论文标题
基于长期短期记忆技术的一种新方法
A new approach for trading based on Long Short Term Memory technique
论文作者
论文摘要
股票市场预测一直对利益相关者，贸易商和投资者至关重要。我们开发了一个合奏长期内存（LSTM）模型，其中包括两次频率（年度和每日参数），以预测次日的收盘价（提前一步）。基于四步方法，该方法是两种LSTM算法的串行组合。经验实验适用于417个纽约证券交易所公司。基于开放的高低关闭指标和其他财务比率，这种方法证明可以改善股票市场预测。
The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 NY stock exchange companies. Based on Open High Low Close metrics and other financial ratios, this approach proves that the stock market prediction can be improved.
