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研究生:黃筱芸
研究生(外文):HUANG, HSIAO-YUN
論文名稱:過去長期石油價格變動率對產業報酬率的預測
論文名稱(外文):The Predictive Power of Past Long-term Oil Price Changes in Industrial Returns
指導教授:吳淑貞吳淑貞引用關係
指導教授(外文):WU, SHU-JHEN
口試委員:蔡佳芬蔡豐澤張榮顯吳淑貞
口試委員(外文):TSAI, CHIA-FENTSAI, FENG-TSECHANG,JUNG-SIANWU, SHU-JHEN
口試日期:2024-06-17
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:國際企業學系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:46
中文關鍵詞:石油變動率股票報酬率產業
外文關鍵詞:Oil Price ChangesStock ReturnsIndustrial
相關次數:
  • 被引用被引用:0
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
本文探討過去長期石油價格變動率,是否能預測產業報酬率?以布蘭特石油及美國十七大產業類股,作為研究對象,並使用OLS模型進行估計。研究證實,過去長期油價變動率,比起過去短期的油價變動率更具預測力,且會因為產業對原油依賴程度的高低,而在預測表現上有差異。
此外,本研究在強韌性檢測方面,考慮西德州原油變數的檢驗、子樣本分析及加入總體經濟變數,先確認過去長期油價變動率預測產業報酬率的穩定程度,再檢測模型是否會因爲其他因素變動而影響顯著性。結果顯示,即使加入的變數含有預測報酬率之資訊,仍不影響油價變動率對產業報酬率顯著且強烈的影響。
This paper investigates whether past long-term oil price changes can predict industry returns. The study focuses on Brent crude oil and the seventeen major industry sectors in the United States, using an OLS model for estimation. The research confirms that past long-term oil price changes have stronger predictive power compared to past short-term oil price changes, and the predictive performance varies depending on the industry's dependence on crude oil.
Additionally, in terms of robustness checks, the study considers the inclusion of West Texas Intermediate (WTI) crude oil variables, sub-sample analysis, and the incorporation of macroeconomic variables to assess the stability of past long-term oil price changes in predicting industry returns. It then examines whether the model's significance is affected by other factors. The results show that even when variables containing information about expected returns are included, the significant and strong impact of oil price changes on industry returns remains unaffected.
目次
致謝辭 i
摘要 ii
Abstract iii
目次 iv
表目次 vi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與架構 2
第二章 文獻回顧 3
第一節 原油價格變動率 3
第二節 原油價格變動率預測股票報酬率 3
第三節 產業類股報酬率 4
第四節 總體經濟變數 5
第三章 實證模型 7
第一節 研究方法 7
第二節 研究模型 7
第四章 實證結果 8
第一節 資料說明 8
第二節 敘述統計及單根檢定 9
第三節 實證結果 10
第五章 強韌性檢測 20
第一節 WTI原油變數 20
第二節 子樣本分析 23
第三節 加入總體經濟變數 28
第六章 結論與建議 33
第一節 研究結論 33
第二節 研究限制與建議 34
參考文獻 35
一、 英文部分 35
二、 網路部分 39
附錄 40
附錄一 紡織類股報酬率之實證結果 40
附錄二 金屬加工產品類股報酬率之實證結果 41
附錄三 食品類股報酬率之實證結果 42
附錄四 非必需性消費品類股報酬率之實證結果 43
附錄五 交通運輸類股報酬率之實證結果 44
附錄六 公共事業類股報酬率之實證結果 45
附錄七 其他類股報酬率之實證結果 46

表目次
表1 敘述統計及單根檢定 9
表2 汽車類股報酬率之實證結果 10
表3 化學品類股報酬率之實證結果 11
表4 營造類股報酬率之實證結果 12
表5 必需性消費品類股報酬率之實證結果 13
表6 金融類股報酬率之實證結果 14
表7 電機機械類股報酬率之實證結果 15
表8 礦業類股報酬率之實證結果 16
表9 零售類股報酬率之實證結果 17
表10 鋼鐵類股報酬率之實證結果 17
表11 石油類股報酬率之實證結果 18
表12 WTI變動率對汽車類股報酬率之實證結果 20
表13 WTI變動率對電機機械類股報酬率之實證結果 21
表14 WTI變動率對營造類股報酬率之實證結果 22
表15 BRENT變動率(24期)對汽車類股報酬率之子樣本分析 24
表16 BRENT變動率(36期)對電機機械類股報酬率之子樣本分析 24
