Adaptive evolutionary neural networks for forecasting and trading without a data-snooping bias

In this paper, we present two Neural Network based techniques, an adaptive evolutionary Multilayer Perceptron (aDEMLP) and an adaptive evolutionary Wavelet Neural Network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a Smooth Transition Autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional Neural Networks such as the data snooping bias and the time-consuming and biased processes involved in optimizing their parameters.