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
arXiv 15 Oct 2026 · Econometrics
arXiv:2610.22522 · PDF · Extracted main text
Automated markets increasingly host autonomous, learning trading agents and are powered by formulaic automated market makers (AMMs). We study how heterogeneous agent behavior shapes stress, risk-adjusted performance, liquidity, and volatility in these types of markets. Three behavioral archetypes are modeled as agentic reinforcement learning (RL) systems: a rational arbitrageur, a manipulator, and a retail herd-follower. Each archetype is defined by its reward function within a common, asset-agnostic exposure-control environment, and each is trained with a value-based Deep Q-Network (DQN) and with REINFORCE using a learned baseline. A pilot on hourly cryptocurrency data (AAVE, ETC, SUI) motivates the hypotheses. Herd-followers produce the most frequent but shortest stress episodes, manipulators the fewest but most persistent, and rational agents the most stable rolling Sharpe ratios despite higher return volatility. SHAP attributions tie these patterns to drawdown and cross-asset signals, short-term volatility and position state, and momentum, respectively. The results motivate behavior-specific regulation rather than uniform limits on volatility or turnover.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Barber, Brad M. and Odean, Terrance (2008) All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors | 0.405 | 1 | 1 | 100% |
| 2 | Cong, Lin William and Li, Xi and Tang, Ke and Yang, Yang (2023) Crypto Wash Trading | 0.405 | 1 | 1 | 100% |
| 3 | Deng, Yue and Bao, Feng and Kong, Youyong and Ren, Zhiquan and Dai,… (2016) Deep Direct Reinforcement Learning for Financial Signal Representation and Trading | 0.405 | 1 | 1 | 100% |
| 4 | Gandal, Neil and Hamrick, J. T. and Moore, Tyler and Oberman, Tali (2018) Price Manipulation in the Bitcoin Ecosystem | 0.405 | 1 | 1 | 100% |
| 5 | Glosten, Lawrence R. and Milgrom, Paul R (1985) Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders | 0.405 | 1 | 1 | 100% |
| 6 | Kyle, Albert S (1985) Continuous Auctions and Insider Trading | 0.405 | 1 | 1 | 100% |
| 7 | LeBaron, Blake (2006) Agent-Based Computational Finance | 0.405 | 1 | 1 | 100% |
| 8 | Lundberg, Scott M. and Lee, Su-In (2017) A Unified Approach to Interpreting Model Predictions | 0.405 | 1 | 1 | 100% |
| 9 | Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, A… (2015) Human-Level Control through Deep Reinforcement Learning | 0.405 | 1 | 1 | 100% |
| 10 | Moody, John and Saffell, Matthew (2001) Learning to Trade via Direct Reinforcement | 0.405 | 1 | 1 | 100% |
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