Improving the accuracy of financial time series prediction using ensemble networks and high order statistics

We apply neural network ensembles to the task of forecasting financial time series and explore the use of high order statistical information as part of network inputs. We show that the prediction accuracy of the time series can be significantly improved utilizing this methodology. Since prediction accuracy is only an estimate for the profitability on the financial market, we report good and profitable results using a profit/loss metric based on market simulations. Our simulations show an improvement of between 1.3 to 12.4% over a simple buy and hold trading strategy, and an improvement of between 6.5 to 20.9% over trading strategy using linear autoregressive models