Order Placement Strategy: Trade-off between Market Impact and Non-Execution Risk

This contribution proposes an order placement strategy which can be run on simulating continuous financial markets, within an agent-based model framework. In order to improve the efficiency of price discovery, the order placement decision is given by an optimization model which minimizes the risk adjusted execution cost, taking into consideration relevant market microstructure factors such as market impact. The trading behavior of the agents has been extracted from intraday LOB data of Foulad Stock in Tehran Stock Exchange. The market has been simulated for 30 days and the results indicated that the optimized ordering strategy, in terms of the average purchase price of the share, the average waiting time for the transaction of each share and the average volume of the order traded, had better performance in comparison to other strategies examined. We can claim that taking into consideration both non-execution risk and execution cost could raise the performance in comparison to other strategies based on the aggressive level of the traders.