  In the financial sector, a reliable forecast the future financial performance
of a company is of great importance for investors' investment decisions. In
this paper we compare long-term short-term memory (LSTM) networks to temporal
convolution network (TCNs) in the prediction of future earnings per share
(EPS). The experimental analysis is based on quarterly financial reporting data
and daily stock market returns. For a broad sample of US firms, we find that
both LSTMs outperform the naive persistent model with up to 30.0% more accurate
predictions, while TCNs achieve and an improvement of 30.8%. Both types of
networks are at least as accurate as analysts and exceed them by up to 12.2%
(LSTM) and 13.2% (TCN).
