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- W2003205995 abstract "Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such that it is dynamic with respect to favourable/unfavourable conditions during realtime execution, thereby improving overall cost of trading. We consider the standard Almgren-Chriss model with linear price impact as a candidate base model. This model is popular amongst sell-side institutions as a basis for arrival price benchmark execution algorithms. By training a learning agent to modify a volume trajectory based on the market's prevailing spread and volume dynamics, we are able to improve post-trade implementation shortfall by up to 10.3% on average compared to the base model, based on a sample of stocks and trade sizes in the South African equity market." @default.
- W2003205995 created "2016-06-24" @default.
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- W2003205995 date "2014-03-01" @default.
- W2003205995 modified "2023-09-24" @default.
- W2003205995 title "A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution" @default.
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- W2003205995 doi "https://doi.org/10.1109/cifer.2014.6924109" @default.
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