Stock Price Predictors Based on Bayesian Method

Many stock Price Predictors transformating input (historical data, theory) to output (forecast) have been publishing. For example papers [1], [2] deal with ARMA and exponential smoothing methods. Proposed contribution present an approach based on Bayesian method. Bayesian method, applied to stock price forecast, enables to predict stock prices when much historical data are unavailable or where the users of such information processing systems might not be able to accumulate them. This article shows basic approach to Bayesian estimation of constant process and demonstrates its methodology. Finally, we present example illustrating the application of this approach.