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Publicação
Forecasting oil & gas etfs´ price movements using convolutional neural networks
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Resumo(s)
Thanks to advances in processing power, we have seen the revival of artificial intelligence after
the 1980s, and algorithmic trading has become quite popular in the last two decades. In this
paper, a convolutional neural network for image recognition was constructed. The CNN
recognises patterns in 2D images generated from financial data and classifies them as BUY,
SELL or HOLD. The analysed ETF, XLE, is from the Oil & Gas sector. The results are
evaluated computationally and financially and compared to other industries. Overall, the CNN
approach seems promising but generally, it was not possible to outperform the Buy&Hold
strategy.
Descrição
Palavras-chave
Forecasting Deep learning Technical analysis Oil & gas industry Convolutional neural networks