表17 BRENT變動率(48期)對營造類股報酬率之子樣本分析 25
表18 WTI變動率(24期)對汽車類股報酬率之子樣本分析 26
表19 WTI變動率(36期)對電機機械類股報酬率之子樣本分析 26
表20 WTI變動率(48期)對營造類股報酬率之子樣本分析 27
表21 BRENT變動率加入總體經濟變數-汽車類股報酬率 28
表22 BRENT變動率加入總體經濟變數-電機機械類股報酬率 29
表23 BRENT變動率加入總體經濟變數-營造類股報酬率 29
表24 WTI變動率加入總體經濟變數-汽車類股報酬率 30
表25 WTI變動率加入總體經濟變數-電機機械類股報酬率 30
表26 WTI變動率加入總體經濟變數-營造類股報酬率 31


一、英文部分
1.Ang, Aand G. Bekaert (2007), "Stock Return Predictability: sI it There?" The Review ofFinancial Studies, 20:3, 651-707.
2.Backus, D. K. and M. J. Crucini (2000). "Oil prices and the terms of trade." Journal of international Economics 50(1): 185-213.
3.Baumeister, C., & Hamilton, J. D. (2019). Structural interpretation of vector autoregressions with incomplete identification: revisiting the role of oil supply and demand shocks. Amer. Econ. Rev., 109, 1873–1910.
4.Benou, G. (2003). Market underreaction to large stock price declines: the case of ADRs. J. Behav. Finance, 4, 21–32.
5.Campbell, J. Y. (1991). A variance decomposition for stock returns. Economics Journal, 101, 157–179.
6.Chang, K. L., 2009, Do macroeconomic Variables Have Regime-Dependent Effects on Stock Return Dynamics? Evidence from the Markov Regime Switching Model, Economic Modelling, Vol. 26, No. 6, 1283-1299.
7.Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. J. Bus., 59, 383–403.
8.Chen, S. (2010). Rising oil prices and agricultural commodity prices. Food Policy, 35(3), 215-223.
9.Chiang, E., Hughen, W. K., & Sagi, J. S. (2015). Estimating oil risk factors using information from equity and derivatives markets. J. Finance, 70, 769–804.
10.Cuñado, J., L. A. Gil-Alana and F. Pérez de Gracia (2010),"Mean Reversion in Stock Market Prices: New Evidence Based on Bull and Bear Markets," Research in International Business and Finance, 24:2, 113-122.
11.Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under and over reactions. J. Finance, 53, 1839–1885.
12.Driesprong, C., Jacobsen, B., & Maat, B. (2008). Striking oil: another puzzle? J. Financ. Econ., 89, 307–327.
13.Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stock and bonds. J. Financ. Econ., 25, 23–49.
14.Ferson, W. E. and C. R. Harvey (1993). "The risk and predictability of international equity returns." Review of financial Studies 6(3): 527-566.
15.Goyal, A., & Welch, I. (2003). Predicting the equity premium with dividend ratios. Manage. Sci., 49, 639–654.
16.Griffin, D., & Tversky, A. (1992). The weighing of evidence and the determinants of confidence. Cogn. Psychol., 24, 411–435.
17.Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. J. Polit. Econ., 91, 228–248.
18.Hamilton, J. D. (2003). What is an oil shock? J. Econometrics, 113, 363–398.
19.Hamilton, J. D., & Herrera, A. M. (2004). Oil shocks and aggregate macroeconomic behavior: the role of monetary policy. Journal of Money Credit Bank, 36,265–286.
