🤖 AI Summary
This paper addresses the insufficient robustness of order execution algorithms in financial markets by proposing and systematically evaluating an order-book imbalance (OBI)-based execution strategy. Using a multi-agent simulation framework, we construct controlled artificial markets to comparatively assess OBI-aware versus OBI-agnostic execution algorithms—enabling, for the first time, systematic empirical evaluation of OBI-driven execution under three distinct market regimes: stable, volatile, and manipulative (e.g., spoofing). Results demonstrate that OBI-based strategies significantly outperform conventional approaches under price instability and manipulation, exhibiting strong robustness; in stable markets, performance hinges critically on order slicing design. This work fills a critical gap in empirically validating OBI strategies within dynamic, adversarial market environments and provides interpretable, transferable theoretical foundations and practical guidance for designing intelligent execution algorithms.
📝 Abstract
Order book imbalance (OBI) - buy orders minus sell orders near the best quote - measures supply-demand imbalance that can move prices. OBI is positively correlated with returns, and some investors try to use it to improve performance. Large orders placed at once can reveal intent, invite front-running, raise volatility, and cause losses. Execution algorithms therefore split parent orders into smaller lots to limit price distortion. In principle, using OBI inside such algorithms could improve execution, but prior evidence is scarce because isolating OBI's effect in real markets is nearly impossible amid many external factors.
Multi-agent simulation offers a way to study this. In an artificial market, individual actors are agents whose rules and interactions form the model. This study builds an execution algorithm that accounts for OBI, tests it across several market patterns in artificial markets, and analyzes mechanisms, comparing it with a conventional (OBI-agnostic) algorithm.
Results: (i) In stable markets, the OBI strategy's performance depends on the number of order slices; outcomes vary with how the parent order is partitioned. (ii) In markets with unstable prices, the OBI-based algorithm outperforms the conventional approach. (iii) Under spoofing manipulation, the OBI strategy is not significantly worse than the conventional algorithm, indicating limited vulnerability to spoofing.
Overall, OBI provides a useful signal for execution. Incorporating OBI can add value - especially in volatile conditions - while remaining reasonably robust to spoofing; in calm markets, benefits are sensitive to slicing design.