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AUTHOR(S):

Luca Di Persio, Oleksandr Honchar

 

TITLE

Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements

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ABSTRACT

In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.

KEYWORDS

Artificial neural networks, Multi-layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets, Time series analysis, financial forecasting

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Cite this paper

Luca Di Persio, Oleksandr Honchar. (2016) Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements. International Journal of Economics and Management Systems, 1, 158-162

 

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