分析師營收預測與油價變動關聯性之探討

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：23

、訪客IP：18.188.252.23

姓名

詹?妃(Chin-Fei Chan) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

分析師營收預測與油價變動關聯性之探討
(The Relationship between Analysts’ Revenue Forecasts and Oil Price Fluctuations)

相關論文

★ 電動車產業之經營策略研究－以F公司為例	★ 機車製造廠於新產品開發之過程管制探討-以Y公司為例
★ 酒類消費與犯罪率之視覺化分析	★ 以均值回歸特性探討分析師個人評價產生之長短期預測不一致
★ 產業差異對於分析師目標價與推薦門檻影響之探討	★ 分析師多年期預測與其預測能力之探討
★ 探討分析師對盈餘成份認定與其對資本市場之影響	★ 探討企業未來性資產對分析師盈餘預測與推薦不一致之影響
★ 以中國新財富票選探討擇時能力對成為明星分析師之影響	★ 分析師觀點建構過度投資指標
★ 公司財務限制與企業社會責任之關聯-以現金股利及庫藏股買回為指標	★ 探討分析師資本支出預測之資訊意涵
★ 過度投資與球場因素對美國職棒關係之研究	★ 探討分析師推薦是否能區別高階經理人為過度自信之研究
★ 企業發生負面事件與其企業社會責任之關聯	★ 過度自信與資訊不對稱對CEO的債務融資偏好之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

本研究探討分析師預測是否可以領先油價，據以形成新油價預測模式。過去油價預測主要賴以供需模型、時間序列、期貨價格、總體變數以及機器學習模型來預測原油價格。而本研究以分析師營收預測來預測油價，因油價漲跌會反映在原油產業的營業收入，故營收預測會領先於油價，本研究主要以營收預測來對未來油價進行解釋，並以盈餘預測作為附加檢測，並進一步探討發生特殊事件或結構性改變，分析師對油價的預測能力是否會因此改變。研究樣本為2007年至2018年4月北美原油上游產業，以西德州原油價格作為研究標的，來檢驗分析師預測對油價是否具解釋能力。實證結果顯示，分析師營收及盈餘預測成長率對油價並無解釋能力，而分析師營收及盈餘預測修正與未來油價呈現正向關係，在金融海嘯期間，分析師預測能力顯著下降；但以頁岩油為例之技術進步，分析師預測能力顯著上升。

摘要(英)

This study discusses whether analysts can predict crude oil prices. In the previous studies, oil price forecasts were based on the supply and demand model of crude oil price. In other words, it means forecasting crude oil price fluctuations based on crude oil inventory levels. In this study, revenue is used to forecast oil prices, and the revenue of the petroleum industry is a reflection of the oil price. Therefore, this study will use revenue forecast to carry out future crude oil prices prediction and use earnings forecast as an additional test. Furthermore, we also detect the relationship between revenue forecast and oil price when specific exogenous events or structural change is happening. North American crude oil upstream firms from 2007 to 2018 and West Texas Crude Oil Price (WTI) was taken as the research samples to test whether revenue forecast can predict oil prices.
Empirical result shows that there is no relationship between analysts’ revenue & earnings forecast growth rate and future oil price. But, a positively significant relationship between analysts′ revenue & earnings forecast revision and future oil price. In the period of financial tsunami, the analysts’ forecast ability decreases. But, analysts’ forecast ability increases in technology improvement of shale oil extraction.

關鍵字(中)

★ 原油價格預測
★ 營業收入預測
★ 預測修正
★ 分析師

關鍵字(英)

★ Oil Price Forecast
★ Revenue Forecast
★ Forecast Revision
★ Analyst

論文目次

摘要 i
Abstract ii
表目錄 v
圖目錄 vi
一、緒論 1
二、文獻探討 4
2.1 油價在總體經濟所扮演的角色 4
2.2 油價預測 5
2.3 分析師對市場的預測能力 7
2.4 營收預測 8
2.5頁岩油革命 9
2.6 研究假說建立 10
三、研究方法 13
3-1 資料來源與選樣方法 13
3-2研究方法 14
3-2-1分析師預測成長率與未來累積油價報酬之關係 14
(1)營收預測成長率 14
(2)盈餘預測成長率 15
3-2-2分析師預測修正與未來累積油價報酬之關係 17
(1)營收修正 17
(2)盈餘修正 18
3-2-3特殊事件發生造成分析師預測能力改變 19
3-2-3-1金融海嘯 19
3-2-3-2頁岩油技術突破 22
3-2-3-3結構性改變 25
四、實證分析 28
4-1 分析師預測成長率與未來累積油價報酬之關係 28
4-1-1營收預測成長率 28
4-1-2盈餘預測成長率 29
4-2 分析師預測修正與未來累積油價報酬之關係 31
4-2-1營收預測修正 31
4-2-2盈餘預測修正 32
4-3特殊事件發生造成分析師預測能力改變 33
4-3-1 金融海嘯 33
4-3-2 頁岩油技術突破 34
4-3-3 結構性改變 35
五、研究結論與建議 36
參考文獻 38

