  Technical and fundamental analysis are traditional tools used to analyze
individual stocks; however, the finance literature has shown that the price
movement of each individual stock correlates heavily with other stocks,
especially those within the same sector. In this paper we propose a general
purpose market representation that incorporates fundamental and technical
indicators and relationships between individual stocks. We treat the daily
stock market as a "market image" where rows (grouped by market sector)
represent individual stocks and columns represent indicators. We apply a
convolutional neural network over this market image to build market features in
a hierarchical way. We use a recurrent neural network, with an attention
mechanism over the market feature maps, to model temporal dynamics in the
market. We show that our proposed model outperforms strong baselines in both
short-term and long-term stock return prediction tasks. We also show another
use for our market image: to construct concise and dense market embeddings
suitable for downstream prediction tasks.
