臺灣博碩士論文加值系統

本研究採用Enders & Granger(1998) and Enders & Siklos(2001)所詳細說明的門檻自迴歸(TAR)和門檻誤差修正模型(TECM），探討在疫情2020年1月至2023年6月期間股價波動與成交量以及股價波動與報酬率之間的因果關係。研究結果說明報酬率和股價波動及成交量與股價波動之間有非對稱之門檻共整合關係，指出成交量與股價波動及報酬率與股價波動之間具有長期因果關係，在門檻誤差修正模型測試中發現，股價波動與成交量之間存在雙向的短期因果關係，而股價波動與報酬率之間僅具單向的股價波動領先報酬率之短期因果關係。此外，股價波動與成交量之間不僅具有雙向長期均衡關係，且具有長期共整合關係，而僅在股價波動與報酬率之間雖具有長期均衡關係，但報酬率與股價波動之間不具備長期共整合關係。

In this study, the threshold autoregressive model (TAR) and threshold error correction model (TECM) detailed by Enders & Granger (1998) and Enders & Siklos (2001) are used to explore the causal relationship between stock price fluctuation and trading volume and between stock price fluctuation and return rate during the epidemic period from January 2020 to June 2023. The results show that there is an asymmetric threshold co-integration relationship between the return rate and volume and the stock price fluctuation, and it is pointed out that there is a long-term causal relationship between the stock price fluctuation and volume and between the stock price fluctuation and the return rate. In the threshold error correction model test, it is found that there is a two-way short-term causal relationship between the stock price fluctuation and volume, but only the stock price fluctuation has a one-way short-term causal relationship with the return rate. In addition, there is not only a two-way long-term equilibrium relationship between stock price fluctuation and trading volume, but also a long-term co-integration relationship, only when stock price fluctuation affects return rate, and there is no long-term co-integration relationship between return rate and stock price fluctuation.

第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 4
第三節 研究目的 5
第四節 研究架構 6
第二章 文獻探討 8
第一節 影響股價波動的外在因素 8
第二節 股價與交易量相關文獻 9
第三節 報酬率與成交量相關文獻 11
第四節 股價波動相關論文 13
第三章 研究方法 15
第一節 單根檢定 15
第二節 門檻共整合檢定 19
第三節 動能門檻誤差修正模型 21
第四章 實證結果與分析 24
第一節 研究資料來源 24
第二節 敘述統計量 24
第三節 單根檢定 27
第四節 門檻共整合檢定 29
第五節 門檻誤差修正模型 32
第五章 結論與建議 38
第一節 結論 38
第二節 建議 39
參考文獻 40

表次
表1變數之敘述統計 25
表2 PP單根檢定 27
表3 NP檢定 28
表4 KPSS檢定 28
表5成交量與股價波動模型門檻共整合檢定 29
表6報酬率與股價波動門檻共整合模型檢定 31
表7股價波動與成交量門檻誤差修正模型檢定 34
表8股價波動與報酬率模型門檻誤差修正模型 36
表9門檻誤差修正模型綜合比較 37

