Luca Di Persio, Oleksandr Honchar



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

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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.


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


[1] A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012, Nevada, 2012

[2] A. Dutta, G. Bandopadhyay and S. Sengupta, Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression, International Journal of Business and Information, 2012

[3] J. Chen , M. Chen and Nan Ye, Forecasting the Direction and Strength of Stock Market Movement, Technical report, 2013

[4] Y. Wang and In-Chan Choi, Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI, Technical report, 2013

[5] Wei Huang, Yoshiteru Nakamori, Shou-Yang Wang, Forecasting stock market movement direction with support vector machine, Computers and Operations Research, archive Volume 32, Issue 10, 2005, pp. 2513–2522

[6] Marcelo S. Lauretto, B. C. Silva and P. M. Andrade, Evaluation of a Supervised Learning Approach for Stock Market Operations, arXiv:1301.4944 [stat.ML], 2013

[7] V.V.Kondratenko and Yu. A Kuperin, Using Recurrent Neural Networks To Forecasting of Forex, arXiv:cond-mat/0304469 [cond-mat.disnn], 2003

[8] D. P. Kingma, J. Lei Ba, Adam: A method for stochastic optimization, 3rd Int. Conf. for Learning Representations, San Diego, 2015

[9] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research Volume 12, 2011, pp.2825–2830

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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