Financial stock market manipulators can profit illegally by misleading investors about market conditions. For example, in several recent incidents, manipulators successfully spoofed markets by inserting orders that deceived investors about supply or demand for the security. This kind of behavior has increased with the prevalence of algorithmic trading. It imposes substantial harm to the economy, by reducing the efficiency of capital allocation, and more seriously, threatening to compromise the integrity and stability of financial markets. Spoofing is difficult to detect because the underlying actions have legitimate purposes as well as nefarious ones. This project will apply innovative approaches to improve detection and deterrence of market manipulation.
The project will integrate data-driven methods, including calibration of detectors with normal background activity and extraction of manipulation signatures from enhanced time series, with model-based techniques for characterizing manipulation strategies based on strategic analysis of market microstructure. The key idea is to use simulation and optimization to generate successful manipulation strategies for trading models calibrated from available market data streams. These strategies will then be injected into the trading models, to produce enhanced data streams that include labeled manipulation activity. Having labeled activity enables the application of machine learning techniques to extract signatures of spoofing activity, which can be used to construct surveillance and audit algorithms. Methods produced in this project in conjunction with guidance on market design and regulation policy can contribute to reducing the threat from increasingly capable market manipulators.
