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

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  • Zineb Lanbouri
  • Saaid Achchab

Abstract

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.

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  • Zineb Lanbouri & Saaid Achchab, 2020. "A new approach for trading based on Long Short Term Memory technique," Papers 2001.03333, arXiv.org.
    • Handle: RePEc:arx:papers:2001.03333
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
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