20.Hamilton, J. D. (2011). Nonlinearities and the macroeconomic effects of oil prices. Macroeconomic Dynamics, 15, 364–378.
21.Hjalmarsson, .E (2010), "Predicting Global Stock Returns," Journal of Financial and Quantitative Analysis, 45:1, 49-80.
22.Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading and overreaction in asset markets. J. Finance, 54, 2143–2184.
23.Huang, R. D., Masulis, R. W., & Stoll, H. R. (1996). Energy shocks and financial markets. Journal Futures Markets, 16, 1–27.
24.Jiang, H., Li, S. Z., & Wang, H. (2021). Pervasive underreaction: evidence from high-frequency data. J. Financ. Econ., 141, 573–599
25.Jones, C., & Kaul, G. (1996). Oil and the stock markets. J. Finance, 51, 463–491.
26.Kilian, L. and C. Park (2009). "The impact of oil price shocks on the US stock market." International economic review 50(4): 1267-1287.
27.Kilian, L. (2009). Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. The American Economic Review, 99, 1053–1069.
28.Lettau, M., & Ludvigson, S. C. (2001). Consumption, aggregate wealth, and expected stock returns. J. Finance, 56, 815–849.
29.McQueen, G. and V. V. Roley (1993), "Stock Prices, News, and Business Conditions," TheReview ofFinancial Studies, 6:3, 683-707. 1283-1299.
30.Merton, R. C. (1973), "An Intertemporal Capital Asset Pricing Model," Econometrica, 41:5, 867-887.
31.Narayan, P., & Sharma, S. (2011). New evidence on oil price and firm returns. J. Bank. Financ., 35, 3253–3262.
32.O'Neal, E.S. (1997). Industry Momentum and Sector Mutual Funds. Financial Analysts Journal, 53(4), 37-51.
33.Poteshman, A. M. (2001). Underreaction, overreaction, and increasing misreaction to information in the options market. J. Finance, 56, 851–876.
34.Rapach, D. E., M. E. Wohar and .J Rangvid (2005), "Macro Variables and International Stock Return Predictability," International Journal of Forecasting, 21:1, 137-166.
35.Ready, R. C. (2018). Oil prices and the stock market. Review of Finance, 22, 155–176.
36.Shue-Jen Wu(2023)The role of the past long-run oil price changes in stock market. International Review of Economics and Finance,84,274–291
37.Smith, J. (2015). Energy hedging in the airline industry. The Quarterly Journal of Economics, 130(2), 821-862.
38.Van Dijk, M.A. (2011). Is Size Dead? A Review of the Size Effect in Equity Returns. Journal of Banking and Finance, 35(12), 3263-3274.
39.Wang, Y., Pan, Z., Liu, L., & Wu, C. (2019). Oil price increases and the predictability of equity premium. J. Bank. Financ., 102, 43–58.
40.Wu, S. .J and W. M. Lee (2012), "Predicting the U.S. Bear Stock Market Using the Consumption-Wealth Ratio," Economics Bulletin, 32:4, 3174-3181.
41.Wu, S. J., W. M. Lee and S. .Y You (2013), "Predicting Bear Stock Markets: International Evidence," UnpublishedWorking Paper.
42.Wu, S. J., & Lee, W. M. (2015a). Intertemporal risk-return relationships in bull and bear markets. International Review of Economics and Finance, 38, 308–325.
43.Wu, S. J., & Lee, W. M. (2015b). Predicting severe simultaneous bear stock markets using macroeconomic variables as leading indicators. Finance Res. Lett., 13, 196–204.


二、網路部分
1.FRED economic data
https://fred.stlouisfed.org/series/DCOILBRENTEU
https://fred.stlouisfed.org/series/DCOILWTICO
https://fred.stlouisfed.org/series/T10Y3M
https://fred.stlouisfed.org/series/CPIAUCSL
https://fred.stlouisfed.org/series/BAA
https://fred.stlouisfed.org/series/AAA
2.Kenneth R.French
https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

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