  Today, the use of social networking data has attracted a lot of academic and
commercial attention in predicting the stock market. In most studies in this
area, the sentiment analysis of the content of user posts on social networks is
used to predict market fluctuations. Predicting stock marketing is challenging
because of the variables involved. In the short run, the market behaves like a
voting machine, but in the long run, it acts like a weighing machine. The
purpose of this study is to predict EUR/USD stock behavior using Capsule
Network on finance texts and Candlestick images. One of the most important
features of Capsule Network is the maintenance of features in a vector, which
also takes into account the space between features. The proposed model,
TI-Capsule (Text and Image information based Capsule Neural Network), is
trained with both the text and image information simultaneously. Extensive
experiments carried on the collected dataset have demonstrated the
effectiveness of TI-Capsule in solving the stock exchange prediction problem
with 91% accuracy.
