Abstract
AbstractThe amount of data generated daily in the financial markets is diverse and extensive; hence, creating systems that facilitate decision-making is crucial. In this paper, different intelligent systems are proposed and tested to predict the closing price of the IBEX 35 using ten years of historical data with four different neural networks architectures. The first was a multi-layer perceptron (MLP) with two different activation functions (AF) to continue with a simple recurrent neural network (RNN), a long-short-term memory (LSTM) network and a gated recurrent unit (GRU) network. The analytical results of these models have shown a strong, predictable power. Furthermore, by comparing the errors of predicted outcomes between the models, the LSTM presents the lowest error with the highest computational time in the training phase. Finally, the empirical results revealed that these models could efficiently predict financial data for trading purposes.KeywordsMachine learningIBEX35Stock market predictionArtificial neural networksRecurrent neural networkGated recurrent unitLong-short-term memory
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