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Stock market prediction is still a challenging problem because there are manyfactors effect to the stock market price such as company news and performance,industry performance, investor sentiment, social media sentiment and economicfactors. This work explores the predictability in the stock market using DeepConvolutional Network and candlestick charts. The outcome is utilized to designa decision support framework that can be used by traders to provide suggestedindications of future stock price direction. We perform this work using varioustypes of neural networks like convolutional neural network, residual networkand visual geometry group network. From stock market historical data, weconverted it to candlestick charts. Finally, these candlestick charts will befeed as input for training a Convolutional Neural Network model. ThisConvolutional Neural Network model will help us to analyze the patterns insidethe candlestick chart and predict the future movements of stock market. Theeffectiveness of our method is evaluated in stock market prediction with apromising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stockmarket dataset respectively. The constructed model have been implemented as aweb-based system freely available at http://140.138.155.216/deepcandle/ forpredicting stock market using candlestick chart and deep learning neuralnetworks.

Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market