臺灣博碩士論文加值系統

在現今的資本市場中，投資人的行為及其對股價的影響一直以來都是學術界研究的重要議題。過去研究顯示，投資人的認知與注意力對於公司股價的影響具有顯著意義，然而，在資訊充斥的時代裡，投資人如何在有限的注意力資源中進行選擇和分配，對於股價波動的影響依然存在許多未知的空間。本文研究的目的，主要是想要試圖依網路搜尋量來解釋股價波動，並以目前從業的電池材料產業進行研究，本研究以Pchome股市2013年01月至2023年03月間所有鋰電池概念股作為研究對象，共取得18家上市櫃公司名單，主要探討在電池材料產業中，Google網路搜尋量與股價變動之間的關係。實證結果發現Google網路搜尋量與股價變動之間呈現正向關係，意即當投資人使用Google搜尋引擎頻繁搜索與公司相關的資訊時，股票市場的交易活動亦會隨之增加，而公司的被關注度越高，越能吸引投資人對其進行投資，進而影響股價波動

In today's capital market, the behavior of investors and its impact on stock prices has always been an important topic of research in the academic field. Previous studies have shown that investors' perceptions and attention significantly influence company stock prices. However, in the era of information overload, there are still many unknowns regarding how investors make choices and allocate their limited attention resources, and how this affects stock price volatility. The main objective of this study is to attempt to explain stock price volatility based on internet search volume, specifically focusing on the battery materials industry, which is currently in practice. This study focuses on the lithium battery sector within the PChome stock market from January 2013 to March 2023. A total of 18 listed and OTC companies were selected as the sample for investigation. The main objective is to explore the relationship between Google search volume and stock price movement in the battery materials industry. Empirical results indicate a positive relationship between Google search volume and stock price movement. This implies that when investors frequently search for information related to the company using the Google search engine, trading activity in the stock market also increases. Higher attention towards the company attracts more investors, influencing stock price volatility.

摘要 i
目錄 iv
一. 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
二. 文獻回顧 4
2.1 資本資產定價模型 4
2.2 有限注意力 7
2.3 網路搜尋量指數 11
2.4 研究假說 13
三. 產業介紹 14
3.1 電池的發展 14
3.2 電池材料產業的發展 16
3.3 鋰電池的結構 17
四. 研究設計 19
4.1 樣本選取與資料來源 19
4.2 變數定義與衡量 23
4.3 實證模型 25
五. 實證結果分析 27
5.1 描述統計分析 27
5.2 Google網路搜尋量對電池材料產業股價變動之影響－以CAPM分析 33
5.3 Google網路搜尋量對電池材料產業股價變動之影響－以Fama-French三因子模型分析 38
5.4 Google網路搜尋量對電池材料產業股價變動之影響－以Carhart四因子模型分析 43
六. 結論與建議 48
6.1 研究結果 48
6.2 研究限制與建議 49
參考文獻 50


圖目錄
圖 1　SVI趨勢圖 3
圖 2　效率前緣曲線與資本市場線 4
圖 3　鋰電池結構 17
圖 4　電池模組之產業結構圖 18
表目錄
表 1　PChome股市鋰電池概念股 20
表 2　電池材料產業經營項目 21
表 3　Google Trend搜尋關鍵字 22
表 4　敘述性統計量 28
表 5　Pearson相關係數分析 30
表 6 SVI平均數對風險調整後報酬之影響－12個月月報酬之CAPM分析 35
表 7 SVI最大值對風險調整後報酬之影響－12個月月報酬之CAPM分析 35
表 8 SVI平均數對風險調整後報酬之影響－24個月月報酬之CAPM分析 36
表 9 SVI最大值對風險調整後報酬之影響－24個月月報酬之CAPM分析 36
表 10 SVI平均數對風險調整後報酬之影響－36個月月報酬之CAPM分析 37
表 11 SVI最大值對風險調整後報酬之影響－36個月月報酬之CAPM分析 37
表 12 SVI平均數對風險調整後報酬之影響－12個月月報酬之三因子模型分析 40
表 13 SVI最大值對風險調整後報酬之影響－12個月月報酬之三因子模型分析 40
表 14 SVI平均數對風險調整後報酬之影響－24個月月報酬之三因子模型分析 41
表 15 SVI最大值對風險調整後報酬之影響－24個月月報酬之三因子模型分析 41
表 16 SVI平均數對風險調整後報酬之影響－36個月月報酬之三因子模型分析 42
表 17 SVI最大值對風險調整後報酬之影響－36個月月報酬之三因子模型分析 42
表 18 SVI平均數對風險調整後報酬之影響－12個月月報酬之四因子模型分析 45
表 19 SVI最大值對風險調整後報酬之影響－12個月月報酬之四因子模型分析 45
表 20 SVI平均數對風險調整後報酬之影響－24個月月報酬之四因子模型分析 46
表 21 SVI最大值對風險調整後報酬之影響－24個月月報酬之四因子模型分析 46
表 22 SVI平均數對風險調整後報酬之影響－36個月月報酬之四因子模型分析 47
表 23 SVI最大值對風險調整後報酬之影響－36個月月報酬之四因子模型分析 47

