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研究生:劉重鑫
研究生(外文):Chung-Hsin Liu
論文名稱(外文):The Impact of Extreme Rainfall on the Market Value and Investment of Listed Companies in Taiwan
指導教授:張傳章張傳章引用關係
學位類別:碩士
校院名稱:國立中央大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:48
中文關鍵詞:氣候變遷極端降雨公司價值投資策略台灣上市公司
外文關鍵詞:Climate ChangeExtreme RainfallFirm ValueInvestment StrategyListed Company in Taiwan
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近年來,氣候變遷給帶來的許多挑戰,尤其是極端降雨颱風和梅雨季的影響。由於台
灣特殊的地理位置關係,容易受到來自太平洋的颱風影響。每年的颱風季節,通常在
夏季和初秋(七月至十月),會帶來強風和豪雨,對農業、漁業、基礎設施和人類安全
構成重大威脅,這樣的氣候狀況對於各行各業來說都是一個很大的挑戰。
由於過往氣候變遷或降雨的文獻大多是與土木工程或水文有關,少有文獻在探討極
端氣候對於公司價值及投資策略的影響。本研究旨在了解當企業面臨極端降雨情況時,
公司價值和投資行為是否會受到影響。通過分析歷史降雨數據和台灣上市公司的財務
信息,研究不同投資策略在應對各種極端降雨情況下所產生的價值。觀察這些公司的
市場價值和投資方式,以確定極端降雨是否會影響公司價值,以及是否會導致其投資
活動的變化。最後也會分析在面對降雨後,公司作出正確的策略決策是否會增加公司
價值。
從研究結果發現,企業會根據降雨模式採取不同的投資措施。當面臨過量降雨時,
企業會增加投資。相反,在降雨不足的情況下,企業會減少或維持原本的投資。最終,
當公司做出正確的投資決策時,其價值顯著增加。
這篇研究為極端降雨的氣候變遷提供了初步的研究方向,期望能為台灣產業在應對
氣候變遷時做出一份貢獻。
Taiwan has faced many challenges from climate change in recent years, particularly the
impacts of extreme weather events such as typhoons and the rainy (plum rain) season.
Taiwan’s geographical location makes it susceptible to typhoons from the Pacific Ocean.
Each typhoon season, typically in summer and early autumn (from July to October),
brings strong winds and heavy rain, posing significant threats to agriculture, fisheries,
infrastructure, and human safety, posing challenges for every industry in Taiwan.
This study aims to understand whether company value and investment behavior are
affected when firms face extreme rainfall by analyzing historical rainfall data and the
financial information of publicly listed companies in Taiwan. It will follow differential
investment strategies to generate value in response to diverse extreme rainfall conditions.
This study will observe these firms’ market valuations and investment patterns to deter-
mine if extreme rainfall influences company value and whether it leads to changes in their
investment activities. The study will also assess whether making the correct strategic
decisions in response to rains increases company value.
The results found that companies adopt various hedging measures in response to rain-
fall patterns. When facing excess rainfall-departure, companies will increase investments
to improve infrastructure and disaster prevention equipment to ensure business continu-
ity and reduce losses. In contrast, during deficit rainfall-departure, companies will reduce
unnecessary investments and focus resources on drought-resistant measures and water re-
source management. Finally, when a company makes the right investment decisions, its
value increases significantly.
Contents
1 Introduction 1
2 Literature Review and Hypothesis 4
2.1 Immediate effects on market valuations due to rainfall departures . . . . . 4
2.2 Investments after Rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.3 Investments strategies, Firm value after rainfall . . . . . . . . . . . . . . . 7
3 Empirical Data & Methodology 9
3.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Baseline Rainfall & Rainfall-Departure . . . . . . . . . . . . . . . . . . . . 9
3.3 Treated and Control firms . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.4 Measurement of Investment, Firm value . . . . . . . . . . . . . . . . . . . 13
3.5 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.6 Descriptive Statistics of Data . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Empirical Result 18
4.1 Immediate impact on firm value . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Investment after Rainfall-departure . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Investment and firm value . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.5.1 Location issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.5.2 Another Measurements of Investment . . . . . . . . . . . . . . . . . 26
4.5.3 Sequential one-year pair observations . . . . . . . . . . . . . . . . . 27
4.5.4 Industry Robustness Check . . . . . . . . . . . . . . . . . . . . . . 31
5 Conclusion 32
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