情緒指數能否預測石油期貨價格之變化

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葉芷君(Chih-Chun Yeh) 查詢紙本館藏

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財務金融學系

論文名稱

情緒指數能否預測石油期貨價格之變化
(Can Sentiment Index Predict Changes in Oil Futures Prices?)

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至系統瀏覽論文 (2025-7-31以後開放)

摘要(中)

本研究主要係探討情緒指數是否能夠預測石油期貨價格的變化，著重於西德州和布蘭特原油期貨價格之變化方面，研究期間為1994年至2019年，研究分別使用月頻率、週頻率和日頻率等十一種情緒指數來進行預測，並比較情緒指數間預測能力之高低，研究過程中採用標準預測迴歸模型、格蘭傑因果關係檢定及Garch-in-Mean模型加以驗證，實證結果發現部分情緒指數確實可以有效預測石油期貨報酬。再者，藉由Garch-in-Mean檢驗結果，顯示專業投資人情緒指數 - CBSI情緒指數對西德州原油期貨報酬具有聰明錢效應 (smart money effect)；此外，該指數也對於兩種原油期貨報酬具有相當的預測能力，希冀透過本研究能增進投資人對於不同情緒指數的認識，進而能有效利用情緒指數去判斷石油期貨市場中其價格的變化。

關鍵字: 情緒指數、CBSI、石油期貨、格蘭傑因果檢定、Garch-in-Mean

摘要(英)

This thesis explores whether the sentiment index can forecast the changes of oil futures prices, focusing on studying changes in the West Texas Intermediate and Brent crude oil futures prices. The sample period is from 1994 to 2019, using eleven types of monthly-, weekly- and daily-frequency sentiment indexes. We use each individual sentiment index to make prediction and compare the predictive power among them. The research methods include the standard predictive regression model, Granger causality test and Garch-in-Mean model. The empirical results show that some of the sentiment indexes effectively predict oil futures returns. Furthermore, the result in the Garch-in-Mean test also shows that the professional investor sentiment index - CBSI sentiment index has a smart money effect on the West Texas Intermediate crude oil futures returns. In addition, this index also has a considerable predictive ability. Through this study, investors can understand the efficacy between each sentiment index, and also use these sentiment indexes to judge the changes in oil futures prices when investing in the oil futures markets.

Keywords: Sentiment Index, CBSI, Oil Futures, Granger Causality Test, Garch-in-Mean

關鍵字(中)

★ 情緒指數
★ CBSI
★ 石油期貨
★ 格蘭傑因果檢定
★ Garch-in-Mean

關鍵字(英)

★ Sentiment Index
★ CBSI
★ Oil Futures
★ Granger Causality Test
★ Garch-in-Mean

論文目次

目錄
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 v
第一章緒論 1
第一節研究背景與動機 1
第二節研究目的 2
第三節論文研究架構與流程 3
第二章文獻回顧 5
第一節情緒指數之重要性 5
第二節股票市場與投資人情緒之關聯性 7
第三節石油價格分析與預測 8
第四節石油市場與投資人情緒之關聯性 10
第五節 CBSI情緒指數 (Consensus Bullish Sentiment Index) 11
第三章研究方法 13
第一節研究對象與樣本選取 13
第二節研究變數選取與定義 14
第三節研究模型建立 18
第四章實證結果與分析 21
第一節敘述統計量與相關性分析 21
第二節樣本內及樣本外預測分析 23
第三節格蘭傑因果檢定 (Granger Causality Test) 27
第四節 Garch-in-Mean模型 29
第五章結論與建議 31
第一節結論 31
第二節研究限制與建議 32
參考文獻 33
附錄 38

表目錄
表1 變數對照表 38
表2 月頻率資料之敘述統計量 39
表3 週頻率與日頻率資料之敘述統計量 40
表4 月頻率資料之相關係數 41
表5 週頻率與日頻率資料之相關係數 42
表6 西德州原油期貨報酬之樣本內預測分析結果 43
表6-1同頻率情緒指數間樣本內預測分析結果之比較 (西德州原油期貨報酬) 44
表7 布蘭特原油期貨報酬之樣本內預測分析結果 45
表7-1同頻率情緒指數間樣本內預測分析結果之比較 (布蘭特原油期貨報酬) 46
表8 西德州原油期貨報酬之樣本外預測分析結果 47
表8-1同頻率情緒指數間樣本外預測分析結果之比較 (西德州原油期貨報酬) 47
表9 布蘭特原油期貨報酬之樣本外預測分析結果 48
表9-1同頻率情緒指數間樣本外預測分析結果之比較 (布蘭特原油期貨報酬) 48
表10 西德州原油期貨報酬與情緒指數間之格蘭傑因果檢定 49
表11 布蘭特原油期貨報酬與情緒指數間之格蘭傑因果檢定 50
表12 西德州原油期貨報酬之條件波動度和情緒指數：Garch-in-Mean模型 51
表13 布蘭特原油期貨報酬之條件波動度和情緒指數：Garch-in-Mean模型 52

