IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0289691.html
   My bibliography  Save this article

Evaluation and screening of technology start-ups based on PCA and GA-BPNN

Author

Listed:
  • Jiaxin Li
  • Mingming Meng
  • Xin Liu
  • Yanjie Lv
  • Jian Yu

Abstract

Purpose: Due to the existence of information opacity, there is a common problem of adverse selection in the process of screening alternative technology start-ups (TSs) and determining investment targets by venture capital institutions, which does not reveal the true value of enterprises and makes the market inefficient. The aim of this paper is to design an evaluation and screening system help venture capital institutions to select the qualified TSs as their investment objective. Design: A research framework of four dimensions that include conception, technical innovation, business model and team structure, was built based on previous studies. Based on the research framework, 15 second-level indicators and 33 third-level indicators were extracted with literature research method. This paper proposes an evaluation model with back propagation neural network (BPNN) optimized by genetic algorithm (GA) to improve the rate of selecting and investing in qualified start-ups. Findings: The results show that the evaluation accuracy of the evaluation model for qualified and unqualified enterprises can reach 80.33% and 93.67% respectively, which has verified the effectiveness of the model and algorithm. Originality/Value: This paper established an effective evaluation system based on PCA and GA-BPNN to help venture capital institutions preliminarily screen potential technology start-ups, which provides the possibility for venture capital institutions to greatly reduce the screening time and cost, improve the screening efficiency of TSs, and scientifically assess the risk of investee projects or investee enterprises to obtain sustainable and stable excess profits.

Suggested Citation

  • Jiaxin Li & Mingming Meng & Xin Liu & Yanjie Lv & Jian Yu, 2024. "Evaluation and screening of technology start-ups based on PCA and GA-BPNN," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-21, February.
    • Handle: RePEc:plo:pone00:0289691
    DOI: 10.1371/journal.pone.0289691
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0289691
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0289691&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0289691?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
    ---><---

    References listed on IDEAS

    as
    1. Boyoung Kim & Hyojin Kim & Youngok Jeon, 2018. "Critical Success Factors of a Design Startup Business," Sustainability, MDPI, vol. 10(9), pages 1-15, August.
    2. 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).
    3. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    4. Mario Tirelli, 2021. "On the optimal investment finance of small businesses," Small Business Economics, Springer, vol. 56(4), pages 1639-1665, April.
    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. Honjo, Yuji & Ikeuchi, Kenta & Nakamura, Hiroki, 2024. "Does risk aversion affect individuals’ interests and actions in angel investing? Empirical evidence from Japan," Japan and the World Economy, Elsevier, vol. 70(C).
    2. Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    3. Lin Wang & Wuyue An & Feng‐Ting Li, 2024. "Text‐based corn futures price forecasting using improved neural basis expansion network," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2042-2063, September.
    4. Ren, Siyu & Hao, Yu & Wu, Haitao, 2022. "The role of outward foreign direct investment (OFDI) on green total factor energy efficiency: Does institutional quality matters? Evidence from China," Resources Policy, Elsevier, vol. 76(C).
    5. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    6. Zhiyu Lv & Xu Zhang, 2023. "Influencing Factor Analysis on Energy-Saving Refrigerator Purchases from the Supply and Demand Sides," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    7. Jin Kuang & Tse-Chen Chang & Chia-Wei Chu, 2022. "Research on Financial Early Warning Based on Combination Forecasting Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    8. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    9. Hu, Jing & Zhong, Lejia & Qiao, Jingyuan, 2024. "Equity pledge and debt financing of listed companies," Finance Research Letters, Elsevier, vol. 59(C).
    10. Beatrice Karitu & Wanja Wangondu & Stephen Muathe, 2022. "A theoretical route towards conceptualization of start-ups in emerging markets: A Kenyan perspective," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 11(4), pages 448-457, June.
    11. Nascimento, Erick Giovani Sperandio & de Melo, Talison A.C. & Moreira, Davidson M., 2023. "A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy," Energy, Elsevier, vol. 278(C).
    12. Lu Peng & Sheng‐Xiang Lv & Lin Wang, 2024. "Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2064-2087, September.
    13. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    14. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
    15. Zhao, Yongning & Zhao, Yuan & Liao, Haohan & Pan, Shiji & Zheng, Yingying, 2025. "Interpreting LASSO regression model by feature space matching analysis for spatio-temporal correlation based wind power forecasting," Applied Energy, Elsevier, vol. 380(C).
    16. Eulalia Skawińska & Romuald I. Zalewski, 2020. "Success Factors of Startups in the EU—A Comparative Study," Sustainability, MDPI, vol. 12(19), pages 1-28, October.
    17. Shengcai Zhang & Changsheng Zhu & Xiuting Guo, 2024. "Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model," Energies, MDPI, vol. 17(9), pages 1-22, April.
    18. Anh Tuan Dang & Nguyen Ngoc Khanh Dung, 2024. "The Impact of Covid-19 on Earnings Management: Empirical Evidence from Vietnam," SAGE Open, , vol. 14(3), pages 21582440241, July.
    19. Gao, Xiaoguang & Luo, Juncheng & Zhao, Qiang, 2025. "Impact of equity pledges on analysts earnings forecasts," Finance Research Letters, Elsevier, vol. 71(C).
    20. Zhang, Chu & Qiao, Xiujie & Zhang, Zhao & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator," Energy, Elsevier, vol. 295(C).

    More about this item

    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:plo:pone00:0289691. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.