圖次
圖 1 研究架構流程圖 7
圖 2 元大台灣50股價與成交量之時間趨勢圖 26
圖 3 元大台灣50股價與報酬率之時間趨勢圖 26

一、中文參考文獻
1.胡佑姵、張巧玟、黃永成 (2022)，「交易量爆增後股價報酬之研究大數據與R程式語言之應用」，華人經濟研究，第21卷，第1期 ，頁69-89.
2.王明昌、許婉琪、李飛涵、柯建全 (2022)，「市場恐慌情緒對台股新聞
3.事件之股價反應的影響」，管理與系統 ，第29卷，第2期，頁147-186 。
4.陳央玫 (2022)，「COVID-19疫情對股價報酬率的影響：以台灣海運類股為 例」，國立中正大學經濟所碩士論文。
5.徐合成 (1994)，「台灣股市股票報酬率與交易量關係之實證研究--GARCH 模型之應用」，國立臺灣大學財務金融研究所碩士論文。
6.劉永欽 (1996)，「臺灣股票市場價量之線性與非線性Granger因果關係之研究」，國立交通大學管理科學研究所碩士論文。
7.劉映興、陳家彬 (2002)，「臺灣股票市場交易值、成交量與發行量加權股價指數關係之實證研究－光譜分析之應用」，農業經濟半年刊，第72期，頁65-87。
8.姚蕙芸、聶建中 (2003)，「空頭走勢期間台灣股票市場成交量與股價之關聯 性研究」，臺北商技學報，第4期，頁2-25。
9.姚蕙芸、梁志民 (2005)，「空頭與多頭走勢期間台股股價與相關因素因果關 係探討－以2000及2003年為例」，企業管理學報，第66期，頁1-39。
10.蕭幸金 (1993)，「股價與成交量相依程度之探討-臺灣股市實證分析」，國立政治大學會計研究所碩士論文。
11.鄭婉秀、李命志、邱哲修、李英菖 (2004)，「利率價差對國際匯價走勢之影響」，朝陽商管評論，第三卷，第1期，頁55-76。
12.蔡松蒝 (2009)，「數股票型基金(ETF)之動能投資策略-以台灣50ETF為例」，國立成功大學財務金融研究所碩士論文。
13.陳秀慧 (2022)，「法人買賣超與股價報酬率關聯性之研究」，輔仁大學金融與國際企業學系金融碩士在職專班。
14.高儷珊 (2013)，「美國量化寬鬆貨幣政策對G2股匯市非線性因果關係探討」，淡江大學財務金融學所碩士論文。
15.廖麗淑 (2018) ，「淨值市價比、益本比、現金流市價比與股價之非線性因果關係之研究，以lululemon為例」，國立台北大學國際財務金融所碩士在職專班論文。


