|
|
Oil Price Trends and the Stock Market: Empirical Evidence from 35 Countries along “the Belt and Road” |
ZHU Xiaoneng, YUAN Jingfa
|
School of Finance, Shanghai University of Finance and Economics/Shanghai Institute of International Finance and Economics |
|
|
Abstract Large fluctuations in international oil prices not only exacerbate global economic uncertainty but also make it difficult to study the impact of crude oil prices on the economy. Researchers usually study this relationship using end-of-month closing price data. However, the price volatility caused by the financialization of crude oil markets imparts a substantial amount of noise—i.e., information unrelated to economic fundamentals—to crude oil prices, with the result that closing prices on adjacent trading days diverge significantly. It is unlikely that short-term changes in crude oil prices will affect economic fundamentals, and there is no evidence that such short-term fluctuations will affect the stock market. Therefore, considering only end-of-month closing price data will inevitably lead to large errors in analysis results and even erroneous conclusions. Information noise will distort the price of crude oil, causing it to deviate from the normal supply and demand price. Therefore, researchers must try to eliminate the impact of noise when using crude oil prices to analyze economy. To this end, we propose a simple and feasible method, the moving average method, with which we expect to reduce the impact of noise to some extent. The empirical results show that the oil price trend factor based on the moving average method has significantly improved predictive power for the stock market compared to the simple oil price factor. Why is the oil price trend factor based on the moving average method significantly better at predicting the stock market? We believe there are several reasons. First, short-term fluctuations in international crude oil prices contain noise unrelated to economic fundamentals, but the moving average method can weaken the influence of noise to some extent. Second, due to an insufficient investor response (Hong and Stein, 1999), it takes time for oil price information to fully reflect the stock market (Driesprong et al., 2008). Unlike the simple oil price factor, which contains price information only for a particular day, the trend factor based on the moving average method contains price information for the preceding period and thus has a stronger forecasting effect on the stock market. Third, the trend factor can better grasp unfolding price trends and influence investor expectations. Based on the moving average method, this paper extracts the oil price trend factor from international crude oil prices to study the impact of oil price fluctuations on the stock markets of 35 “Belt and Road” countries. The study finds that the moving average method can effectively reduce information noise in oil prices. The stock market forecasting effect of the oil price trend factor based on the moving average method exists both inside and outside the sample. This paper also examines the stock markets of oil-producing countries and those of non-oil-producing countries. It confirms that the stock market impact of oil price fluctuations is asymmetric betweenoil-producing and non-oil-producing countries in two aspects. First, a rise in crude oil prices is conducive to the stock markets of oil-producing countries but not to those of non-oil-producing countries. Second, stock markets of oil-producing countries are more sensitive to fluctuations in crude oil prices. In addition, the paper finds that the stock market forecasting ability of international oil prices is time-varying. When the economy is in a down cycle, international oil prices are more predictive of the stock market. The main contribution of this paper is that it proposes and confirms a simple, feasible method of reducing oil price information noise, namely the moving average method. Second, this paper provides new and powerful evidence on the impact of oil prices on the stock market. Whether the price of oil can predict the stock market is still controversial (Chen et al., 1986; Huang et al., 1996; Jones and Kaul, 1996; Driesprong et al., 2008). After reducing noise with the oil price trend factor, this paper finds that there still exists an impact of international oil prices on the stock market.
|
Received: 19 July 2018
Published: 27 September 2019
|
|
|
|
[1] |
曹伟、言方荣和鲍曙明,2016,《人民币汇率变动、邻国效应与双边贸易——基于中国与“一带一路”沿线国家空间面板模型的实证研究》,《金融研究》第9期,第50~66页。
|
[2] |
陈胜蓝和刘晓玲,2018,《公司投资如何响应“一带一路”倡议?—基于准自然实验的经验研究》,《财经研究》第4期,第20~33页。
|
[3] |
韩立岩和尹力博,2012,《投机行为还是实际需求?——国际大宗商品价格影响因素的广义视角分析》,《经济研究》第12期,第83~96页。
|
[4] |
姜富伟、凃俊、David E. Rapach、Jack K. Strauss和周国富,2011,《中国股票市场可预测性的实证研究》,《金融研究》第9期,第107~121页。
|
[5] |
金洪飞和金荦,2010,《国际石油价格对中国股票市场的影响——基于行业数据的经验分析》,《金融研究》02期,第173~187页。
|
[6] |
金洪飞和金荦,2008,《石油价格与股票市场的溢出效应——基于中美数据的比较分析》,《金融研究》第2期,第83~97页。
|
[7] |
李兵和颜晓晨,2018,《中国与“一带一路”沿线国家双边贸易的新比较优势——公共安全的视角》,《经济研究》第1期,第183~197页。
|
[8] |
田利辉和谭德凯,2014,《大宗商品现货定价的金融化和美国化问题——股票指数与商品现货关系研究》,《中国工业经济》第10期,第72~84页。
|
[9] |
田利辉和谭德凯,2015,《原油价格的影响因素分析:金融投机还是中国需求?》,《经济学(季刊)》第3期,第961~982页。
|
[10] |
赵书博和胡江云,2016,《“一带一路”战略构想下完善我国企业境外投资所得税制的思考》,《管理世界》第11期,第11~19页。
|
[11] |
Akbas, F., C. Jiang and P. D. Koch, 2017, “The Trend in Firm Profitability and the Cross Section of Stock Returns.” The Accounting Review, 92(5):Forthcoming.
