TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
arXiv:2606.08379v1 Announce Type: cross Abstract: This study addresses the optimal execution of large stock sell programs by introducing TT-DAC-PS (Twin-Target Deterministic Actor-Critic with Policy Smoothing), a deterministic actor-critic architecture that combines twin exponential-moving-average critic targets with pessimistic min backup, TD3-style target policy smoothing noise, delayed actor updates, and conservative Q regularisation to curb overestimation. Exploration uses Ornstein-Uhlenbeck (OU) noise with a hybrid schedule: deterministic episode-wise decay, variance-guided adjustment bas
The ongoing advancement in AI and reinforcement learning research allows for increasingly sophisticated applications in complex financial domains like optimal trade execution.
This development indicates a growing sophistication in AI's ability to manage high-value financial operations, potentially leading to more efficient markets and altered competitive landscapes in trading.
Algorithms are becoming more robust and nuanced in managing large-scale financial transactions, moving beyond simple execution to strategic, risk-mitigated optimal paths.
- · Quantitative trading firms
- · Hedge funds
- · Financial technology providers
- · Institutional investors
- · Traditional high-touch brokers
- · Firms without advanced AI capabilities
- · Manual trade execution desks
More efficient and less market-impactful execution of large orders, potentially reducing transaction costs for institutional players.
Increased adoption of advanced AI-driven execution strategies, concentrating expertise and competitive advantage among firms with deep AI research capabilities.
The development of 'AI versus AI' dynamics in market microstructure, where sophisticated algorithms contend for optimal trade paths, potentially increasing market complexity and requiring new regulatory oversight.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG