A Study of Quantitative Modeling and Capital Market Efficiency Enhancement in High Frequency Trading

[1] Aquilina, M., Budish, E., & O’neill, P. (2022). Quantifying the high-frequency trading “arms race”. The Quarterly Journal of Economics, 137(1), 493-564. Search in Google Scholar

[2] Du, S., & Zhu, H. (2017). What is the optimal trading frequency in financial markets?. The Review of Economic Studies, 84(4), 1606-1651. Search in Google Scholar

[3] Breckenfelder, J. (2024). Competition among high-frequency traders and market quality. Journal of Economic Dynamics and Control, 104922. Search in Google Scholar

[4] Liu, S., Zhang, C., & Ma, J. (2017). CNN-LSTM neural network model for quantitative strategy analysis in stock markets. In Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, November 14-18, 2017, Proceedings, Part II 24 (pp. 198-206). Springer International Publishing. Search in Google Scholar

[5] Korajczyk, R. A., & Murphy, D. (2019). High-frequency market making to large institutional trades. The Review of Financial Studies, 32(3), 1034-1067. Search in Google Scholar

[6] Huang, B., Huan, Y., Xu, L. D., Zheng, L., & Zou, Z. (2019). Automated trading systems statistical and machine learning methods and hardware implementation: a survey. Enterprise Information Systems, 13(1), 132-144. Search in Google Scholar

[7] Aldridge, I., & Krawciw, S. (2017). Real-time risk: What investors should know about FinTech, high-frequency trading, and flash crashes. John Wiley & Sons. Search in Google Scholar

[8] Chan, E. P. (2021). Quantitative trading: how to build your own algorithmic trading business. John Wiley & Sons. Search in Google Scholar

[9] Brogaard, J., Carrion, A., Moyaert, T., Riordan, R., Shkilko, A., & Sokolov, K. (2018). High frequency trading and extreme price movements. Journal of Financial Economics, 128(2), 253-265. Search in Google Scholar

[10] Ta, V. D., Liu, C. M., & Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), 437. Search in Google Scholar

[11] Seddon, J. J., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307. Search in Google Scholar

[12] Pilbeam, K. (2018). Finance and financial markets. Bloomsbury Publishing. Search in Google Scholar

[13] Pedersen, L. H. (2019). Efficiently inefficient: how smart money invests and market prices are determined. Princeton University Press. Search in Google Scholar

[14] Barth, M. E., Cahan, S. F., Chen, L., & Venter, E. R. (2017). The economic consequences associated with integrated report quality: Capital market and real effects. Accounting, Organizations and Society, 62, 43-64. Search in Google Scholar

[15] Brogaard, J., & Garriott, C. (2019). High-frequency trading competition. Journal of Financial and Quantitative Analysis, 54(4), 1469-1497. Search in Google Scholar

[16] Vo, A., & Yost-Bremm, C. (2020). A high-frequency algorithmic trading strategy for cryptocurrency. Journal of Computer Information Systems, 60(6), 555-568. Search in Google Scholar

[17] Koy, A., & Çolak, A. B. (2024). The intraday high-frequency trading with different data ranges: A comparative study with artificial neural network and vector autoregressive models. Archives of Advanced Engineering Science, 2(3), 123-133. Search in Google Scholar

[18] Liu, Y., Liu, Q., Zhao, H., Pan, Z., & Liu, C. (2020, April). Adaptive quantitative trading: An imitative deep reinforcement learning approach. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 02, pp. 2128-2135). Search in Google Scholar

[19] Kirilenko, A., Kyle, A. S., Samadi, M., & Tuzun, T. (2017). The flash crash: High‐frequency trading in an electronic market. The Journal of Finance, 72(3), 967-998. Search in Google Scholar

[20] Zhou, L., Qin, K., Torres, C. F., Le, D. V., & Gervais, A. (2021, May). High-frequency trading on decentralized on-chain exchanges. In 2021 IEEE Symposium on Security and Privacy (SP) (pp. 428-445). IEEE. Search in Google Scholar

[21] Van Kervel, V., & Menkveld, A. J. (2019). High‐frequency trading around large institutional orders. The Journal of Finance, 74(3), 1091-1137. Search in Google Scholar

[22] Bello, H. O., Ige, A. B., & Ameyaw, M. N. (2024). Deep learning in high-frequency trading: conceptual challenges and solutions for real-time fraud detection. World Journal of Advanced Engineering Technology and Sciences, 12(02), 035-046. Search in Google Scholar

[23] Demertzis, M., Merler, S., & Wolff, G. B. (2018). Capital markets union and the fintech opportunity. Journal of financial regulation, 4(1), 157-165. Search in Google Scholar

[24] Christensen, P. O., & Frimor, H. (2020). Public information and efficient capital investments: Implications for the cost of capital and firm values. The Accounting Review, 95(5), 57-93. Search in Google Scholar

[25] Koepke, R. (2019). What drives capital flows to emerging markets? A survey of the empirical literature. Journal of Economic Surveys, 33(2), 516-540. Search in Google Scholar

[26] Zhou, S., Simnett, R., & Green, W. (2017). Does integrated reporting matter to the capital market?. Abacus, 53(1), 94-132. Search in Google Scholar

[27] Lenee, T. L., & Oki, J. (2017). Capital market development and economic growth: Evidence from the MINT countries. Journal of Economics and Sustainable Development, 8(2), 68-107. Search in Google Scholar

[28] Tao Han,Hanfeng Wen,Nanyu Zhao,Chengcheng Liu,Guozhen Wang,Zhe Wang & Hong Yan. (2024). A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records. Palaeogeography, Palaeoclimatology, Palaeoecology112406-112406. Search in Google Scholar

[29] Alessio Emanuele Biondo,Laura Mazzarino & Alessandro Pluchino. (2024). Trading strategies and Financial Performances: A simulation approach. International Review of Financial Analysis(PB),103426-103426. Search in Google Scholar

[30] Parisa Famil Alamdar & Abbas Seifi. (2024). A deep Q-learning approach to optimize ordering and dynamic pricing decisions in the presence of strategic customers. International Journal of Production Economics109154-. Search in Google Scholar

[31] Qinna Zhu,Haibo Yu,Li Kang & Jianchao Zeng. (2024). A Q-learning driven competitive surrogate assisted evolutionary optimizer with multiple oriented mutation operators for expensive problems. Information Sciences121224-121224. Search in Google Scholar