Michael Jackson, Rice University
Project Description/Abstract
Recent successes in both Artificial Neural Networks and Wavelets analysis have placed these two methods in the spotlight of quantitative traders looking for the next best tool to forecast financial time series. The Wavelet Neural Network (W-NN), a prediction model which combines wavelet-based denoising and ANN, has successfully combined the two strategies in order to make accurate predictions of financial time series. In the project, we explore how the most recent formulation of the W-NN model, with the Nonlinear Autoregressive Neural Network with Exogenous variables (NARX), is affected by the choice of wavelet thresholding technique when predicting daily returns of American future contracts. We explore how the choice of thresholding technique affects the profitability of two technical trading models based on daily return predictions from a NARX based W-NN. We compare the predictability of the W-NN models along with the profitability of the two trading algorithms on four of the most heavily traded American index future contracts. The purpose of this research is twofold: to compare the effect of different wavelet thresholding techniques on a NARX based W-NN’s forecasting ability on one-day returns of American index futures contract, and to offer two easy to implement trading strategies.
Co-Authors
Katherine Ensor, Rice University
Yifan Zhang, Rice University