Stock market forecasting using hidden Markov model: a new approach
By Md. Rafiul Hassan, Baikunth Nath. 2005
422 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Arash Ghanbari
- Hassan Shavandi
- Esmaeil Hadavandi
- Jimmy J. Lin
- Chris Dyer
- Yosi Keller
- A. Navon
- Michael Kirley
In Signal Processing
- Arash Ghanbari
- Hassan Shavandi
- Esmaeil Hadavandi
- Jimmy J. Lin
- Chris Dyer
- Yosi Keller
- A. Navon
- Michael Kirley
Citations in the corpus
- Financial Time Series Prediction Using Deep Learning (2017, methodology): “Hassan [12] applied HMMs to forecasting stock market, by training an HMM model on a specific stock, and matching past temporal patterns to current stocks patterns.”
- A fusion model of HMM, ANN and GA for stock market forecasting (2007, methodology): “Using weighted average to forecast stock price In the HiMMI model a single match of current day's stock behaviour is extracted from historical data and then price difference is calculated (Hassan & Nath, 2005).”
- Short-term stock market price trend prediction using a comprehensive deep learning system (2020, background): “Hassan and Nath in [9] applied the Hidden Markov Model (HMM) on the stock market forecasting on stock prices of four different Airlines.”
- Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting (2010, methodology): “The comparison showed that forecasting ability of the fusion model is better than ARIMA model and HMM proposed in [19].”
- Computational Visual Analysis of the Order Book Dynamics for Creating High-frequency Foreign Exchange Trading Strategies (2015, background): “Hassan [5] is one of the first authors who extracted HMMs from speech and image recognition problems and placed them in the stock price prediction domain.”
- Stock Market Embedding and Prediction: A Deep Learning Method (2018, methodology): “Hassan [20] applied HMMs to forecasting stock market, by training an HMM model on a specific stock and matching past temporal pattern to current stocks patterns.”
- Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, methodology): “These simple but powerful models have been used in applications as diverse as speech recognition [54], information extraction [96], gene finding [100], part of speech tagging [30], stock market forecasting [48], and word alignment of parallel (translated) texts [106].”