Over the past decade of accounting and finance research, the Ohlson model has been often examined as a framework for equity valuation. In this paper, we apply Bayesian statistics to the Ohlson model, and evaluate improvement in predictive power. Specifically, focusing on SP500 firms, we use 23 quarters of data starting in Q1 1999 to estimate the prediction models, which we then use to predict stock price in Q4 of 2004. We use two types of estimation approaches, maximum likelihood and Bayesian statistics. We find that Bayesian analyses generally result in smaller predictive errors than maximum likelihood analyses. We perform several transformations, however transformations of the maximum likelihood models do not outweigh the usefulness of applying Bayesian statistics. We conclude that applying Bayesian statistics is a fruitful way to improve the Ohlson's classical framework for equity valuation.