A new approach for Trading based on Long-Short Term memory technique

Une nouvelle approche pour le Trading basée sur la technique Long-Short Term Memory

Zineb Lanbouri () and Saaid Achchab
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Zineb Lanbouri: ENSIAS - Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes - UM5 - Université Mohammed V de Rabat [Agdal]
Saaid Achchab: ENSIAS - Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes - UM5 - Université Mohammed V de Rabat [Agdal]

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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's 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 New York 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: 2019-06
Note: View the original document on HAL open archive server: https://hal.science/hal-02396905
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Published in International Journal of Computer Science Issues, 2019

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