Fetching the paper…
Reading the bibliography…
Modern financial exchanges use an electronic limit order book (LOB) to store bid and ask orders for a specific financial asset.
Explaining agent-based financial market simulation
David Byrd. 2019 · 1909
Earlier work this paper cites.
Efficient capital markets: II
Eugene F Fama. 1991 · 1991
Earlier work this paper cites.
Mosaic organization of DNA nucleotides
C-K Peng, Sergey V Buldyrev, Shlomo Havlin, Michael Simons, H Eugene Stanley, and Ary L Goldberger. 1994 · 1994
Earlier work this paper cites.
Herd behavior in financial markets
Sushil Bikhchandani and Sunil Sharma. 2000 · 2000
Earlier work this paper cites.
More stylized facts of financial markets: leverage effect and downside correlations
Jean-Philippe Bouchaud and Marc Potters. 2001 · 2001
Earlier work this paper cites.
Empirical properties of asset returns: stylized facts and statistical issues
Rama Cont. 2001 · 2001
Earlier work this paper cites.
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Ilya M Sobol. 2001 · 2001
Earlier work this paper cites.
Statistical properties of stock order books: empirical results and models
Jean-Philippe Bouchaud, Marc Mézard, and Marc Potters. 2002 · 2002
Earlier work this paper cites.
Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management (2nd ed.)
Jean-Philippe Bouchaud and Marc Potters. 2003 · 2003
Earlier work this paper cites.
Master curve for price-impact function
Fabrizio Lillo, J Doyne Farmer, and Rosario N Mantegna. 2003 · 2003
Earlier work this paper cites.
The long memory of the efficient market
Fabrizio Lillo and J Doyne Farmer. 2004 · 2004
Earlier work this paper cites.
Order book characteristics and the volume–volatility relation: Empirical evidence from a limit order market
Randi Næs and Johannes A Skjeltorp. 2006 · 2006
Earlier work this paper cites.
Volatility clustering in financial markets: empirical facts and agent-based models
Rama Cont. 2007 · 2007
Earlier work this paper cites.
Statistical analysis of financial returns for a multiagent order book model of asset trading
Tobias Preis, Sebastian Golke, Wolfgang Paul, and Johannes J Schneider. 2007 · 2007
Earlier work this paper cites.
A statistical physics view of financial fluctuations: Evidence for scaling and universality
H Eugene Stanley, Vasiliki Plerou, and Xavier Gabaix. 2008 · 2008
Earlier work this paper cites.
Emergence of long memory in stock volatility from a modified Mike-Farmer model
Gao-Feng Gu and Wei-Xing Zhou. 2009 · 2009
Earlier work this paper cites.
Profitability of technical stock trading: Has it moved from daily to intraday data?
Stephan Schulmeister. 2009 · 2009
Earlier work this paper cites.
Herding and information based trading
Rhea Tingyu Zhou and Rose Neng Lai. 2009 · 2009
Cited alongside, same era.
A stochastic model for order book dynamics
Rama Cont, Sasha Stoikov, and Rishi Talreja. 2010 · 2010
Cited alongside, same era.
A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts
Olivier Brandouy, Angelo Corelli, Iryna Veryzhenko, and Roger Waldeck. 2012 · 2012
Cited alongside, same era.
Measuring high-frequency causality between returns, realized volatility, and implied volatility
Jean-Marie Dufour, René Garcia, and Abderrahim Taamouti. 2012 · 2012
Cited alongside, same era.
Linking agent-based models and stochastic models of financial markets
Ling Feng, Baowen Li, Boris Podobnik, Tobias Preis, and H Eugene Stanley. 2012 · 2012
Cited alongside, same era.
The competitive landscape of high-frequency trading firms
Ekkehart Boehmer, Dan Li, and Gideon Saar. 2018 · 2018
Later among the works it cites.
Contrarian strategy and herding behaviour in the Chinese stock market
Qiwei Chen, Xiuping Hua, and Ying Jiang. 2018 · 2018
Later among the works it cites.
Agent-Based Model Exploration of Latency Arbitrage in Fragmented Financial Markets. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, New York, NY, USA, 2312–2320
Matthew Duffin and John Cartlidge. 2018 · 2018
Later among the works it cites.