二、英文參考文獻
1.Bagh, T.,Khan, M.,Fenyves, V., and Olah, J. (2023), “Asymmetric Effect of Investors Sentiments on Herding Behavior and Stock Returns: Pre and Post Covid-19 Analysis,” Montenegrin Journal of Economics, Vol.19, No.1 , pp. 43-55.
2.Bannigidaamath, D., Narayan, P.K., Phan, D.H.B., Gong, Q. (2021) , “How Stock Markets Reacted to COVID-19? Evidence from 25 countries,” Finance Research Letters, Vol.45, No.1, pp1-12.
3.Bonga, W., Chimwai, L., Siyakiya, P., and Choga, I., (2022), “Stock Market Volatility In Zimbabwe Stock Exchange During Pandemic Period,” Eurasian Journal of Economcs and Finance,Vol. 10, No2. , pp. 68-82.
4.Bouri, E., Muhammad A.,Safwan M., Mbarki, I., and Saeed, T. (2022), “Government Responses to COVID-19 and Industry Stock Returns,” Ekonomska Istrazivanja , Vol.35, No.1 , pp. 1967-1990.
5.Cevik,E.,Kirci Kirci Altinkeski, B.,Cevik, E., and Dibooglu, S. (2022), “Investor Sentiments and Stock Markets During the COVID 19 Pandemic,” Financial Innovation , Vol. 8 , No. 1 , pp. 69.
6.Chna, K., S. (1993), “Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model,” Annals of Statistics, Vol.21, No.1 , pp.520-533.
7.Chundakkadan, R., and Nedumparambil, E. (2022), “ In Search of COVID-19 and Stock Market Behavior,” Global Finance Journal, Vol. 54, No.1, pp. 1-10.
8.Ciner, C. (2002), “The Stock Price-Volume Linkage on the Toronto Stock Exchange：Before and After Automation,” Review of Quantitative Finance and Accounting, Vol.19 , No.4 , pp. 335-349.
9.Enders, W. and Granger, C. W., (1998), “Unit-Root Rests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates,” Journal of Business & Economic Statistics, Vol. 16, No. 3, pp. 304-311.
10.Enders, W. and Siklos, P. L., (2001), “Cointegration and Threshold Adjustment,” Journal of Business & Economic Statistics, Vol. 19, No. 2, pp. 166-176.
11.Engle, R. F. and Granger, C. W., (1987), “Co-integration and Error Correction: Representation, Estimation, and Testing,” Econometrica, Vol. 55, No. 2, pp. 251-276.
12.French K.R., Schwert G.W., and Stambaugh R.F. (1987), “Expected Stock Retuns and Volatility,” Journal of Financial Economics, Vol.19 , No.1, pp. 3-29.
13.Granger, C. W. J. (1969), “Investigating Causal Relations by Economic Models and Cross-Spectral Methods,” Journal of Economeicatr, Vol.37, No. 3, p424-438.
14.Granger, C. W. J. (1969) , “Some Properties of Time Series Data and Their Use in Econometrics Mode; Specification,” Journal of Economrtrica, Vol.37, No. 3, pp. 424-438.
15.Granger, C. W. (1981) , “Some properties of time series data and their use in econometric model specification,” Journal of econometrics, Vol.16, No.1, pp.121-130.
16.Gu, G.,Zhu, W.,Wang, C., and Ekonomska I. (2022) , “Time-Varying Influence of Interest Rates on Stock Returns: Evidence From China,” Znanstveno-Strucni Casopis, Vol.35, No.1 , pp. 2510-2529.
17.Jung, W. and Kang, M. (2021) , “The Short-Term Mean Reversion of Stock Price and the Change in Trading Volume,” Journal of Derivatives and Quantitative Studies, Vol.29, No.3 , pp. 190-214.
18.Kilic, A., Veysel, K. U. L. A., and ÖZDEMIR, L. (2023) , “The Relationship Between Exchange Rate Volatility and Stock Index Return: Evidence Form Turkey,” Prizren Social Science Journal,Vol. 7, No.1 , pp. 1-13.
19.Kudryavtsev, A. (2020) , “Immediate and Longer-Term Stock Price Dynamics Following Large Stock Price Changes,” Annals of Financial Economics,Vol. 15, No.1, pp.1-17.
20.Kumari, J. (2022) , “Does Liquidity Predicts the Stock Price Volatility: Evidence from the Asia Pacific Markets,” Journal of Management Research, Vol.22, No.2, pp. 73-100.
21.Kwiatkowski, D., Phillips , P. C., Schmidt, P., and Shin, Y. (1992), “ Testing the Nullhypothesis of Stationarity Against the Alternative of a Unit Root: How Sure are we that Economic Time Series Have a Unit Root ?, ” Journal of Econometrics, Vol.54, No. 1, pp. 159-178.
22.Lim, J. J., Kew, S. R., and Tee, L. T. (2023),  “Covid-19 Impact on Stock Returns of Travel, Leisure and Hospitality Sectors In Malaysia,” Labuan Bulletin of International Business and Finance,Vol. 21, No.1, pp. 1-8.
23.Lyocsa, S., Baumohl, E., Vyrost, T. and Molnar, P. (2020) , “Fear of the Coronavirus and the Stock Market,” Finance Research Letters, Vol.36, pp. 1-7.
24.Ng, S., and Perron, P. (1996) ,“ The exact error in estimating the spectral density at the Origin,” Journal of Time Series Analysis, ” Vol.17, No.4, pp.379-408.
25.Ng, S., and Perron, P. (2001) , “Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power,” Econometrica , Vol.69, No.6, pp. 1519-1554.
26.Ofori-Boateng, K., Ohemeng, W., Agyapong, E. K., and Bribinti, B. J. (2022) ,  “The Impact of COVID-19 on Stock Returns of Listed Firms on the Stock Market: Ghana's Experience,” African Journal of Economic and Management Studies, Vol.13, No.1, pp. 136-146.
27.Phillips, P. C., and Perron, P. (1988) , “ Testing for a Unit Root in Time Series Regression,” Biometrika , Vol.75, No.2, pp. 335-346.
28.Rowland, R., Chia, R. C. J., and Liew, V. K. S. (2023), “Do Non-Pharmaceutical Policies in Response to COVID-19 Affect Stock Performance? Evidence From Malaysia Stock Market Return and Volatility,” PLoS One, Vol. 18, No.1, pp. 1-21.
29.Shah, S. A., Raza, H., and Mustafa Hashmi, A. (2022) , “Downside Risk-Return Volatilities During Covid 19 Outbreak: a Comparison Across Developed and Emerging Markets,”Environmental Science and Pollution Research ,Vol.29, No.46, pp. 70179-70191.
30.Spiegel, M. I., and Wang, X. (2005) , “Cross-Sectional Variation in Stock Returns: Liquidity and Idiosyncratic Risk,” Yale ICF Working Paper No. 5-13, pp.1-51.
31.Zhussipova, E. Y., Ydyrys, S. S., Makhanbetova, U., Tayauova, G. and Pirmanova, Z. (2023) , “The Relationship Between the Highest Prices and Trading Volume in the Share Indices of Energy and Oil Companies in Kazakhstan,” International Journal of Energy Economics and Policy , Vol.13 , No. 3, pp. 28-35.
32.Ziadat, S. A., and AlKhouri, R. (2022),“Revisiting Volatility Spillovers in the Gulf Cooperation Council,” Cogent Economics & Finance, Vol.10 , No.1 , pp.1-21.