An Intelligent Stock Forecasting System Using a Unify Model of CEFLANN, HMM and GA for Stock Time Series Phenomena

Bebarta, Dwiti Krishna; Sudha, T. Eswari; Bisoyi, Ranjeeta

doi:10.1007/978-3-319-13731-5_53

Cited by 3 publications

(1 citation statement)

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“…Galeshchuk [5] found the neural network with the best forecasting abilities and used it for time-series predictions on exchange market data. Bebarta, Sudha [6] applied ensemble learning and combined artificial neural networks, hidden Markov models and genetic algorithms to construct a framework of a stock forecasting system. In consequence of the difficulty of giving suitable label manually for stock price, unsupervised methods like hidden Markov model have been researched a lot.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Trading Strategy of Market States Prediction Based on HMM

Fu¹,

Wu²

2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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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 Astock 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.

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Section: Introductionmentioning

confidence: 99%