PREDICTIVE ANALYTICS FOR CRUDE OIL PRICE USING RNN�LSTM NEURAL NETWORK

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, predicting if the price of a particular stock is going to go up or not in the near
future.