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Forecasting the stock returns of Chinese oil companies: Can investor attention help?

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  • Zhang, Yue-Jun
  • Li, Zhao-Chen

Abstract

This paper builds the investor attention index based on the Google Search Volume Index to forecast the stock returns of three major Chinese oil companies during 2004–2019. It takes into account several different model properties, including the time-varying parameters, regime switching and mixed-data frequency, etc. The empirical results suggest that investor attention helps to forecast the stock returns, and its capacity to forecast the US and Hong Kong stock markets is stronger than that of China's A-share market. For the US and Hong Kong markets, forecasting models with investor attention improves the accuracy by up to 35% compared with the benchmark model; but for the A-share market, the new forecasting models are superior only when it is one-month ahead. In addition, this paper confirms that investor attention does have economic value and can help mean-variance investors to get larger certainty equivalent return.

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  • Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
    • Handle: RePEc:eee:reveco:v:76:y:2021:i:c:p:531-555
    DOI: 10.1016/j.iref.2021.07.006
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Bataa, Erdenebat & Park, Cheolbeom, 2017. "Is the recent low oil price attributable to the shale revolution?," Energy Economics, Elsevier, vol. 67(C), pages 72-82.
    3. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    4. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    5. Plante, Michael, 2019. "OPEC in the news," Energy Economics, Elsevier, vol. 80(C), pages 163-172.
    6. Yuan, Yu, 2015. "Market-wide attention, trading, and stock returns," Journal of Financial Economics, Elsevier, vol. 116(3), pages 548-564.
    7. Raj Aggarwal & Brian M. Lucey, 2007. "Psychological barriers in gold prices?," Review of Financial Economics, John Wiley & Sons, vol. 16(2), pages 217-230.
    8. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
    9. Simon van Norden & Robert Vigfusson, 1996. "Regime-Switching Models, A guide to the Bank of Canada Gauss Procedures," Staff Working Papers 96-3, Bank of Canada.
    10. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
    11. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    12. Uddin, Gazi Salah & Rahman, Md Lutfur & Shahzad, Syed Jawad Hussain & Rehman, Mobeen Ur, 2018. "Supply and demand driven oil price changes and their non-linear impact on precious metal returns: A Markov regime switching approach," Energy Economics, Elsevier, vol. 73(C), pages 108-121.
    13. John Y. Campbell & Tuomo Vuolteenaho, 2004. "Inflation Illusion and Stock Prices," American Economic Review, American Economic Association, vol. 94(2), pages 19-23, May.
    14. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    15. Gupta, Kartick & Banerjee, Rajabrata, 2019. "Does OPEC news sentiment influence stock returns of energy firms in the United States?," Energy Economics, Elsevier, vol. 77(C), pages 34-45.
    16. Christiane Baumeister & Lutz Kilian, 2016. "Understanding the Decline in the Price of Oil since June 2014," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(1), pages 131-158.
    17. Cao, Guangxi & Zhou, Ling, 2019. "Asymmetric risk transmission effect of cross-listing stocks between mainland and Hong Kong stock markets based on MF-DCCA method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    18. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    19. Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
    20. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    21. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    22. Herwartz, Helmut, 2017. "Stock return prediction under GARCH — An empirical assessment," International Journal of Forecasting, Elsevier, vol. 33(3), pages 569-580.
    23. Jadidzadeh, Ali & Serletis, Apostolos, 2017. "How does the U.S. natural gas market react to demand and supply shocks in the crude oil market?," Energy Economics, Elsevier, vol. 63(C), pages 66-74.
    24. Amit Goyal & Ivo Welch, 2003. "Predicting the Equity Premium with Dividend Ratios," Management Science, INFORMS, vol. 49(5), pages 639-654, May.
    25. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    26. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
    27. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    28. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    29. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    30. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    31. Ji, Qiang & Liu, Bing-Yue & Zhao, Wan-Li & Fan, Ying, 2020. "Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS," International Review of Financial Analysis, Elsevier, vol. 68(C).
    32. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    33. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    34. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    35. Yin, Libo & Feng, Jiabao, 2019. "Can investors attention on oil markets predict stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 786-800.
    36. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    37. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    38. Bravo Caro, José Manuel & Golpe, Antonio A. & Iglesias, Jesús & Vides, José Carlos, 2020. "A new way of measuring the WTI – Brent spread. Globalization, shock persistence and common trends," Energy Economics, Elsevier, vol. 85(C).
    39. Golombek, Rolf & Irarrazabal, Alfonso A. & Ma, Lin, 2018. "OPEC's market power: An empirical dominant firm model for the oil market," Energy Economics, Elsevier, vol. 70(C), pages 98-115.
    40. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    41. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    42. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    43. Smith, James L. & Lee, Thomas K., 2017. "The price elasticity of U.S. shale oil reserves," Energy Economics, Elsevier, vol. 67(C), pages 121-135.
    44. Liu, Jing & Ma, Feng & Zhang, Yaojie, 2019. "Forecasting the Chinese stock volatility across global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 466-477.
    45. Li, Sufang & Zhang, Hu & Yuan, Di, 2019. "Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests," Energy Economics, Elsevier, vol. 84(C).
    46. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
    47. Brigida, Matthew, 2014. "The switching relationship between natural gas and crude oil prices," Energy Economics, Elsevier, vol. 43(C), pages 48-55.
    48. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
    49. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    50. Yang, Kun & Wei, Yu & Li, Shouwei & He, Jianmin, 2020. "Asymmetric risk spillovers between Shanghai and Hong Kong stock markets under China’s capital account liberalization," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    51. Goh, Jeremy C. & Jiang, Fuwei & Tu, Jun & Wang, Yuchen, 2013. "Can US economic variables predict the Chinese stock market?," Pacific-Basin Finance Journal, Elsevier, vol. 22(C), pages 69-87.
    52. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    53. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    54. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    55. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    56. Lv, Xin & Lien, Donald & Yu, Chang, 2020. "Who affects who? Oil price against the stock return of oil-related companies: Evidence from the U.S. and China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 85-100.
    57. Zhang, Yue-Jun & Wu, Yao-Bin, 2019. "The time-varying spillover effect between WTI crude oil futures returns and hedge funds," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 156-169.
    58. Zhu, Zhaobo & Ji, Qiang & Sun, Licheng & Zhai, Pengxiang, 2020. "Oil price shocks, investor sentiment, and asset pricing anomalies in the oil and gas industry," International Review of Financial Analysis, Elsevier, vol. 70(C).
    59. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    60. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    61. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    62. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    63. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    64. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    65. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    66. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
    67. Rahman, Sajjadur, 2016. "Another perspective on gasoline price responses to crude oil price changes," Energy Economics, Elsevier, vol. 55(C), pages 10-18.
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