An Exploration of Hidden Markov Models with Applications to the Stock Market

A Markov chain is a unique random variable because it is memoryless and the probability of moving to the next state in the process depends only on the current state of the process. The uniqueness of this random variable makes it applicable across a range of topics including two problems in particular discussed in Chapter’s 5 and 6. The focus of this thesis however, is on a specific type of Markov model called a Hidden Markov Model (HMM). The model emits observations that are used to predict the actual state of the model that is unknown. Finally, the paper discusses how a HMM is applied to the stock market in order to help an investor make a decision regarding a particular stock.