  Stock prediction aims to predict the future trends of a stock in order to
help investors to make good investment decisions. Traditional solutions for
stock prediction are based on time-series models. With the recent success of
deep neural networks in modeling sequential data, deep learning has become a
promising choice for stock prediction. However, most existing deep learning
solutions are not optimized towards the target of investment, i.e., selecting
the best stock with the highest expected revenue. Specifically, they typically
formulate stock prediction as a classification (to predict stock trend) or a
regression problem (to predict stock price). More importantly, they largely
treat the stocks as independent of each other. The valuable signal in the rich
relations between stocks (or companies), such as two stocks are in the same
sector and two companies have a supplier-customer relation, is not considered.
In this work, we contribute a new deep learning solution, named Relational
Stock Ranking (RSR), for stock prediction. Our RSR method advances existing
solutions in two major aspects: 1) tailoring the deep learning models for stock
ranking, and 2) capturing the stock relations in a time-sensitive manner. The
key novelty of our work is the proposal of a new component in neural network
modeling, named Temporal Graph Convolution, which jointly models the temporal
evolution and relation network of stocks. To validate our method, we perform
back-testing on the historical data of two stock markets, NYSE and NASDAQ.
Extensive experiments demonstrate the superiority of our RSR method. It
outperforms state-of-the-art stock prediction solutions achieving an average
return ratio of 98% and 71% on NYSE and NASDAQ, respectively.
