Investment advice is essential in individual and/or corporate financial planning. The goal of this paper was to investigate viable analytical models for investment advisors. This study has found that Bayesian modelling has a lot of potential for providing useful analytical tools that may include historical/domain knowledge through proper prior considerations. This study gives a practical introduction to defining, estimating and evaluating single-level linear regression models in a Bayesian context. The R package rstanarm for Bayesian applied regression modelling was utilized in this work. The goal is to build a single-level regression model using the stan glm function, using the data from an investment consultant. The variable call amount predicts the response net return linearly. When the call amount increases by one unit, the mean net return value rises. Several models have been achieved by adding more predictors and the results are obtained.

Bayesian modelling, linear regression, rstanarm.

Received: January 6, 2022; Accepted: January 14, 2022; Published: January 28, 2022

How to cite this article: S. Mythreyi Koppur and Dr. B. Senthilkumar, A Bayesian regression modelling on investment advisory using rstanarm, Advances in Probability, Stochastic Processes and Applied Statistics 1 (2022), 13-25. DOI: 10.17654/PAS2022002

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License 

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