參考文獻

Abbas, A. E., Bakır, N. O., Klutke, G. A., & Sun, Z. (2013). Effects of risk aversion on the value of information in two-action decision problems. Decision Analysis, 10(3), 257-275.
Alquist, R., & Kilian, L. (2010). What do we learn from the price of crude oil futures?. Journal of Applied Econometrics, 25(4), 539-573.
Baker, M., & Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271-299.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. Journal of Finance, 61(4), 1645-1680.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-152.
Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104(2), 272-287.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 1593-1636.
Baumeister, C., Guérin, P., & Kilian, L. (2015). Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting, 31(2), 238-252.
Bernanke, B.S (2004). Oil and the Economy. Speech presented at Darton College, Albany, Georgia.
Borovkova, S. (2011). News analytics for energy futures. Available at SSRN 1719582.
Campbell, J. Y., & Thompson, S. B. (2008). Predicting excess stock returns out of sample: Can anything beat the historical average?. Review of Financial Studies, 21(4), 1509-1531.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383-403.
Chinn, M. D., & Coibion, O. (2014). The predictive content of commodity futures. Journal of Futures Markets, 34(7), 607-636.
Chou, P. H., Hsieh, C. H., & Shen, C. H. H. (2016). What explains the orange juice puzzle: Sentiment, smart money, or fundamentals?. Journal of Financial Markets, 29, 47-65.
Coppola, A. (2008). Forecasting oil price movements: Exploiting the information in the futures market. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 28(1), 34-56.
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738.
Deeney, P., Cummins, M., Dowling, M., & Bermingham, A. (2015). Sentiment in oil markets. International Review of Financial Analysis, 39, 179-185.
Dowling, M., Cummins, M., & Lucey, B. M. (2016). Psychological barriers in oil futures markets. Energy Economics, 53, 293-304.
Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica, 55(2), 391-407.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Fisher, K. L., & Statman, M. (2003). Consumer confidence and stock returns. Journal of Portfolio Management, 30(1), 115-127.
Gramlich, E. M. (2004). Oil shocks and monetary policy, Annual Economic Luncheon. Federal Reserve Bank of Kansas City.
Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.
Greenspan, A. (2004). Oil. Speech presented at the National Italian American Foundation, Washington, DC.
Hamilton, J. D. (2009). Understanding crude oil prices. Energy Journal, 30(2).
Han, B. (2008). Investor sentiment and option prices. Review of Financial Studies, 21(1), 387-414.
Han, L., Lv, Q., & Yin, L. (2017). Can investor attention predict oil prices?. Energy Economics, 66, 547-558.
Hansen, P. R., & Timmermann, A. (2012). Choice of sample split in out-of-sample forecast evaluation (No. ECO2012/10). Working Paper, University of California at San Diego.
Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. Review of Financial Studies, 28(3), 791-837.
Husain, M. A. M., & Bowman, C. (2004). Forecasting commodity prices: Futures versus judgment (No. 4-41). International Monetary Fund.
Jin, X. (2016). Does the future price help forecast the spot price?. Discussion Paper in Economics, 16(8), Department of Economics, University of Aberdeen.
Kaplanski, G., & Levy, H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics, 95(2), 174-201.
Kelly, B., & Pruitt, S. (2013). Market expectations in the cross‐section of present values. Journal of Finance, 68(5), 1721-1756.
Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3), 1053-69.
Kilian, L., & Hicks, B. (2013). Did unexpectedly strong economic growth cause the oil price shock of 2003–2008?. Journal of Forecasting, 32(5), 385-394.
Kliesen, K., & Smith, D. C. (2010). Measuring financial market stress. Economic Synopses, 2.
