Improving Fair Trading Mechanisms and Preventing Associated Risks

Abstract

As securities asset management becomes increasingly digital and automated, fair trading controls have expanded beyond basic constraints on trader conduct to comprehensively cover the entire operational workflow. This encompasses instruction generation, master-order management, algorithmic execution, trade allocation, anomaly monitoring, and post-trade review. Drawing on an asset management institution's practical application to launch an advanced algorithmic trading module, alongside related system test reports and fair trading inquiry materials, this paper combines normative and business-process analysis to examine fairness requirements and the specific channels through which risk spreads in multi-portfolio trading. The empirical findings indicate that fair trading risk is primarily concentrated in five critical areas: the consolidation of multi-portfolio instructions, the equitable allocation of partial fills, the precise identification of same-direction and opposite-direction trades, the permissions for manual intervention, and the overall operation of algorithms and information systems. In response to these vulnerabilities, the paper sets out a comprehensive end-to-end rule framework. It refines master-order formation and partial-fill allocation, develops a dual-track monitoring framework for same-direction and opposite-direction trades, strengthens the traceability of manual intervention, and brings algorithmic trading under lifecycle-wide risk management. Furthermore, it proposes robust operational safeguards based on monitoring indicators, periodic analysis, and traceable data, ultimately offering asset management institutions a practical reference for making fair trading controls significantly easier to implement and audit in a modern algorithmic trading environment.