參考文獻

Abramson, B., & Finizza, A. (1991). Using belief networks to forecast oil prices. International Journal of Forecasting, 7(3), 299-315.
Abramson, B., & Finizza, A. (1995). Probabilistic forecasts from probabilistic models: a case study in the oil market. International Journal of Forecasting, 11(1), 63-72.
Amin-Naseri, M. R., & Gharacheh, E. A. (2007). A hybrid artificial intelligence approach to monthly forecasting of crude oil price time series. Paper presented at the The Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, CEUR-WS284.
Andrews, D. W., Lee, I., & Ploberger, W. (1996). Optimal changepoint tests for normal linear regression. Journal of Econometrics, 70(1), 9-38.
Asquith, P., Mikhail, M. B., & Au, A. S. (2005). Information content of equity analyst reports. Journal of financial economics, 75(2), 245-282.
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 47-78.
Barone-Adesi, G., Bourgoin, F., & Giannopoulos, K. (1998). Don′t look back.
Barsky, R. B., & Kilian, L. (2001). Do we really know that oil caused the great stagflation? A monetary alternative. NBER Macroeconomics annual, 16, 137-183.
Baumeister, C., & Kilian, L. (2016). Understanding the Decline in the Price of Oil since June 2014. Journal of the Association of Environmental and Resource Economists, 3(1), 131-158.
Beaver, W., Lambert, R., & Morse, D. (1980). The information content of security prices. Journal of Accounting and Economics, 2(1), 3-28.
Beaver, W. H. (2002). Perspectives on recent capital market research. The Accounting Review, 77(2), 453-474.
Behar, A., & Ritz, R. A. (2017). OPEC vs US shale: Analyzing the shift to a market-share strategy. Energy Economics, 63, 185-198.
Bretschneider, S., & Schroeder, L. (1985). Revenue forecasting, budget setting and risk. Socio-Economic Planning Sciences, 19(6), 431-439.
Brown, P. (1993). Comments on ‘Earnings forecasting research: its implications for capital markets research’by L. Brown. International Journal of Forecasting, 9(3), 331-335.
Brown, L. D., Hagerman, R. L., Griffin, P. A., & Zmijewski, M. E. (1987). Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings. Journal of Accounting and Economics, 9(1), 61-87.
Buettner, T., & Kauder, B. (2010). Revenue forecasting practices: differences across countries and consequences for forecasting performance. Fiscal Studies, 31(3), 313-340.
Carruth, A., Hooker, M., & Oswald, A. (1994). Unemployment, oil prices and the real interest rate: evidence from Canada and the UK. Retrieved from
Carruth, A. A., Hooker, M. A., & Oswald, A. J. (1998). Unemployment equilibria and input prices: Theory and evidence from the United States. Review of economics and Statistics, 80(4), 621-628.
Clement, M. B. (1999). Analyst forecast accuracy: Do ability, resources, and portfolio complexity matter? Journal of Accounting and Economics, 27(3), 285-303.
Considine, T. J., & Larson, D. F. (2001). Risk premiums on inventory assets: the case of crude oil and natural gas. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 21(2), 109-126.
Cortazar, G., & Schwartz, E. S. (1997). Implementing a real option model for valuing an undeveloped oil field. International Transactions in Operational Research, 4(2), 125-137.
Datta, S., Iskandar-Datta, M., & Sharma, V. (2011). Product market pricing power, industry concentration and analysts’ earnings forecasts. Journal of Banking & Finance, 35(6), 1352-1366.
Davis, S. J., & Haltiwanger, J. (2001). Sectoral job creation and destruction responses to oil price changes. Journal of monetary economics, 48(3), 465-512.
Ferderer, J. P. (1996). Oil price volatility and the macroeconomy. Journal of macroeconomics, 18(1), 1-26.
Gulen, S. G. (1998). Efficiency in the crude oil futures market. Journal of Energy Finance & Development, 3(1), 13-21.
Gibson, R., & Schwartz, E. S. (1990). Stochastic convenience yield and the pricing of oil contingent claims. The Journal of Finance, 45(3), 959-976.
Gillman, M., & Nakov, A. (2009). Monetary effects on nominal oil prices. The North American Journal of Economics and Finance, 20(3), 239-254.
Grushevenko, E., & Grushevenko, D. (2012). Unconventional oil potential tends to change the world oil market. Energy Science and Technology, 4(1), 68-74.
Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of political economy, 91(2), 228-248.
Hooker, M. A. (1996). What happened to the oil price-macroeconomy relationship? Journal of monetary economics, 38(2), 195-213.
Huntington, H. G. (1994). Oil price forecasting in the 1980s: what went wrong? The Energy Journal, 1-22.
Ivashina, V., & Scharfstein, D. (2010). Bank lending during the financial crisis of 2008. Journal of financial economics, 97(3), 319-338.
