A Probabilistic Model for Predicting Stock Price Movements Using Bayesian Inference

Predicting stock price movements is a challenging task due to the complex and dynamic nature of financial markets. In this paper, we propose a probabilistic model for predicting stock price movements using Bayesian inference. Our approach leverages historical stock price data to estimate the parameters of a Bayesian model, which captures the underlying probabilistic relationships between relevant market variables and stock price changes. We begin by formulating a Bayesian framework for modeling stock price movements, incorporating prior beliefs about the distribution of market variables and updating these beliefs based on observed data. We use techniques such as Markov chain Monte Carlo (MCMC) sampling to infer the posterior distribution of model parameters, allowing us to make probabilistic predictions about future stock price movements.