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Note: The paper was published in 2011 and has been discussed and critiqued since that time. Some market participants have raised questions about its methodology and efficacyd.

Electronic markets have emerged as popular venues for the trading of a wide variety of financial assets, and computer based algorithmic trading has also asserted itself as a dominant force in financial markets across the world. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators.

The authors of a paper propose to characterize traders' behavior in terms of the reward functions most likely to have given rise to the observed trading actions. Their approach is to model trading decisions as a Markov Decision Process (MDP), and use observations of an optimal decision policy to find the reward function. This is known as Inverse Reinforcement Learning (IRL).

A IRL-based approach to characterizing trader behavior strikes a balance between two desirable features in that it captures key empirical properties of order book dynamics and yet remains computationally tractable. Using an IRL algorithm based on linear programming, the authors are able to achieve more than 90% classification accuracy in distinguishing high frequency trading from other trading strategies in experiments on a simulated E-Mini S&P 500 futures marke......................

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