中文文獻
王瑋瑄，2016，Google搜尋量指數對臺灣股票報酬、成交量的關係研究，銘傳大學財務金融學所碩士論文。
朱柏儒，2016，搜尋量指數與台灣50成分股報酬率之研究，國立高雄科技大學金融資訊所碩士論文。
吳永富，2018，電化學工程原理，初版，五南出版社。
李永隆、林玉振、王瑋瑄，2017，“Google搜尋量指數對臺灣股票報酬與成交量之影響” ，管理與系統，24卷，4期，頁565-590。
林羿仲，2017，網路搜尋量與台股報酬之關聯性，國立雲林科技大學財務金融所碩士論文。
莊家彰、管中閔，2015, “台灣與美國股市價量關係的分量迴歸分析” ，經濟論文, 33捲，4期，頁379-404。
陳怡靜，2014，谷歌搜尋引擎與台灣股市交易活動之關聯性，國立高雄科技大學金融所理財組碩士論文。
彭啟鳴，2016，違約利差(信用指標)與產出之關係，國立政治大學經濟所碩士論文。
華南投顧，2018，“鋰電池產業的現況與發展” ，產業週報。
華南投顧，2021，“儲能產業分析”，產業週報。
潘意鈴，2009，台中市學齡前幼兒睡眠品質與學習注意力之相關研究，國立臺中教育大學幼兒教育所碩士論文。
蘇愛智，2021，油價波動對台北市不同所得層級家庭消費財成本的影響差異，佛光大學應用經濟所碩士論文。
顧廣平，2005，“單因子、三因子或四因子模式？” 證券市場發展季刊，卷17，2期，頁101-146。
 
