Application of Bayesian Regression Model in Financial Stock Market Forecasting

Abstract

The Bayesian method is a statistics field targeting the Bayes theorem in interpreting probabilities. The Bayesian formula provides an insight into conditional probability based on present data and prior information. Due to the efficiency of the Bayesian model in predicting future outcomes, the model is integrated with regression analysis which is a set of statistical methods utilized for estimating relationships between dependent and independent variables. Bayesian regression analysis is a reliable model for investigating variables having a significant impact on the output of a particular process, such as financial stock market forecasting considered in this research. To fulfill the study's aim, the research adopts secondary research on published journals, case studies, and reports documented by scholars in the field. Due to the stochastic nature of stock market variables, inadequate data or poorly dispersed data can be addressed using Bayesian linear regression allowing investors to make better decisions and cut larger profit margins. The vector autoregression and the classical frequentist approach achieve a higher probability accuracy than non-Bayesian methods such as the Auto-Regressive and Moving Average Model time series models. The author found that by studying the vector Bayesian autoregressive prediction model, it is possible to analyze how investors use the Bayesian model to predict stock market volatility.