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The stock market prediction has always been crucial for stakeholders, tradersand investors. We developed an ensemble Long Short Term Memory (LSTM) modelthat includes two-time frequencies (annual and daily parameters) in order topredict the next-day Closing price (one step ahead). Based on a four-stepapproach, this methodology is a serial combination of two LSTM algorithms. Theempirical experiment is applied to 417 NY stock exchange companies. Based onOpen High Low Close metrics and other financial ratios, this approach provesthat the stock market prediction can be improved.

A new approach for trading based on Long Short Term Memory technique