A new approach for trading based on Long Short Term Memory technique
This work addresses stock price forecasting for traders and investors, but it is incremental as it builds on existing LSTM methods.
The authors tackled stock market price prediction by developing an ensemble LSTM model using two-time frequencies, achieving improved prediction accuracy for 417 NYSE companies.
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.