Stock market's price movement prediction with LSTM neural networks

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. For that goal, a prediction model was built, and a series of experiments were executed and theirs results analyzed against a number of metrics to assess if this type of algorithm presents and improvements when compared to other Machine Learning methods and investment strategies. The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.

[1]  W. Marsden.  I and J , 2012 .

[2]  A. Lo,et al.  A Non-Random Walk Down Wall Street , 1999 .

[3]  Luca Di Persio,et al.  Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements , 2016 .

[4]  Nicholas G. Polson,et al.  Deep Learning in Finance , 2016, ArXiv.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[7]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[8]  Karolin Baecker,et al.  A Random Walk Down Wall Street , 2016 .

[9]  Dirk Helbing,et al.  Are Random Trading Strategies More Successful than Technical Ones? , 2013, PloS one.

[10]  Robert Kissell,et al.  Multi-Asset Risk Modeling , 2013 .

[11]  Franklin Allen,et al.  Using genetic algorithms to find technical trading rules , 1999 .

[12]  Abhijit Sharang,et al.  Using machine learning for medium frequency derivative portfolio trading , 2015, ArXiv.

[13]  C. Kirkpatrick,et al.  Technical Analysis: The Complete Resource for Financial Market Technicians , 2006 .

[14]  Alex Graves,et al.  Supervised Sequence Labelling with Recurrent Neural Networks , 2012, Studies in Computational Intelligence.

[15]  Bilberto Batres-Estrada,et al.  Deep learning for multivariate financial time series , 2015 .

[16]  Snehanshu Saha,et al.  Predicting the direction of stock market prices using random forest , 2016, ArXiv.

[17]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Kai Chen,et al.  A LSTM-based method for stock returns prediction: A case study of China stock market , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[19]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.