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

Zineb Lanbouri and Saaid Achchab

Papers from arXiv.org

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.

Date: 2020-01
New Economics Papers: this item is included in nep-fmk
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Published in IJCSI (International Journal of Computer Science Issues), Volume 16, Issue 1, March 2019

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2001.03333

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