Laborda, R., & Olmo, J. (2014). Investor sentiment and bond risk premia. Journal of Financial Markets, 18, 206-233.
Lemieux, J., & Peterson, R. A. (2011). Purchase deadline as a moderator of the effects of price uncertainty on search duration. Journal of Economic Psychology, 32(1), 33-44.
Lemmon, M., & Portniaguina, E. (2006). Consumer confidence and asset prices: Some empirical evidence. Review of Financial Studies, 19(4), 1499-1529.
Mian, G. M., & Sankaraguruswamy, S. (2012). Investor sentiment and stock market response to earnings news. Accounting Review, 87(4), 1357-1384.
Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. Journal of Finance, 32(4), 1151-1168.
Narayan, P. K., Sharma, S., Poon, W. C., & Westerlund, J. (2014). Do oil prices predict economic growth? New global evidence. Energy Economics, 41, 137-146.
Nartea, G. V., Bai, H., & Wu, J. (2019). Investor sentiment and the economic policy uncertainty premium (No.19/14). Working Paper, University of Canterbury, Department of Economics and Finance.
Newey, W. and West, K. (1987). A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703-708.
Qadan, M., & Nama, H. (2018). Investor sentiment and the price of oil. Energy Economics, 69, 42-58.
Qadan, M., & Yagil, J. (2012). Fear sentiments and gold price: testing causality in-mean and in-variance. Applied Economics Letters, 19(4), 363-366.
Reeve, T. A., & Vigfusson, R. J. (2011). Evaluating the forecasting performance of commodity futures prices. FRB International Finance Discussion Paper, (1025).
Reichsfeld, D. A., & Roache, S. K. (2011). Do commodity futures help forecast spot prices? (No. 11/254). International Monetary Fund.
Rossi, B., & Inoue, A. (2012). Out-of-sample forecast tests robust to the choice of window size. Journal of Business & Economic Statistics, 30(3), 432-453.
Rossi, B., Sekhposyan, T., & Soupre, M. (2016). Understanding the Sources of Macroeconomic Uncertainty. Available at SSRN 2780213.
Sanders, D. R., Irwin, S. H., & Leuthold, R. M. (1997). Noise Traders, Market Sentiment, and Futures Price Behavior (No. 9707001). University Library of Munich, Germany.
Sanders, D. R., Irwin, S. H., & Leuthold, R. M. (2003). The theory of contrary opinion: a test using sentiment indices in futures markets. Journal of Agribusiness, 21(345-2016-15207), 39-64.
Schmeling, M. (2007). Institutional and individual sentiment: Smart money and noise trader risk?. International Journal of Forecasting, 23(1), 127-145.
Shiller, R. J., Fischer, S., & Friedman, B. M. (1984). Stock prices and social dynamics. Brookings Papers on Economic Activity, 1984(2), 457-510.
Sims, C. A. (1972). Money, income, and causality. American Economic Review, 62(4), 540-552.
Stambaugh, R. F., Yu, J., & Yuan, Y. (2012). The short of it: Investor sentiment and anomalies. Journal of Financial Economics, 104(2), 288-302.
Van Roye, B. (2011). Financial stress and economic activity in Germany and the Euro Area (No. 1743). Kiel Institute for the World Economy (IfW).
Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17-35.
Wang, C. (2001). Investor sentiment and return predictability in agricultural futures markets. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 21(10), 929-952.
Welch, I., & Goyal, A. (2008). A comprehensive look at the empirical performance of equity premium prediction. Review of Financial Studies, 21(4), 1455-1508.
Westerlund, J., & Narayan, P. (2015). Testing for predictability in conditionally heteroskedastic stock returns. Journal of Financial Econometrics, 13(2), 342-375.
Westerlund, J., & Narayan, P. K. (2012). Does the choice of estimator matter when forecasting returns?. Journal of Banking & Finance, 36(9), 2632-2640.
李永隆, 杜玉振, & 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(4), 565-590.
李見發, 洪振義, & 林益倍. (2012). 國際原油價格上漲對台灣產業生產成本與物價水準的影響. 應用經濟論叢, (92), 163-197.
周賓凰, 張宇志, & 林美珍. (2019). 投資人情緒與股票報酬互動關係. 證券市場發展季刊:行為財務學特別專刊, 153-190.
孫佩君. (2014). 情緒指標對黃金期貨價格的影響. 中央大學財務金融學系碩士在職專班學位論文, 1-37.

指導教授

高櫻芬葉錦徽(Yin-Feng Gau Jin-Huei Yeh)

審核日期

2020-7-15

推文