Title :
Risk-averse reinforcement learning for algorithmic trading
Author :
Yun Shen ; Ruihong Huang ; Chang Yan ; Obermayer, Klaus
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Tech. Univ. Berlin, Berlin, Germany
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.
Keywords :
commerce; learning (artificial intelligence); market opportunities; risk analysis; NASDAQ; algorithmic trading; risk-averse reinforcement learning; risk-neutral reinforcement learning; Approximation algorithms; Ash; Computer crashes; Educational institutions; Heuristic algorithms; Learning (artificial intelligence); Standards;
Conference_Titel :
Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on
Conference_Location :
London
DOI :
10.1109/CIFEr.2014.6924100
