Quantitative Trading Strategy of Market States Prediction Based on HMM

DOI

10.2991/msmee-17.2017.245How to use a DOI?

Keywords

Hidden Markov models, Market states prediction, Trading strategy.

Abstract

Hidden Markov model has been widely applied in various fields and has obtained excellent effects. In this paper, we verify the feasibility of applying HMM to quantitative finance and the potential to obtain stable profits and detect coming bear market to avoid sharp falling process. We creatively make full use of raw data and list a few candidate features. Corresponding feature selection method, which uses HMM itself to test performance on each single feature, has been proposed. The comprehensive model is trained using selected features and is tested performance on CSI 300 index in Chinese A-stock market and S&P 500 index in American stock market. Experiments on both markets illustrate that HMM has great ability to identify market states and obtain excess return. And HMM-based strategy has better stability and profitability compared with strategies based on double-MA and K-means. HMM is appropriate to be applied as the core of quantitative strategies to judge the trends of financial markets.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhongjie Fu
AU  - Qingqiang Wu
PY  - 2017/05
DA  - 2017/05
TI  - Quantitative Trading Strategy of Market States Prediction Based on HMM
BT  - Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)
PB  - Atlantis Press
SP  - 1309
EP  - 1319
SN  - 2352-5401
UR  - https://doi.org/10.2991/msmee-17.2017.245
DO  - 10.2991/msmee-17.2017.245
ID  - Fu2017/05
ER  -