Research Review of Quantitative Investment Strategies based on NASDAQ
With a mature trading mechanism and technology-focused underlying assets, the NASDAQ market has become the global core of quantitative investment, where quantitative trading accounts for 75% of total turnover as of 2025. Existing studies mostly focus on single strategy types or specific technical models, lacking a comprehensive analytical framework for quantitative strategies tailored to NASDAQ’s unique characteristics. This paper adopts an integrative systematic literature review method to sort out the market foundation, research progress, and application features of five core quantitative strategies-multi-factor stock selection, statistical arbitrage, trend-following strategies, high-frequency trading/market-making strategies, and machine-learning-based strategies-in NASDAQ, integrating empirical results of cutting-edge models including Autoencoder-GSVM and genetic algorithms. It further analyzes core challenges of strategy implementation, and finally puts forward cognitive conclusions, practical suggestions and future research directions, providing a panoramic reference for theoretical research and practice of NASDAQ quantitative investment.
References
- Gawer, A. (2022). Digital platforms and ecosystems: Remarks on the dominant organizational forms of the digital age. Innovation, 24 (1), 110–124. https://doi.org/10.1080/14479338.2021.1965888
- Xu, W., Chen, X., Dong, Y., et al. (2021). Impact of decision rules and non cooperative behaviors on minimum consensus cost in group decision making. Group Decision and Negotiation, 30, 1239–1260. https://doi.org/10.1007/s10726 020 09653 7
- Chen, C., & Zhu, X. (2024). Design and application of a model quantitative investment strategy based on the fusion of boosting algorithms under the stacking framework. Journal of Econometrics, 4(2), 425–441.
- Zhang, J. T. (2011). Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices. Technometrics, 53(4), 426–439. https://doi.org/10.1198/TECH.2011.10128
- Ren, S., & Song, W. (2024). Feature extraction and prediction of multidimensional time series based on the GGInformer model. Computer Engineering and Science, 46(4), 590–598.
- Wang, Q., & Hu, D. (2022). Optimization of the Spark based K means fast clustering algorithm. Computer Simulation, 39(3), 344–349.
- Li, Y. (2023). A study on machine learning based quantitative investment strategies for A shares fundamentals. Financial Guest, (8), 34–36.
- Ye, K., Ran, J., & Chen, M. (2022). Investment risk assessment for private equity funds based on empirical probability distributions. Mathematical Statistics and Management, 41(3), 541–553. https://doi.org/10.13860/j.cnki.sltj.20220331 008
- Luo, J. (2022). A multi factor stock selection model based on industry rotation strategy and an empirical study of its investment performance. Science & Technology Information, 20(20), 5.
- Lai, X., & Guo, Z. (2024). Quantitative analysis of legal risks and regulatory recommendations for cross border financial data flow. Financial Regulatory Research, (4), 1–19. https://doi.org/10.13490/j.cnki.frr.2024.04.005