ALGORITHMIC TRADING WITH LEARNING

Álvaro Cartea (), Sebastian Jaimungal () and Damir Kinzebulatov ()
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Sebastian Jaimungal: Department of Statistical Sciences, University of Toronto, Toronto, Canada
Damir Kinzebulatov: The Fields Institute for Mathematical Sciences, Toronto, Canada

International Journal of Theoretical and Applied Finance (IJTAF), 2016, vol. 19, issue 04, 1-30

Abstract: We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of their predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates their predictions to tweak their strategy. Compared to a trader who cannot learn from market dynamics or from a view of the market, the algorithmic trader’s profits are higher and more certain. Even though the trader executes a strategy based on a directional view, the sources of profits are both from making the spread as well as capital appreciation of inventories. Higher volatility of prices considerably impairs the trader’s ability to learn from price innovations, but this adverse effect can be circumvented by learning from a collection of assets that comove. Finally, we provide a proof of convergence of the numerical scheme to the viscosity solution of the dynamic programming equations which uses new results for systems of PDEs.

Keywords: Algorithmic trading; high frequency trading; nonlinear filtration; Brownian bridge; stochastic optimal control; adverse selection (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (12)

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DOI: 10.1142/S021902491650028X

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