  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.