Kaboudan, M. (2001). Compumetric forecasting of crude oil prices. Paper presented at the Evolutionary Computation, 2001. Proceedings of the 2001 Congress on.
Kilian, L. (2016). The impact of the shale oil revolution on US oil and gasoline prices. Review of Environmental Economics and Policy, 10(2), 185-205.
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.
Kross, W., Ro, B., & Schroeder, D. (1990). Earnings expectations: The analysts′ information advantage. Accounting Review, 461-476.
LAI, K. (2005). Crude oil price forecasting with TEI@ I methodology. Journal of Systems Sciences and Complexity, 18(2), 145-166.
Lang, M. H., & Lundholm, R. J. (1996). Corporate disclosure policy and analyst behavior. Accounting Review, 467-492.
Lanza, A., Manera, M., & Giovannini, M. (2005). Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Economics, 27(6), 831-848.
Laughton, D. G., & Jacoby, H. D. (1993). Reversion, timing options, and long-term decision-making. Financial Management, 225-240.
Laughton, D. G., & Jacoby, H. D. (1995). The effects of reversion on commodity projects of different length. Real options in capital investments: Models, strategies, and applications. Ed. by L. Trigeorgis. Westport: Praeger Publisher, 185-205.
Lin, K.-P., Pai, P.-F., Lu, Y.-M., & Chang, P.-T. (2013). Revenue forecasting using a least-squares support vector regression model in a fuzzy environment. Information Sciences, 220, 196-209.
M?nescu, C. B., & Nuno, G. (2015). Quantitative effects of the shale oil revolution. Energy Policy, 86, 855-866.
Mirmirani, S., & Cheng Li, H. (2004). A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. In Applications of Artificial Intelligence in Finance and Economics (pp. 203-223): Emerald Group Publishing Limited.
Monge, M., Gil-Alana, L. A., & de Gracia, F. P. (2017). US shale oil production and WTI prices behaviour. Energy, 141, 12-19.
Morana, C. (2001). A semiparametric approach to short-term oil price forecasting. Energy Economics, 23(3), 325-338.
Mork, K. A. (1989). Oil and the macroeconomy when prices go up and down: an extension of Hamilton′s results. Journal of political economy, 97(3), 740-744.
Mork, K. A., Olsen, O., & Mysen, H. T. (1994). Macroeconomic responses to oil price increases and decreases in seven OECD countries. The Energy Journal, 19-35.
Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica: Journal of the Econometric Society, 1361-1401.
Phelps, E. S. (1994). Structural slumps: The modern equilibrium theory of unemployment, interest, and assets: Harvard University Press.
Pindyck, R. S. (2001). The dynamics of commodity spot and futures markets: a primer. The Energy Journal, 1-29.
Schipper, K. (1991). Analysts′ forecasts. Accounting horizons, 5(4), 105.
Schwartz, E., & Smith, J. E. (2000). Short-term variations and long-term dynamics in commodity prices. Management Science, 46(7), 893-911.
Schwartz, E. S. (1997). The stochastic behavior of commodity prices: Implications for valuation and hedging. The Journal of Finance, 52(3), 923-973.
Slack, N., Chambers, S., & Johnston, R. (2010). Operations management: Pearson education.
Smith, H. C., Herbig, P., Milewicz, J., & Golden, J. E. (1996). Differences in forecasting behaviour between large and small firms. Journal of Marketing Practice: Applied Marketing Science, 2(1), 35-51.
Tang, L., & Hammoudeh, S. (2002). An empirical exploration of the world oil price under the target zone model. Energy Economics, 24(6), 577-596.
Wang, S., Yu, L., & Lai, K. K. (2005). A novel hybrid AI system framework for crude oil price forecasting. In Data Mining and Knowledge Management (pp. 233-242): Springer.
Whitfield, R., & Duffy, A. (2013). Extended revenue forecasting within a service industry. International Journal of Production Economics, 141(2), 505-518.
Xie, W., Yu, L., Xu, S., & Wang, S. (2006). A new method for crude oil price forecasting based on support vector machines. Paper presented at the International Conference on Computational Science.

Ye, M., Zyren, J., & Shore, J. (2002). Forecasting crude oil spot price using OECD petroleum inventory levels. International Advances in Economic Research, 8(4), 324-333.
Ye, M., Zyren, J., & Shore, J. (2005). A monthly crude oil spot price forecasting model using relative inventories. International Journal of Forecasting, 21(3), 491-501.
Ye, M., Zyren, J., & Shore, J. (2006). Forecasting short-run crude oil price using high-and low-inventory variables. Energy Policy, 34(17), 2736-2743.
Yu, L., Wang, S., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623-2635.
Zeileis, A., Kleiber, C., Kramer, W., & Hornik, K. (2003). Testing and dating of structural changes in practice. Computational Statistics & Data Analysis, 44(1-2), 109-123.

?

指導教授

黃承祖(Cheng-Tsu Huang)

審核日期

2018-7-20

推文