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Stock market forecasting is a classic problem that has been thoroughlyinvestigated using machine learning and artificial neural network based toolsand techniques. Interesting aspects of this problem include its time relianceas well as its volatility and other complex relationships. To combine them,hidden markov models (HMMs) have been utilized to anticipate the price ofstocks. We demonstrated the Maximum A Posteriori (MAP) HMM method forpredicting stock prices for the next day based on previous data. An HMM istrained by analyzing the fractional change in the stock price as well as theintraday high and low values. It is then utilized to produce a MAP estimateacross all possible stock prices for the next day. The approach demonstrated inour work is quite generalized and can be used to predict the stock price forany company, given that the HMM is trained on the dataset of that company'sstocks dataset. We evaluated the accuracy of our models using some extensivelyused accuracy metrics for regression problems and came up with a satisfactoryoutcome.

DSE Stock Price Prediction using Hidden Markov Model