Algorithmic Trading with Learning: Informed versus Uninformed
Seminar Room 2, Newton Institute Gatehouse
AbstractHigh-frequency traders often take a view on the market and then act accordingly: buy an asset if they predict an upward trend or sell an asset if they expect a downward trend. However, if they are not fully confident in their prediction, how can they optimally trade? Here, we develop a framework to address this problem by first modeling the asset mid-price with a randomized Brownian bridge. The randomization encodes the trader's prior estimate of the asset's future midprice distribution, e.g., a two point discrete random variable corresponds to upward/downward movements. We pose and solve the optimal control and stopping problem for how the trader should post limit orders at the touch and/or cross the spread and execute market orders. The optimal trading strategy indeed learns from the dynamics of the asset's midprice which trend is being realized and modifies its behavior accordingly. By comparing the performance three traders who differ in the accuracy of their predictions and whether they learn or not, we demonstrate that traders can significantly benefit from using our approach.
Authors: Alvaro Cartea, Ryan Donnelly, Sebastian Jaimungal