|
[12] |
Brock, W., J. Lakonishok and B. Lebaron, 1992, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5):1731~1764.
|
[13] |
Campbell, J. Y. and S. B. Thompson, 2008, “Predicting Excess Stock Returns out of Sample: Can Anything Beat the Historical Average?” Review of Financial Studies, 21(4):1509~1531.
|
[14] |
Chen, N., R. Roll and S. A. Ross, 1986, “Economic Forces and the Stock Market.” Journal of Business, 59(3):383~403.
|
[15] |
Chiang, I. E., W. K. Hughen and J. S. Sagi, 2015, “Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets.” Journal of Finance, 70(2):769~804.
|
[16] |
Ciner, C., 2001, “Energy Shocks and Financial Markets: Nonlinear Linkages.” Studies in Nonlinear Dynamics & Econometrics, 5(3):1079.
|
[17] |
Clark, T. E. and K. D. West, 2007, “Approximately Normal Tests for Equal Predictive Accuracy in Nested Models.” Journal of Econometrics, 138(1):291~311.
|
[18] |
Driesprong, G., B. Jacobsen and B. Matt, 2008, “Striking Oil: Another Puzzle?” Journal of Financial Economics, 89(2):307~327.
|
[19] |
Falkowski, M., 2011, “Financialization of Commodities.” Contemporary Economics, 5(4):4~17.
|
[20] |
Han, Y., G. Zhou and Y. Zhu, 2016, “A Trend Factor: Any Economic Gains from Using Information over Investment Horizons?” Journal of Financial Economics, 122∶352~375.
|
[21] |
Henkel, S. J., J. S. Martin and F. Nardari, 2011, “Time-Varying Short-Horizon Return Predictability.” Journal of Financial Economics, 99(3):560~580.
|
[22] |
Hiemstra, C. and J. D. Jones, 1994, “Testing for Linear and Nonlinear Granger Causality in the Stock Price‐Volume Relation.” Journal of Finance, 49(5):1639~1664.
|
[23] |
Hong, H. and J. C. Stein, 1999, “A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets.” Journal of Finance, 54(6):2143~2184.
|
[24] |
Huang, D., F. Jiang, J. Tu and G. Zhou, 2015, “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns.” Review of Financial Studies, 28(3):791~837.
|
[25] |
Huang, R. D., R. W. Masulis and H. R. Stoll, 1996, “Energy Shocks and Financial Markets.” Journal of Futures Markets, 16(1):1~27.
|
[26] |
Huang, Y., 2016, “Understanding China's Belt & Road Initiative: Motivation, Framework and Assessment.” China Economic Review, 40∶314~321.
|
[27] |
Jones, C. M. and G. Kaul, 1996, “Oil and the Stock Markets.” Journal of Finance, 51(2):463~491.
|
[28] |
Mele, A., 2007, “Asymmetric Stock Market Volatility and the Cyclical Behavior of Expected Returns.” Journal of Financial Economics, 86(2):446~478.
|
[29] |
Neely, C. J., D. E. Rapach, J. Tu and G. Zhou, 2014, “Forecasting the Equity Risk Premium: The Role of Technical Indicators.” Management Science, 60(7):1772~1791.
|
[30] |
O'Neill, T. J., J. Penm and R. D. Terrell, 2008, “The Role of Higher Oil Prices: A Case of Major Developed Countries.” Research in Finance, 24(24):287~299.
|
[31] |
Park, J. and R. A. Ratti, 2008, “Oil Price Shocks and Stock Markets in the U.S. and 13 European Countries.” Energy Economics, 30(5):2587~2608.
|
[32] |
Rapach, D. E., J. K. Strauss and G. Zhou, 2010, “Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy.” Review of Financial Studies, 23(2):821~862.
|
[33] |
Sadorsky, P., 1999, “Oil Price Shocks and Stock Market Activity.” Energy Economics, 21(5):449~469.
|
[34] |
Singleton, K. J., 2013, “Investor Flows and the 2008 Boom/Bust in Oil Prices.” Management Science, 60(2):300~318.
|
[35] |
Sockin, M. and W. Xiong, 2015, “Informational Frictions and Commodity Markets.” Journal of Finance, 70(5):2063~2098.
|
[36] |
Tang, K. and W. Xiong, 2012, “Index Investment and the Financialization of Commodities.” Financial Analysts Journal, 68(6):54~74.
|
[37] |
Welch, I. and A. Goyal, 2008, “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction.” Review of Financial Studies, 21(4):1455~1508.
|
|
|
|