Abstract
ABSTRACT This work is based on a mixe between the dynamics of investor decision making and the effectiveness of the forecasting models used to model market movements. Thus, it appears that the determination of adequate models can help to explain the behavior of agents and result to easier decision making through the anticipation of future prices. For this reason, we will use artificial intelligence models, in particular Machine Learning and Deep Learning algorithms, in order to better understand the variation of asset prices and their future evolution. In order to do so, we will use Recurrent Neural Networks (RNN), which has proven to be very suitable in the case of the Moroccan banking sector. The comparison between classical models and advanced artificial intelligence (AI) algorithms has demonstrated the inadequacy of classical statistical models. The latter are based on certain assumptions not verified in the framework of financial series, which reduces the capacity of classical models to correctly predict new data. The integration of AI has also made it possible to overcome the assumption of market efficiency by modeling the behavior of irrational agents, who trade on rumors and false news.
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