A fusion model of HMM, ANN and GA for stock market forecasting

By Md. Rafiul Hassan, Baikunth Nath, Michael Kirley. 2007

374 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

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Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Investors can leverage ﬁnancial data to make investment decisions, as Hassan et al. [181] have introduced a fusion model of Hidden Markov Model (HMM), NN, and Genetic Algorithm (GA) for stock market prediction.”
A Double-Layer Neural Network Framework for High-Frequency Forecasting (2017, methodology): “Classic forecasting methods in ﬁnance belong roughly to two categories: the time series models [Chen et al. 2007; Hassan et al. 2007; Hsu et al. 2009; Pai and Lin 2005; Tsai and Hsiao 2010], and the machine learning-based approaches [Kara et al. 2011; Patel et al. 2015; Ar´evalo et al. 2016; Pai…”
Forecasting stock market short-term trends using a neuro-fuzzy based methodology (2009, neutral): “…Thawornwong , 2005 ; Fernandez - Rodriguez , Gonzalez - Martel , & Sosvilla - Rivebo , 2000 ; Halliday , 2004 ; Harvey , Travens , & Costa , 2000 ; Hassan , Nath , & Kirley , 2007 ; Jang , 1992 ; Kim & Chun , 1998 ; Kim , 1998 ; Kimoto , Asakawa , Yoda , & Takeoka , 1990 ; Koulouriotis , Diakoula…”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[224] Stock market forecasting HMM, ANN and GA Conventional HMM, ARIMA Hybrid model performs better”
A Bayesian regularized artificial neural network for stock market forecasting (2013, methodology): “a Models from Hassan et al. (2007)”