Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory
This work addresses stock market prediction for investors, but it is incremental as it combines existing methods without introducing a fundamentally new approach.
The paper tackles stock market trend prediction by applying Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) networks, comparing four hybrid methods (GMM-HMM, XGB-HMM, GMM-HMM+LSTM, XGB-HMM+LSTM) to identify the best for timing strategies.
This paper intends to apply the Hidden Markov Model into stock market and and make predictions. Moreover, four different methods of improvement, which are GMM-HMM, XGB-HMM, GMM-HMM+LSTM and XGB-HMM+LSTM, will be discussed later with the results of experiment respectively. After that we will analyze the pros and cons of different models. And finally, one of the best will be used into stock market for timing strategy.