IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v134y2024ics0140988324003025.html
   My bibliography  Save this article

Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks

Author

Listed:
  • Ren, Xiaocong
  • Huang, Zilong
  • He, Yiqun

Abstract

Venture capital firms may be unable to withstand the high investments and risks associated with coal mining exploration due to limitations in the funding scale. Risk control capability is also a challenge for venture capital companies. Finding a balance between high-risk and high-return coal mining exploration projects is a problem that venture capital companies must face. We propose a financial risk-warning model for coal mining investment enterprises based on the fruit fly optimisation algorithm (FOA) with the backpropagation neural network (BPNN). The fusion of standardised and dimensionless sample data through factor analysis reduces the input dimension of the BPNN and improves its stability. The financial warning algorithm of the model has been effectively validated through experiments. The research results indicate that its accuracy has reached a high level of financial warning, reaching a level of 95%.

Suggested Citation

  • Ren, Xiaocong & Huang, Zilong & He, Yiqun, 2024. "Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks," Energy Economics, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324003025
    DOI: 10.1016/j.eneco.2024.107594
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324003025
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107594?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. Zixian Liu & Guansan Du & Shuai Zhou & Haifeng Lu & Han Ji, 2022. "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1481-1499, April.
    2. Shaomin Ren & Miaochao Chen, 2022. "Optimization of Enterprise Financial Management and Decision-Making Systems Based on Big Data," Journal of Mathematics, Hindawi, vol. 2022, pages 1-11, January.
    3. Marcos Geraldo Gomes & Victor Hugo Carlquist da Silva & Luiz Fernando Rodrigues Pinto & Plinio Centoamore & Salvatore Digiesi & Francesco Facchini & Geraldo Cardoso de Oliveira Neto, 2020. "Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles," Sustainability, MDPI, vol. 12(9), pages 1-19, April.
    4. Ren, Siyu & Hao, Yu & Xu, Lu & Wu, Haitao & Ba, Ning, 2021. "Digitalization and energy: How does internet development affect China's energy consumption?," Energy Economics, Elsevier, vol. 98(C).
    5. Wei Zhang & Hao Zhou & Jie Chen & Zifu Fan, 2022. "An Empirical Analysis of the Impact of Digital Economy on Manufacturing Green and Low-Carbon Transformation under the Dual-Carbon Background in China," IJERPH, MDPI, vol. 19(20), pages 1-22, October.
    6. Shahbaz, Muhammad & Siddiqui, Aaliyah & Ahmad, Shabbir & Jiao, Zhilun, 2023. "Financial development as a new determinant of energy diversification: The role of natural capital and structural changes in Australia," Energy Economics, Elsevier, vol. 126(C).
    7. Tykkyläinen, Saila & Ritala, Paavo, 2021. "Business model innovation in social enterprises: An activity system perspective," Journal of Business Research, Elsevier, vol. 125(C), pages 684-697.
    8. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    9. Munir, Qaiser & Lean, Hooi Hooi & Smyth, Russell, 2020. "CO2 emissions, energy consumption and economic growth in the ASEAN-5 countries: A cross-sectional dependence approach," Energy Economics, Elsevier, vol. 85(C).
    10. Bourcet, Clémence, 2020. "Empirical determinants of renewable energy deployment: A systematic literature review," Energy Economics, Elsevier, vol. 85(C).
    11. Yanjun Liang & Wei-hua Zhang & Youjun Lu & Zhong-Sheng Wang, 2020. "Optimal Control and Simulation for Enterprise Financial Risk in Industry Environment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-6, September.
    12. Geronikolaou, George & Papachristou, George, 2016. "Investor competition and project risk in Venture Capital investments," Economics Letters, Elsevier, vol. 141(C), pages 67-69.
    13. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    14. Lau, Chi Keung & Gozgor, Giray & Mahalik, Mantu Kumar & Patel, Gupteswar & Li, Jing, 2023. "Introducing a new measure of energy transition: Green quality of energy mix and its impact on CO2 emissions," Energy Economics, Elsevier, vol. 122(C).
