A PROBABILISTIC PRICE FORECASTING GUIDE WITH TECHNICAL ANALYSIS
Hidden Markov Model- A Statespace Probabilistic Forecasting Approach in Quantitative Finance
Application of Gaussian Mixture Model for Regime detection using historical NASDAQ Index time-series data
https://sarit-maitra.medium.com/membership
Hidden Markov Models (HMM) are proven for their ability to predict and analyze time-based phenomena and this makes them quite useful in financial market prediction. HMM can be considered mix of Brownian movements consisting of hidden layers and observed layers and comprising of sequence of events. In quantitative finance, the states of a system can be modeled as a Markov chain in which each state depends on the previous state in a non-deterministic way. In HMM these states are invisible, while observations which are the inputs of the model and depend on the visible states. HMM is typically used to predict the hidden regimes of observation data. The mathematical foundations of HMM were developed by Baum and Petrie in 1966.
The big question here is that, can we use the performances of stocks in the past to predict their future performances? The data with index seems to have similar behaviors on…
