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Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence

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  • Zhang, Heng-Guo
  • CAO, Tingting
  • Li, Houxuan
  • Xu, Tiantian

Abstract

This paper dynamically measures news-driven information friction in China's carbon market theoretically and empirically. The biggest difference between this paper and the existing literature is that this paper does not assume that the information shock is a white noise process but that the information shock comes from the news. This study does not use fiscal expenditure budget data and fiscal expenditure final data to estimate information friction but rather extends the standard Fève and Pietrunti (2016) theoretical framework to identify effective information and noise signals from market policy news by machine learning. A latent Dirichlet allocation model is employed to estimate the variance of effective information. China's carbon policy uncertainty as indicated by Chinese-language newspapers is used to estimate the variance of the noise signal. Therefore, we can calculate time-varying high-frequency information friction. The results of this study show that enterprises with different attributes in the same industry face different information friction. In China, state-owned industrial enterprises have the lowest levels of information friction. Joint-stock industrial enterprises have the highest levels of information friction. The higher the degree of policy information friction, the weaker the impact of effective information shocks and the greater the reduction in policy effects. These results have policy implications – the government should pay attention to enterprise attributes to reduce policy uncertainty and the degree of policy information friction, thereby enhancing the effectiveness of policy regulation.

Suggested Citation

  • Zhang, Heng-Guo & CAO, Tingting & Li, Houxuan & Xu, Tiantian, 2021. "Dynamic measurement of news-driven information friction in China's carbon market: Theory and evidence," Energy Economics, Elsevier, vol. 95(C).
    • Handle: RePEc:eee:eneeco:v:95:y:2021:i:c:s0140988320303340
    DOI: 10.1016/j.eneco.2020.104994
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    References listed on IDEAS

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    1. Olivier J. Blanchard & Jean-Paul L'Huillier & Guido Lorenzoni, 2013. "News, Noise, and Fluctuations: An Empirical Exploration," American Economic Review, American Economic Association, vol. 103(7), pages 3045-3070, December.
    2. Liebmann, Michael & Orlov, Alexei G. & Neumann, Dirk, 2016. "The tone of financial news and the perceptions of stock and CDS traders," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 159-175.
    3. Langer, Viktoria C.E., 2016. "News shocks, nonseparable preferences, and optimal monetary policy," Journal of Macroeconomics, Elsevier, vol. 49(C), pages 237-246.
    4. Bajo, Emanuele & Raimondo, Carlo, 2017. "Media sentiment and IPO underpricing," Journal of Corporate Finance, Elsevier, vol. 46(C), pages 139-153.
    5. Tang, Tony T., 2009. "Information asymmetry and firms' credit market access: Evidence from Moody's credit rating format refinement," Journal of Financial Economics, Elsevier, vol. 93(2), pages 325-351, August.
    6. 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.
    7. Fève, Patrick & Pietrunti, Mario, 2016. "Noisy fiscal policy," European Economic Review, Elsevier, vol. 85(C), pages 144-164.
    8. Lee, Jongsub & Naranjo, Andy & Velioglu, Guner, 2018. "When do CDS spreads lead? Rating events, private entities, and firm-specific information flows," Journal of Financial Economics, Elsevier, vol. 130(3), pages 556-578.
    9. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    10. Brandt, Michael W. & Gao, Lin, 2019. "Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 64-94.
    11. Neuhierl, Andreas & Scherbina, Anna & Schlusche, Bernd, 2013. "Market Reaction to Corporate Press Releases," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1207-1240, August.
    12. Fève, Patrick & Kass-Hanna, Tannous & Pietrunti, Mario, 2016. "An analytical characterization of noisy fiscal policy," Economics Letters, Elsevier, vol. 148(C), pages 76-79.
    13. Fan, Haichao & Gao, Xiang & Xu, Juanyi & Xu, Zhiwei, 2016. "News shock, firm dynamics and business cycles: Evidence and theory," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 159-180.
    14. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    15. Born, Benjamin & Peter, Alexandra & Pfeifer, Johannes, 2013. "Fiscal news and macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2582-2601.
    16. Karel Mertens & MortenO. Ravn, 2010. "Measuring the Impact of Fiscal Policy in the Face of Anticipation: A Structural VAR Approach," Economic Journal, Royal Economic Society, vol. 120(544), pages 393-413, May.
    17. Smales, Lee A., 2016. "News sentiment and bank credit risk," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 37-61.
    18. Roman Kräussl & Elizaveta Mirgorodskaya, 2017. "Media, sentiment and market performance in the long run," The European Journal of Finance, Taylor & Francis Journals, vol. 23(11), pages 1059-1082, September.
    19. Gao, Haoyu & Wang, Junbo & Wang, Yanchu & Wu, Chunchi & Dong, Xi, 2020. "Media Coverage and the Cost of Debt," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(2), pages 429-471, March.
    20. Nan Hu & Ling Liu & Lu Zhu, 2018. "Credit default swap spreads and annual report readability," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 591-621, February.
    21. Tzu‐Ting Chiu & Yuyan Guan & Jeong‐Bon Kim, 2018. "The Effect of Risk Factor Disclosures on the Pricing of Credit Default Swaps," Contemporary Accounting Research, John Wiley & Sons, vol. 35(4), pages 2191-2224, December.
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    Cited by:

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    2. Düsterhöft, Maximilian & Schiemann, Frank & Walther, Thomas, 2023. "Let’s talk about risk! Stock market effects of risk disclosure for European energy utilities," Energy Economics, Elsevier, vol. 125(C).

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