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This paper presents a novel adaptive-filter approach for predicting assets onthe stock markets. Concepts are introduced here, which allow understanding thismethod and computing of the corresponding forecast. This approach is applied,as an example, through the prediction over the actual valuation of the PETR3shares (Petrobras ON) traded in the Brazilian Stock Market. The first-ratechoices of the window length and the number of filter coefficient areevaluated. This is done by observing the correlation between the predictorsignal and the actual course performed by the market in terms of both thewindow prevision length and filter coefficient values. It is shown that suchadaptive predictors furnish, on the average, very substantial profit on theinvested amount.

Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion