arXiv q-fin· Irene Aldridge, Lourenco Belleau-Bhowmik, James Chan, Josh Chang, Kelly Deng, Pranav Dhingra, Xinda Ding, Haodong Fang, Satya Hanumanchi, Carter He, Shuang He, Yadong Hou, Vallab Karanam, Rithvik Kasarla, Tishya Khanna, Jihyun Kim, Sarah Konety, Abhinav Kotta, Anderson Lo, Jake Luther, Omar Mian, Ishan Patwardhan, Matteo Perona, Chenyue Qi, Shashank Ramachandran, Shubham Rateria, Jake Swain, David Thai, Michael Twersky, Susan Wu, Dean Xu, Helenna Yin, Jiachun Zheng·· 2 days agoAI Score47
AI Agents in Automated Markets
Agentic AI in Automated Markets
AI summary
The research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.
Source:arXiv q-fin · arxiv.org
Machine LearningLarge Language Model ApplicationsCrypto AssetsHigh-Frequency and Market MicrostructureRisk ManagementPaper Research#Papers / Research#Large Language Model Applications#Machine Learning#Crypto Assets#High-Frequency / Market Microstructure#Risk Management