  Stock market forecasting is a classic problem that has been thoroughly
investigated using machine learning and artificial neural network based tools
and techniques. Interesting aspects of this problem include its time reliance
as well as its volatility and other complex relationships. To combine them,
hidden markov models (HMMs) have been utilized to anticipate the price of
stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for
predicting stock prices for the next day based on previous data. An HMM is
trained by analyzing the fractional change in the stock price as well as the
intraday high and low values. It is then utilized to produce a MAP estimate
across all possible stock prices for the next day. The approach demonstrated in
our work is quite generalized and can be used to predict the stock price for
any company, given that the HMM is trained on the dataset of that company's
stocks dataset. We evaluated the accuracy of our models using some extensively
used accuracy metrics for regression problems and came up with a satisfactory
outcome.
