  Stock market prediction is still a challenging problem because there are many
factors effect to the stock market price such as company news and performance,
industry performance, investor sentiment, social media sentiment and economic
factors. This work explores the predictability in the stock market using Deep
Convolutional Network and candlestick charts. The outcome is utilized to design
a decision support framework that can be used by traders to provide suggested
indications of future stock price direction. We perform this work using various
types of neural networks like convolutional neural network, residual network
and visual geometry group network. From stock market historical data, we
converted it to candlestick charts. Finally, these candlestick charts will be
feed as input for training a Convolutional Neural Network model. This
Convolutional Neural Network model will help us to analyze the patterns inside
the candlestick chart and predict the future movements of stock market. The
effectiveness of our method is evaluated in stock market prediction with a
promising results 92.2% and 92.1% accuracy for Taiwan and Indonesian stock
market dataset respectively. The constructed model have been implemented as a
web-based system freely available at http://140.138.155.216/deepcandle/ for
predicting stock market using candlestick chart and deep learning neural
networks.
