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In the big data era, deep learning and intelligent data mining techniquesolutions have been applied by researchers in various areas. Forecast andanalysis of stock market data have represented an essential role in today'seconomy, and a significant challenge to the specialist since the market'stendencies are immensely complex, chaotic and are developed within a highlydynamic environment. There are numerous researches from multiple areasintending to take on that challenge, and Machine Learning approaches have beenthe focus of many of them. There are multiple models of Machine Learningalgorithms been able to obtain competent outcomes doing that class offoresight. This paper proposes the implementation of a generative adversarialnetwork (GAN), which is composed by a bi-directional Long short-term memory(LSTM) and convolutional neural network(CNN) referred as Bi-LSTM-CNN togenerate synthetic data that agree with existing real financial data so thefeatures of stocks with positive or negative trends can be retained to predictfuture trends of a stock. The novelty of this proposed solution that distinctfrom previous solutions is that this paper introduced the concept of a hybridsystem (Bi-LSTM-CNN) rather than a sole LSTM model. It was collected data frommultiple stock markets such as TSX, SHCOMP, KOSPI 200 and the S&P 500,proposing an adaptative-hybrid system for trends prediction on stock marketprices, and carried a comprehensive evaluation on several commonly utilizedmachine learning prototypes, and it is concluded that the proposed solutionapproach outperforms preceding models. Additionally, during the research stagefrom preceding works, gaps were found between investors and researchers whodedicated to the technical domain.

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions