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This paper considers the prediction of noisy time series data, specifically, the prediction of financial signals. A novel Dynamic Ridge Polynomial Neural Network (DRPNN) for financial time series prediction is presented which combines the properties of both higher order and recurrent neural network. In an attempt to overcome the stability and convergence problems in the proposed DRPNN, the stability convergence of DRPNN is derived to ensure that the network posses a unique equilibrium state. In order to provide a more accurate comparative evaluation in terms of profit earning, empirical testing used in this work encompass not only on the more traditional criteria of NMSE, which concerned at how good the forecasts fit their target, but also on financial metrics where the objective is to use the networks predictions to generate profit. Extensive simulations for the prediction of one and five steps ahead of stationary …