Nowadays, market microstructure is completely automated. It allows digital financial data to be easily accessible, stored and transferable, but also manipulated. New disruptive strategies, developed by algorithmic and high frequency traders, raise the question of financial market stability, efficiency but also fairness between investors. We study, in an artificial market environment, the impact of spoofing, a widely used manipulating strategy.
Our evidence should help market regulators to better understand the feedback loop created between spoofing strategies and market dynamics: on one hand, the impact of spoofer on market quality; on the other hand, the profitability of this practice under different market conditions. Our results show that the difficulty of detecting of such manipulations stems from the fleeting nature of spoofing orders and the non-significant impact on extreme price movements. Artificial market allows us to test the profitability of spoofing under different market conditions: average trading volume, volatility of fundamental value, and tick size.
Our study has important implications for the regulation of HFT, as we propose recommendations to regulators in terms of market rules that have not been yet introduced. We show that the random delay of market order execution may significantly reduce the profitability of spoofing without altering market quality.