IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v47y2022ipas154461232200112x.html
   My bibliography  Save this article

The heterogeneous treatment effect of low-carbon city pilot policy on stock return: A generalized random forests approach

Author

Listed:
  • Wang, Man
  • Yang, Qiuping

Abstract

To achieve sustainable development, China has launched the low-carbon city pilot (LCCP) program in 2010. However, the impact of this policy on firm performance has not been well investigated. By the cutting-edge generalized random forests (GRF) method, this paper takes LCCP as a quasi-natural experiment and analyzes its heterogeneous treatment effect on the stock return of Chinese firms. It is found that LCCP has a significantly negative effect on stock return and the market reacts in advance. The effect is heterogeneous and nonlinearly decided by firm features. Specifically, firms with lower financial leverage, greater profitability and longer listing years suffer more decrease in stock return. Best linear predictor test suggests that the GRF method provides satisfying estimate of the true heterogeneous treatment effect.

Suggested Citation

  • Wang, Man & Yang, Qiuping, 2022. "The heterogeneous treatment effect of low-carbon city pilot policy on stock return: A generalized random forests approach," Finance Research Letters, Elsevier, vol. 47(PA).
    • Handle: RePEc:eee:finlet:v:47:y:2022:i:pa:s154461232200112x
    DOI: 10.1016/j.frl.2022.102808
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S154461232200112X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.102808?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Li, Zhong-fei & Zhou, Qi & Chen, Ming & Liu, Qian, 2021. "The impact of COVID-19 on industry-related characteristics and risk contagion," Finance Research Letters, Elsevier, vol. 39(C).
    3. Ramiah, Vikash & Martin, Belinda & Moosa, Imad, 2013. "How does the stock market react to the announcement of green policies?," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1747-1758.
    4. Prayag Lal Yadav & Seung Hun Han & Jae Jeung Rho, 2016. "Impact of Environmental Performance on Firm Value for Sustainable Investment: Evidence from Large US Firms," Business Strategy and the Environment, Wiley Blackwell, vol. 25(6), pages 402-420, September.
    5. Wang, Yanbing & Delgado, Michael S. & Khanna, Neha & Bogan, Vicki L., 2019. "Good news for environmental self-regulation? Finding the right link," Journal of Environmental Economics and Management, Elsevier, vol. 94(C), pages 217-235.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gao, Yihong & Gao, Jiayan, 2023. "Low-carbon transformation and corporate cash holdings," Finance Research Letters, Elsevier, vol. 54(C).
    2. Xu, Xin & Huang, Shupei & Lucey, Brian M. & An, Haizhong, 2023. "The impacts of climate policy uncertainty on stock markets: Comparison between China and the US," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Liu, Xiangsheng & Lv, Lingli, 2023. "The effect of China's low carbon city pilot policy on corporate financialization," Finance Research Letters, Elsevier, vol. 54(C).
    4. Cong Wei & Jiayang Kong, 2022. "The Effect of Innovation City Construction on Carbon Emissions in China," Land, MDPI, vol. 11(7), pages 1-14, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Assad Ullah & Xinshun Zhao & Unbreen Qayyum & Muhammad Abdul Kamal & Aamir Aijaz Sayed, 2024. "Modeling the Relationship Between Environmental Regulations and Stock Market Growth in China: Evidence Beyond Symmetry," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 2460-2481, March.
    2. Yuan Chen & Vinod Singhal & Qinghua Zhu, 2021. "Environmental policies and financial performance: stock market reaction to firms for their proactive environmental practices recognized by governmental programs," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 1548-1562, May.
    3. Guo, Mengmeng & Kuai, Yicheng & Liu, Xiaoyan, 2020. "Stock market response to environmental policies: Evidence from heavily polluting firms in China," Economic Modelling, Elsevier, vol. 86(C), pages 306-316.
    4. Di Chen & Yue Wang & Yang Wen & Honglin Du & Xue Tan & Lei Shi & Zhong Ma, 2021. "Does Environmental Policy Help Green Industry? Evidence from China’s Promotion of Municipal Solid Waste Sorting," IJERPH, MDPI, vol. 18(6), pages 1-15, March.
    5. Omaima A.G. Hassan & Peter Romilly, 2018. "Relations between corporate economic performance, environmental disclosure and greenhouse gas emissions: New insights," Business Strategy and the Environment, Wiley Blackwell, vol. 27(7), pages 893-909, November.
    6. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    7. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    8. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    9. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-Based Identification with Formula Instruments: A Review," NBER Working Papers 31393, National Bureau of Economic Research, Inc.
    10. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    11. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    12. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    13. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    14. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Locally Robust Estimation with Generated Regressors," Papers 2301.10643, arXiv.org, revised Nov 2023.
    15. Shi, Chengchun & Zhou, Yunzhe & Li, Lexin, 2024. "Testing directed acyclic graph via structural, supervised and generative adversarial learning," LSE Research Online Documents on Economics 119446, London School of Economics and Political Science, LSE Library.
    16. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    17. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    18. Yu Jin Chang & Jae Wook Yoo, 2023. "How Does the Degree of Competition in an Industry Affect a Company’s Environmental Management and Performance?," Sustainability, MDPI, vol. 15(9), pages 1-11, May.
    19. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    20. Pham, Huy Nguyen Anh & Ramiah, Vikash & Moosa, Nisreen & Huynh, Tam & Pham, Nhi, 2018. "The financial effects of Trumpism," Economic Modelling, Elsevier, vol. 74(C), pages 264-274.

    More about this item

    Keywords

    Generalized random forests; Heterogeneous treatment effects; Low-carbon city pilot; Stock return; Quasi-natural experiment;
    All these keywords.

    JEL classification:

    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:47:y:2022:i:pa:s154461232200112x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.