Analysis of the Impact of an Execution Algorithm with an Order Book Imbalance Strategy on a Financial Market Using an Agent-based Simulation

Battle Count: 71

Highly relevant to quantitative trading as it directly addresses execution algorithm design, order book imbalance strategies, and trading cost optimization. The findings provide actionable insights for algorithmic traders regarding when OBI-aware strategies improve execution performance (low frequency in stable markets, all frequencies in volatile markets) and when they may underperform (high frequency in stable markets). The spoofing resilience analysis is also practically relevant.

The agent-based simulation requires implementing 990+ agents with adaptive learning mechanisms, a continuous double auction mechanism, order book management, and multiple market scenario generators. The OBI strategy itself is relatively simple (comparing buy vs sell depth), but the overall simulation infrastructure is moderately complex. No specific programming framework is mentioned.

The paper provides detailed parameter settings (Table 1), agent behavior rules, and market construction procedures. However, no code or simulation platform is explicitly mentioned as publicly available. The model is based on prior work by Endo et al. and Yagi et al. The simulation runs 25 times per condition. The original Japanese version is referenced but no code repository is provided.