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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
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    References listed on IDEAS

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    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. Lova, Antonio & Tormos, Pilar & Cervantes, Mariamar & Barber, Federico, 2009. "An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes," International Journal of Production Economics, Elsevier, vol. 117(2), pages 302-316, February.
    4. 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).
    5. Mario Tirelli, 2021. "On the optimal investment finance of small businesses," Small Business Economics, Springer, vol. 56(4), pages 1639-1665, April.
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    Cited by:

    1. Zhai, Dongsheng & Zhao, Kai & Wang, Ming & Zhai, Liang & Xu, Shuo, 2026. "AI-driven opportunity forecasting for technology startup identification: Integrating graph embedding, LLMs, and informetric analysis," Technological Forecasting and Social Change, Elsevier, vol. 227(C).

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