Short Term Prediction Framework for Moroccan Stock Market Using Artificial Neural Networks
| Autor: | Abdelaziz Berrado, Badre Labiad, Loubna Benabbou |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Computer science Term memory 02 engineering and technology Perceptron Machine learning computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Stock market Artificial intelligence business computer Stock (geology) |
| Zdroj: | SITA |
| DOI: | 10.1145/3289402.3289520 |
| Popis: | In this paper we present a short term forecasting framework for stock markets based on Artificial Neural Networks. We are interested in predicting future trends of stock markets (Up, Down and Unchanged) in the very short term (10 to 60 minutes ahead). We present a framework to efficiently implement different ANN architectures namely Multi-Layers Perceptron (MLP) and the Long Short- Term Memory (LSTM). This framework involves the use of intraday prices data (tick-by-tick data) and a selection of technical indicators as input variables and fixes the issues related to the imbalanced target classes and the non-regularly spaced input data.We conduct different experimentations within the proposed framework on data from the Moroccan stock market. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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