得到偏好的貸款合約。最後，對公營銀行有變異政黨傾向的PC效應相較對民營銀行的該效應較為強烈。

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.

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