Research on Innovation of Ai-Driven Investment Decision-Making Paradigm in Primary Market
DOI:
https://doi.org/10.71222/ed2tw043Keywords:
artificial intelligence, primary market investment, venture capital, private equity, data-driven decision-makingAbstract
The primary market plays a critical role in allocating capital to emerging enterprises and innovative startups, yet traditional investment decision-making faces challenges such as information asymmetry, subjective bias, and inefficiency. This review explores the transformative potential of artificial intelligence (AI) in primary market investment, examining applications in venture capital and private equity across deal sourcing, due diligence, portfolio management, and exit planning. It presents a comprehensive AI-driven investment decision framework that integrates multi-source data acquisition, machine learning, deep learning, natural language processing, and graph-based modeling to enhance predictive accuracy and decision support. Key challenges, including data quality, privacy, interpretability, talent gaps, and ethical considerations, are discussed alongside future directions such as multi-modal data fusion, reinforcement learning, explainable AI, and human-AI collaborative strategies. The review highlights AI’s capacity to systematize, optimize, and intelligentize investment processes, offering new pathways for efficiency, risk management, and strategic value creation in primary market activities.
References
1. A. A. Anuar, M. T. Mohamad, and A. A. Sulaiman, “Artificial intelligence in investment: Analysing variations, functions and market impact,” J. Technol. Manag. Bus., vol. 12, no. 1, pp. 15–32, 2025.
2. A. K. Lui, M. C. Lee, and E. W. Ngai, “Impact of artificial intelligence investment on firm value,” Ann. Oper. Res., vol. 308, no. 1, pp. 373–388, 2022.
3. S. O. Adebiyi, O. O. Ogunbiyi, and B. B. Amole, “Artificial intelligence model for building investment portfolio optimization mix using historical stock prices data,” Rajagiri Manag. J., vol. 16, no. 1, pp. 36–62, 2022.
4. A. V. Rutkauskas, V. Stasytyte, and R. Martinkute-Kauliene, “Seeking the investment portfolio efficiency applying the artificial intelligence,” Transform. Bus. Econ., vol. 20, no. 3, 2021.
5. G. Jangra and M. Jangra, “The role of artificial intelligence in investment management: Enhancing decision-making, efficiency, and alpha generation.”
6. F. G. Ferreira, A. H. Gandomi, and R. T. Cardoso, “Artificial intelligence applied to stock market trading: A review,” IEEE Access, vol. 9, pp. 30898–30917, 2021.
7. S. Mokhtari, K. K. Yen, and J. Liu, “Effectiveness of artificial intelligence in stock market prediction based on machine learning,” arXiv preprint arXiv:2107.01031, 2021.
8. A. M. Rahmani, B. Rezazadeh, M. Haghparast, W. C. Chang, and S. G. Ting, “Applications of artificial intelligence in the economy, including applications in stock trading, market analysis, and risk management,” IEEE Access, vol. 11, pp. 80769–80793, 2023.
9. O. Augoye, A. Adewoyin, O. Adediwin, and A. J. Audu, “The role of artificial intelligence in energy financing: A review of sustainable infrastructure investment strategies,” Int. J. Multidiscip. Res. Growth Eval., vol. 6, no. 2, pp. 277–283, 2025.
10. T. Lim, “Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways,” Artif. Intell. Rev., vol. 57, no. 4, p. 76, 2024.
11. O. H. Fares, I. Butt, and S. H. M. Lee, “Utilization of artificial intelligence in the banking sector: A systematic literature review,” J. Financ. Serv. Mark., vol. 1, 2022.
12. G. Gigante and A. Zago, “DARQ technologies in the financial sector: Artificial intelligence applications in personalized banking,” Qual. Res. Financ. Mark., vol. 15, no. 1, pp. 29–57, 2022.
13. G. Northey, V. Hunter, R. Mulcahy, K. Choong, and M. Mehmet, “Man vs machine: How artificial intelligence in banking influences consumer belief in financial advice,” Int. J. Bank Mark., vol. 40, no. 6, pp. 1182–1199, 2022.
14. N. Hicham, H. Nassera, and S. Karim, “Strategic framework for leveraging artificial intelligence in future marketing decision-making,” J. Intell. Manag. Decis., vol. 2, no. 3, pp. 139–150, 2023.
