Optimal order placement in limit order markets
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DOI: 10.1080/14697688.2016.1190030
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References listed on IDEAS
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Cited by:
- Mohammad Zare & Omid Naghshineh Arjmand & Erfan Salavati & Adel Mohammadpour, 2021. "An Agent‐Based model for Limit Order Book: Estimation and simulation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1112-1121, January.
- Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
- Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
- Johannes Muhle‐Karbe & Zexin Wang & Kevin Webster, 2023. "A Leland model for delta hedging in central risk books," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 504-547, July.
- Timoth'ee Fabre & Vincent Ragel, 2023. "Interpretable ML for High-Frequency Execution," Papers 2307.04863, arXiv.org, revised Sep 2024.
- Kashyap, Ravi, 2020. "David vs Goliath (You against the Markets), A dynamic programming approach to separate the impact and timing of trading costs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
- Wu, Liang & Yan, Xin & Fu, Zhiming & Zhang, Rui, 2019. "Do investors choose trade-size according to liquidity, empirical evidence from the S&P 500 index future market," Finance Research Letters, Elsevier, vol. 28(C), pages 275-280.
- Nils Bundi & Ching-Lin Wei & Khaldoun Khashanah, 2024. "Optimal trade execution in cryptocurrency markets," Digital Finance, Springer, vol. 6(2), pages 283-318, June.
- Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
- Schnaubelt, Matthias, 2020. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," FAU Discussion Papers in Economics 05/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
- Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
- Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
- Johannes Muhle-Karbe & Eyal Neuman & Yonatan Shadmi, 2024. "Fluid-Limits of Fragmented Limit-Order Markets," Papers 2407.04354, arXiv.org.
- Bastien Baldacci & Iuliia Manziuk, 2020. "Adaptive trading strategies across liquidity pools," Papers 2008.07807, arXiv.org.
- Xianfei Hui & Baiqing Sun & Hui Jiang & Indranil SenGupta, 2021. "Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters," Papers 2101.08984, arXiv.org, revised Feb 2022.
- Anh Tu Le & Thai-Ha Le & Wai-Man Liu & Kingsley Y. Fong, 2023. "Dynamic limit order placement activities and their effects on stock market quality," Annals of Operations Research, Springer, vol. 330(1), pages 155-175, November.
- Bastien Baldacci & Jerome Benveniste & Gordon Ritter, 2020. "Optimal trading without optimal control," Papers 2012.12945, arXiv.org.
- Andrew Y. Chen & Mihail Velikov, 2020. "Zeroing in on the Expected Returns of Anomalies," Finance and Economics Discussion Series 2020-039, Board of Governors of the Federal Reserve System (U.S.).
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