    15. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    16. Jianjun Xu & Lijie Yu & Rakesh Gupta, 2020. "Evaluating the Performance of the Government Venture Capital Guiding Fund Using the Intuitionistic Fuzzy Analytic Hierarchy Process," Sustainability, MDPI, vol. 12(17), pages 1-24, August.
    17. Chishti, Muhammad Zubair & Sinha, Avik & Zaman, Umer & Shahzad, Umer, 2023. "Exploring the dynamic connectedness among energy transition and its drivers: Understanding the moderating role of global geopolitical risk," Energy Economics, Elsevier, vol. 119(C).
    18. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2019. "Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model," Energy Economics, Elsevier, vol. 78(C), pages 129-142.
    19. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    20. Oleksandr Melnychenko, 2020. "Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?," JRFM, MDPI, vol. 13(9), pages 1-19, August.
    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. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Ma, Xin, 2024. "The butterfly effect of cloud computing on the low-carbon economy," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    2. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    3. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    4. Ye, Tuo & Zhao, Songyu & Lau, Chi Keung Marco & Chau, Frankie, 2024. "Social media sentiment of hydrogen fuel cell vehicles in China: Evidence from artificial intelligence algorithms," Energy Economics, Elsevier, vol. 133(C).
    5. Chen, Yan & Zhang, Ruiqian & Lyu, Jiayi & Hou, Yuqi, 2024. "AI and Nuclear: A perfect intersection of danger and potential?," Energy Economics, Elsevier, vol. 133(C).
    6. Jiao, Anqi & Lu, Juntai & Ren, Honglin & Wei, Jia, 2024. "The role of AI capabilities in environmental management: Evidence from USA firms," Energy Economics, Elsevier, vol. 134(C).
    7. Zhong, Yufei & Chen, Xuesheng & Wang, Zhixian & Lin, Regina Fang-Ying, 2024. "The nexus among artificial intelligence, supply chain and energy sustainability: A time-varying analysis," Energy Economics, Elsevier, vol. 132(C).
    8. Chishti, Muhammad Zubair & Xia, Xiqiang & Dogan, Eyup, 2024. "Understanding the effects of artificial intelligence on energy transition: The moderating role of Paris Agreement," Energy Economics, Elsevier, vol. 131(C).
    9. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    10. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    11. Zhao, Qiuyun & Jiang, Mei & Zhao, Zuoxiang & Liu, Fan & Zhou, Li, 2024. "The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation," Energy Economics, Elsevier, vol. 133(C).
    12. Lee, Chi-Chuan & Fang, Yuzhu & Quan, Shiyun & Li, Xinghao, 2024. "Leveraging the power of artificial intelligence toward the energy transition: The key role of the digital economy," Energy Economics, Elsevier, vol. 135(C).
    13. Yang, Shengyao & Zhu, Meng Nan & Yu, Haiyan, 2024. "Are artificial intelligence and blockchain the key to unlocking the box of clean energy?," Energy Economics, Elsevier, vol. 134(C).
    14. Song, Malin & Pan, Heting & Shen, Zhiyang & Tamayo-Verleene, Kristine, 2024. "Assessing the influence of artificial intelligence on the energy efficiency for sustainable ecological products value," Energy Economics, Elsevier, vol. 131(C).
    15. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    16. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Dependence structure in the Australian electricity markets: New evidence from regular vine copulae," Energy Economics, Elsevier, vol. 90(C).
    17. Ben Cheikh, Nidhaleddine & Ben Zaied, Younes, 2024. "Understanding the drivers of the renewable energy transition," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 604-612.
    18. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    19. Wenjing Zhang & Bin Sun & Zaijun Li & Suleman Sarwar, 2023. "The Impact of the Digital Economy on Industrial Eco-Efficiency in the Yangtze River Delta (YRD) Urban Agglomeration," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    20. Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Wali Ullah, G M & Tiwari, Aviral Kumar & Khan, Isma, 2023. "Tail risk contagion across electricity markets in crisis periods," Energy Economics, Elsevier, vol. 127(PB).

    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:eneeco:v:134:y:2024:i:c:s0140988324003025. 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/eneco .

    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.