IDEAS home Printed from https://ideas.repec.org/a/spr/minecn/v37y2024i1d10.1007_s13563-024-00428-z.html
   My bibliography  Save this article

Investigating the impact of company announcements on stock prices: an application of machine learning on Australian lithium market

Author

Listed:
  • Ahmad Kianrad

    (University of Technology Sydney)

  • Mohadeseh Najafi Arani

    (Edith Cowan University)

  • Karim Hasani

    (Flinders University)

  • Masoumeh Zargar

    (Edith Cowan University)

  • Eila Erfani

    (University of Technology Sydney
    University of New South Wales)

  • Amir Razmjou

    (Edith Cowan University
    Edith Cowan University)

Abstract

This paper investigates the effects of various types of announcements made by lithium producers on stock prices. We collected data from 40 lithium-producing companies listed on the world's largest stock exchanges, spanning from May 2020 to September 2022. To analyze the impact of announcements such as quoted and unquoted securities, market announcements, company reports, public meetings and presentations, financial announcements, and technical announcements on stock prices, we employed an extreme gradient boosting (XGBoost) model. Our results indicate that stock exchange market announcements and announcements about public meetings and presentations significantly influenced the stock prices of all eight large-cap companies studied. Announcements about public meetings and presentations were crucial predictors of stock prices for 73% of all companies analyzed. Additionally, positive financial announcements were key predictors for 70% of the companies. These findings suggest that investors should consider these predictors when making investment decisions in the lithium-related stock market. This study contributes to the existing literature by providing empirical evidence on the impact of different types of announcements made by lithium producers on stock prices. Furthermore, the XGBoost model used in this study can be applied to other industries and markets to analyze the impact of various types of announcements on stock prices.

Suggested Citation

  • Ahmad Kianrad & Mohadeseh Najafi Arani & Karim Hasani & Masoumeh Zargar & Eila Erfani & Amir Razmjou, 2024. "Investigating the impact of company announcements on stock prices: an application of machine learning on Australian lithium market," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(1), pages 163-172, March.
    • Handle: RePEc:spr:minecn:v:37:y:2024:i:1:d:10.1007_s13563-024-00428-z
    DOI: 10.1007/s13563-024-00428-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13563-024-00428-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13563-024-00428-z?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. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Post-Print hal-03676638, HAL.
    2. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.
    3. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    4. Liu, Donghui & Gao, Xiangyun & An, Haizhong & Qi, Yabin & Wang, Ze & Jia, Nanfei & Chen, Zhihua, 2020. "Exploring behavior changes of the lithium market in China: Toward technology-oriented future scenarios," Resources Policy, Elsevier, vol. 69(C).
    Full references (including those not matched with items on IDEAS)

    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. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    2. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    3. Wang, Jianzhou & Lv, Mengzheng & Wang, Shuai & Gao, Jialu & Zhao, Yang & Wang, Qiangqiang, 2024. "Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    4. Saqib Farid & Rubeena Tashfeen & Tahseen Mohsan & Arsal Burhan, 2023. "Forecasting stock prices using a data mining method: Evidence from emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1911-1917, April.
    5. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    6. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    7. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2024. "Forecasting relative returns for S&P 500 stocks using machine learning," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-16, December.
    8. Şirin Özlem & Omer Faruk Tan, 2022. "Predicting cash holdings using supervised machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-19, December.
    9. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    10. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    11. Han Gui, 2024. "Machine learning in weekly movement prediction," Papers 2407.09831, arXiv.org.
    12. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brandon K. Bellows & Yue Zhang & Zugui Zhang & Paul Kolm & William S. Weintraub & Andrew S. Moran & Jincheng Shen, 2022. "An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest," Papers 2204.10971, arXiv.org.
    13. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
    14. Yang, Yanlin & Hu, Xuemei & Jiang, Huifeng, 2022. "Group penalized logistic regressions predict up and down trends for stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    15. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
    16. Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
    17. Song, Huiling & Wang, Chang & Sun, Kun & Geng, Hongjun & Zuo, Lyushui, 2023. "Material efficiency strategies across the industrial chain to secure indium availability for global carbon neutrality," Resources Policy, Elsevier, vol. 85(PB).
    18. Ghallabi, Fahmi & Souissi, Bilel & Du, Anna Min & Ali, Shoaib, 2025. "ESG stock markets and clean energy prices prediction: Insights from advanced machine learning," International Review of Financial Analysis, Elsevier, vol. 97(C).
    19. Junwei Chen, 2023. "Analysis of Bitcoin Price Prediction Using Machine Learning," JRFM, MDPI, vol. 16(1), pages 1-25, January.
    20. Zheng, Shuxian & Tan, Zhanglu & Xing, Wanli & Zhou, Xuanru & Zhao, Pei & Yin, Xiuqi & Hu, Han, 2022. "A comparative exploration of the chaotic characteristics of Chinese and international copper futures prices," Resources Policy, Elsevier, vol. 78(C).

    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:spr:minecn:v:37:y:2024:i:1:d:10.1007_s13563-024-00428-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.