Sell-side analyst forecasts are relied upon by investors for their portfolio and risk management decisions. Evaluating such reports’ accuracy is thus important for investors and other stakeholders. In Sidogi et al. (2022), we explored the use of traditional machine learning techniques to model the bidirectional accuracy of sell-side analyst forecasts on the Johannesburg Stock Exchange. This chapter extends this work by framing it within a Bayesian framework and thus introducing a “Bayesian Investment Analyst.” We train a Bayesian logistic regression (BLR) model using the Metropolis-Adjusted Langevin Algorithm, Hamiltonian Monte Carlo, and Separable Shadow Hamiltonian Hybrid Monte Carlo Markov Chain Monte Carlo (MCMC) techniques. We utilize an automatic relevance determination (ARD) prior distribution for our BLR model to create the BLR-ARD model. The BLR-ARD model allows us to automatically rank the most relevant features for determining the bidirectional accuracy of analyst forecasts. We consider four experiments: (1) using the full dataset, (2) a dataset focusing only on resource stocks, (3) a dataset related only to financial stocks, and 4) a dataset only focusing on industrial stocks. Our results show that, across all the MCMC methods, the BLR-ARD model has the best performance on the industrial stocks followed by the resources stocks, with the worst performance being on the financial stocks. We further find that the MCMC methods agree on which features are the most relevant for this task. The essential features vary across the different sectors, with relative volatility and initial price being most important for the entire dataset; rolling 20day volatility and target price being critical on the resource stocks; analyst coverage, initial price, and target price being important for financial stocks while the target price, initial price, analyst coverage, and Top40 index cross-sectional volatility being significant for industrial stocks.