  Feature extraction from financial data is one of the most important problems
in market prediction domain for which many approaches have been suggested.
Among other modern tools, convolutional neural networks (CNN) have recently
been applied for automatic feature selection and market prediction. However, in
experiments reported so far, less attention has been paid to the correlation
among different markets as a possible source of information for extracting
features. In this paper, we suggest a CNN-based framework with specially
designed CNNs, that can be applied on a collection of data from a variety of
sources, including different markets, in order to extract features for
predicting the future of those markets. The suggested framework has been
applied for predicting the next day's direction of movement for the indices of
S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of
initial features. The evaluations show a significant improvement in
prediction's performance compared to the state of the art baseline algorithms.
