论文标题
深度学习的收入预测
Earnings Prediction with Deep Learning
论文作者
论文摘要
在金融领域，可靠的预测公司的未来财务业绩对于投资者的投资决策至关重要。在本文中，我们将长期短期记忆（LSTM）网络与时间卷积网络（TCN）进行比较，以预测未来每股收益（EPS）。实验分析基于季度财务报告数据和每日股票市场收益。对于美国公司的广泛样本，我们发现两个LSTM的表现都优于天真的持久模型，其准确的预测高达30.0％，而TCN可以实现30.8％。两种类型的网络至少都与分析师一样准确，并且超过了12.2％（LSTM）和13.2％（TCN）。
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).
