Stock market prediction has been one of the more active research areas in the past, given the obvious interest of a lot of major companies. Historically, various machine learning algorithms have been applied with varying degrees of success. However, stock forecasting is still severely limited due to its non-stationary, seasonal, and unpredictable nature. Predicting forecasts from just the previous stock data is an even more challenging task since it ignores several outlying factors.
As seen previously, HMMs are capable of modeling hidden state transitions from the sequential observed data. The problem of stock prediction can also be thought as following the same pattern. The price of the stock depends upon a multitude of factors which generally remain invisible to the investor (hidden variables). The transition between the underlaying factors change...