Abstract
As the structure of China’s security market gradually moves from a brokerage-centered model to a more integrated system of wealth management and capital intermediation, quantitative investing and program trading are increasingly playing important roles in prop trading, market making, derivatives, and asset management. Based on the financial statements and disclosures of Changjiang Securities from 2015 to 2024, this paper constructs an indicator of the intensity of the quantitative investing business and examines the relationship between quantitative investing and revenue growth, return on equity, and the composition of revenue, while also checking the changes in risk-adjusted performance with the Sharpe ratio and maximum drawdown. It’s found that in 2024, the quant-related revenue contributed 15%-20% of the total income, program trading contributed 30% of the total trading-related results, and management fees contributed 40% of the total revenue, while the quant business achieved an annual return of 12%-15%. It also discusses the impact of the tightening regulations on the extra benefits of the quantitative investing business and the governance and risk management lessons for security firms.
References
[1] Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-frequency trading and price discovery. Review of Financial Studies, 27(8), 2267–2306.
[2] Changjiang Securities Co., Ltd. (2025, April 29). 2024 annual report summary [Annual report].
[3] China Securities Regulatory Commission. (2024, May 11). [Announcement No. 8: Administrative rules for program trading in the securities market (Trial)] [Policy document].
[4] China Securities Regulatory Commission. (2024, May 15). [CSRC releases the administrative rules for program trading in the securities market (Trial)] [Press release].
[5] DeYoung, R., & Rice, T. (2004). Noninterest income and financial performance at U.S. commercial banks. Financial Review, 39(1), 101–127.
[6] Hasbrouck, J., & Saar, G. (2013). Low-latency trading. Journal of Financial Markets, 16(4), 646–679.
[7] Hendershott, T., Jones, C. M., & Menkveld, A. J. (2011). Does algorithmic trading improve liquidity? The Journal of Finance, 66(1), 1–33.
[8] Lo, A. W. (2002). The statistics of Sharpe ratios. Financial Analysts Journal, 58(4), 36–52.
[9] Menkveld, A. J. (2013). High frequency trading and the new-market makers. Journal of Financial Markets, 16(4), 571–603.
[10] Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.
[11] Reuters. (2024, June 7). China plans higher transaction fees for high-frequency trading. Reuters.
[12] Shanghai Stock Exchange. (2025, April 3). [Implementation rules for program trading management of SSE] [Policy document]. (Effective July 7, 2025).
[13] Tong, X., & Yang, W. (2025). Empirical analysis of the impact of financial technology on the profitability of listed banks. International Review of Economics & Finance, 98, 103788.
[14] Yang, D., Yang, Y., Luo, J, et al. (2025). Research on the impact of algorithmic trading on market volatility. Scientific Reports, 15, 30073.

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