High frequency trading strategies, market fragility and price spikes: an agent based model perspective
Frank McGroarty, Ash Booth, Enrico Gerding, and VL Chinthalapati. 2019 · 2019
Later among the works it cites.
Do the limit orders of proprietary and agency algorithmic traders discover or obscure security prices?
Samarpan Nawn and Ashok Banerjee. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mark Paddrik, Roy Hayes, Andrew Todd, Steve Yang, Peter Beling, and William Scherer. 2012 · 2012
Cited alongside, same era.
Agent-based model with asymmetric trading and herding for complex financial systems
Jun-Jie Chen, Bo Zheng, and Lei Tan. 2013 · 2013
Cited alongside, same era.
Price dynamics in a Markovian limit order market
Rama Cont and Adrien De Larrard. 2013 · 2013
Cited alongside, same era.
Limit order books
Martin D Gould, Mason A Porter, Stacy Williams, Mark McDonald, Daniel J Fenn, and Sam D Howison. 2013 · 2013
Cited alongside, same era.
HFT and market quality
Bruno Biais, Thierry Foucault, et al · 2014
Cited alongside, same era.
The price impact of order book events
Rama Cont, Arseniy Kukanov, and Sasha Stoikov. 2014 · 2014
Cited alongside, same era.
High frequency trading in the Korean index futures market
Eun Jung Lee. 2015 · 2015
Cited alongside, same era.
ABIDES: Towards High-Fidelity Multi-Agent Market Simulation. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (Miami, FL, Spain) (SIGSIM-PADS ’20) . Association for Computing Machinery, New York, NY, USA, 11–22
David Byrd, Maria Hybinette, and Tucker Hybinette Balch. 2020 · 2020
Later among the works it cites.
Multi-agent reinforcement learning in a realistic limit order book market simulation. In Proceedings of the First ACM International Conference on AI in Finance . Association for Computing Machinery, New York, NY, USA, 1–7
Michaël Karpe, Jin Fang, Zhongyao Ma, and Chen Wang. 2020 · 2020
Later among the works it cites.
Generating realistic stock market order streams. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34(01). AAAI Press, Palo Alto, CA, 727–734
Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, and Michael Wellman. 2020 · 2020
Later among the works it cites.
High-frequency trading and systemic risk: A structured review of findings and policies
Antonio Sánchez Serrano. 2020 · 2020
Later among the works it cites.
Get real: Realism metrics for robust limit order book market simulations. In Proceedings of the First ACM International Conference on AI in Finance . ACM, New York, NY, 1–8
Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, and Tucker Balch. 2020 · 2020
Later among the works it cites.
On the feasibility of automating stock market manipulation. In Annual Computer Security Applications Conference . Association for Computing Machinery, New York, NY, USA, 277–290
Carter Yagemann, Simon P Chung, Erkam Uzun, Sai Ragam, Brendan Saltaformaggio, and Wenke Lee. 2020 · 2020
Later among the works it cites.
Deep Hawkes Process for High-Frequency Market Making
Pankaj Kumar. 2021 · 2021
Later among the works it cites.
The Limit Order Book Recreation Model (LOBRM): An Extended Analysis. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, Cham, Switzerland, 204–220
Zijian Shi and John Cartlidge. 2021 · 2021
Later among the works it cites.
The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35(1). AAAI Press, Palo Alto, CA, 548–556
Zijian Shi, Yu Chen, and John Cartlidge. 2021 · 2021
Later among the works it cites.
Spoofing the limit order book: A strategic agent-based analysis
Xintong Wang, Christopher Hoang, Yevgeniy Vorobeychik, and Michael P Wellman. 2021 · 2021
Later among the works it cites.
Learning to simulate realistic limit order book markets from data as a World Agent. In Proceedings of the Third ACM International Conference on AI in Finance . Association for Computing Machinery, New York, NY, USA, 428–436
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko, and Tucker Balch. 2022 · 2022
Later among the works it cites.
State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . Association for Computing Machinery, New York, NY, USA, 1607–1615
Zijian Shi and John Cartlidge. 2022 · 2022
Later among the works it cites.