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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. Forecasts achieved via these improvements...
Forecasting stock price with traditional time series methods has proven to be difficult. An artificial neural network is probably more suitable for this task, since no assumption of a mathematical model has to be made prior to the forecasting process. Furthermore, a neural network has the ability to extract the main influential factors from large sets of data, which is often required for a successful...
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