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Machine Learning

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Risk-Averse Reinforcement Learning for Algorithmic Trading
Citation key Shen2014a
Author Shen, Y. and Huang, R. and Yan, C. and Obermayer, K.
Title of Book 2014 IEEE Computational Intelligence for Financial Engineering and Economics
Pages 391-398
Year 2014
DOI 10.1109/CIFEr.2014.6924100
Abstract We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.
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