英文文獻
Aboody, D., Lehavy, R., & Trueman, B.(2010). Limited attention and the earnings announcement returns of past stock market winners. Review of Accounting Studies, 15, 317-344.
Adachi, Y., Masuda, M., and Takeda, F.,(2017). Google seach intensity and its relationship to the returns and liquidity of Japanese starup stocks, Pacific-Basin Finance Journal, 46, 243-257.
Bajpai, S. and Sharma, A.K. (2015), “An empirical testing of capital asset pricing model in India”, Procedia - Social and Behavioral Sciences, 189, 259-265.
Bank, M., Larch M. and Peter, G., (2011). Google Search Volume and Its Influence on Liquidity and retruns of German Stocks, Financial Markets and Portfolio Management, 25(3), 239-264.
Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The review of financial studies, 21(2), 785-818.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: delayed price response or risk premium? Journal of Accounting research, 27, 1-36.
Blitz, D., Falkenstein, E. G., & Van Vliet, P. (2013). Explanations for the volatility effect: An overview based on the CAPM assumptions. The Journal of Portfolio Managemen, 40(3), 61-76.
Borup, D., & Schütte, E. C. M. (2022). In search of a job: Forecasting employment growth using Google Trends. Journal of Business & Economic Statistics, 40(1), 186-200.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Chemmanur, T., & Yan, A. (2009). Product market advertising and new equity issues. Journal of financial economics, 92(1), 40-65.
Costola, M., Iacopini, M., & Santagiustina, C. R. (2021). Google search volumes and the financial markets during the COVID-19 outbreak. Finance research letters, 42, 101884.
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The journal of finance, 66(5), 1461-1499.
DellaVigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. The journal of finance, 64(2), 709-749.
Elbannan, M. A. (2015). The capital asset pricing model: an overview of the theory. International Journal of Economics and Finance, 7(1), 216-228.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of finance, 25(2), 383-417.
Fama, E. F., & French, K. R. (1993), Common risk factors in the returns on stocks and bonds. Journal of financial economics, 33(1), 3-56.
Fama, E., Fisher, L., Jensen, M., Roll, R., (1969). The adjustment of stock price to new information. International economic review, 10(1), 1-21.
Fama, E.F. and French, K.R. (1992), The cross-section of expected stock returns. The journal of finance, 47(2), 427-465.
Fehle, F., Tsyplakov, S., & Zdorovtsov, V. (2005). Can companies influence investor behaviour through advertising? Super bowl commercials and stock returns. European Financial Management, 11(5), 625-647.
Francis, J., Pagach, D., & Stephan, J. (1992). The stock market response to earnings announcements released during trading versus nontrading periods. Journal of Accounting research, 30(2), 165-184.
Frederickson, J. R., & Zolotoy, L. (2016). Competing earnings announcements: Which announcement do investors process first? The Accounting Review, 91(2), 441-462.
French, K.R., Roll, R., (1986). Stock return variances: The arrival of information and the reaction of traders. Journal of financial economics,17(1), 5-26.
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of accounting and economics, 36(1-3), 337-386.
Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2009). Driven to distraction: Extraneous events and underreaction to earnings news. The journal of finance, 64(5), 2289-2325.
Huberman, G., & Regev, T. (2001). Contagious speculation and a cure for cancer: A nonevent that made stock prices soar. The journal of finance, 56(1), 387-396.
Jegadeesh and Titman. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. The journal of finance, 23(2), 389-416.
Joseph, K., Wintoki, M. B., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27(4), 1116-1127.
Kahneman, D. (1973). Attention and effort . Englewood Cliffs, NJ: Prentice-Hall, 1063, 218-226.
Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The journal of finance, 20(4), 587-615.
Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. Review of Economics and Statistics, 47, 13-37.
Loh, R. K. (2010). Investor inattention and the underreaction to stock recommendations. Financial management, 39(3), 1223-1252.
Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The journal of finance, 42(3), 483-510.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the econometric society, 34(4), 768-783.
Nasir, M. A., Huynh, T. L. D., Nguyen, S. P., & Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), 1-13.
Nguyen, C. P., Schinckus, C., & Nguyen, T. V. H. (2019). Google search and stock returns in emerging markets. Borsa Istanbul Review, 19(4), 288-296.
Peng, L., & Xiong, W. (2006). Investor attention, overconfidence and category learning. Journal of financial economics, 80(3), 563-602.
R (1987). A simple model of capital market equilibrium with incomplete information. J. finance. 423, 483-510.
Sapp, T., and A. Tiwari. (2004). Does stock return momentum explain the “smart money” effect? Journal of Finance 59(6), 2605-2622.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442.
Taked, F., and Wakao. T., (2014). Google search intensity and its relationship with returns and tranding volume of Japanese storks. Pacific-Basin Finance Journal, 27(4), 1-18.
Treynor, J. (1965). How to rate management of investment funds. Harvard Business Review, 43, 63-75.
Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808-1821.
Wicken, C. D.(1992).Introduction to engineering psychology and human performance In C. D. Wicken (Ed), Engineering psychology and human performace(2nd ed.), New York:Harper Collins.
Ying, Q., Kong, D., and Luo, D, (2015). Investor attention, institutional ownership, and stock return: empirical evidence from China. Emerging Markets Finance and Trade, 51(3), 672-685.
 
網路資料
工業技術研究院(2023)。催生臺灣鋰電池，創新電池技術百花齊放，取自：
https://50th.itri.org.tw/history/materials_chemicals_and_nanotechnology/10/
全球趣味資訊 iFuun(2023)。鎳鎘電池的發明人是誰？取自：
http://www.ifuun.com/a2018052213273949/
林琮祐(2015)。高效能鋁空氣電池之放電特性研究，取自：
https://scitechvista.nat.gov.tw/Article/c000003/detail?ID=bc076a00-c8e8-4456-96a2-7d0f6c067980
林唯耕(2021)。鋰離子電池雖然是目前主流電網級儲能系統，取自：
https://www.tglobalcorp.com/tw/blogs/industry-trend/lithium-battery
科技產業資訊室(2021)。鋰離子電池未來五年仍是主導電池市場，電池材料競爭將扮演要角，取自：https://iknow.stpi.narl.org.tw/Post/Read.aspx?PostID=17582
科技新報(2021)。換一種陽極材料，鋰離子電池充電速度快10倍，取自：
https://technews.tw/2021/11/17/nickel-niobate-lithium-battery/
佛光山人間衛視(2023)。研發十年！新型"離子液體電解質"提升鋰電池安全性 ，取自：https://www.youtube.com/watch?v=DePgixTLnIc 20230302