The use of Hidden Markov model and Markov switching model in a trading strategy - Robovský, Tomáš (2023)
The aim of this thesis is to give an introduction to the Hidden Markov models and evaluate them as a predicting tool for financial time series data. The theoretical part involves topics like stochastic process, Markov chain, Forward algorithm, Viterbi algorithm, and Baum-Welch algorithm. In the second part of this thesis, I implemented the Hidden Markov Model based on historical observation sequences (HMHM), as well as the Markov Switching Autoregressive Model (MS-AR). They were then applied to historical market data and evaluated. From the result, it is evident that using predictions of those models as trading signals is not an optimal strategy. But they managed to prove themselves as capable of successful predictions.