Improving artificial neural network based stock forecasting using fourier de-noising and Hodrick-Prescott Filter

Abstract

Accuracy in financial forecasting is a key determinant of profits in the financial markets. This paper proposes improvements to existing Artificial Neural Network based forecasting approaches using de-noising in frequency domain and the Hodrick-Prescott Filter. Traditionally used technical indicators are replaced with open, close, high, and low prices only… (More)
DOI: 10.1109/ICICS.2015.7459920

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