Simulated RL market makers with dynamic inventory penalties and Pareto-front multi-objective training outperform baseline market makers, and a discounted Thompson sampling policy switcher handles non-stationary markets.
Market making and mean reversion,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Market Making Strategies with Reinforcement Learning
Simulated RL market makers with dynamic inventory penalties and Pareto-front multi-objective training outperform baseline market makers, and a discounted Thompson sampling policy switcher handles non-stationary markets.