  Long-term investors, different from short-term traders, focus on examining
the underlying forces that affect the well-being of a company. They rely on
fundamental analysis which attempts to measure the intrinsic value an equity.
Quantitative investment researchers have identified some value factors to
determine the cost of investment for a stock and compare different stocks. This
paper proposes using sequence prediction models to forecast a value factor-the
earning yield (EBIT/EV) of a company for stock selection. Two advanced sequence
prediction models-Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU)
networks are studied. These two models can overcome the inherent problems of a
standard Recurrent Neural Network, i.e., vanishing and exploding gradients.
This paper firstly introduces the theories of the networks. And then elaborates
the workflow of stock pool creation, feature selection, data structuring, model
setup and model evaluation. The LSTM and GRU models demonstrate superior
performance of forecast accuracy over a traditional Feedforward Neural Network
model. The GRU model slightly outperformed the LSTM model.
