E-LEARNING ECONOMIC CRISIS
E-LEARNING ECONOMIC CRISIS

Summary/Abstract: The potency of e-learning varies from context to context, however, has also been shown to become a valuable method of predicting an economic crisis. Basically, e-learning becomes an accessible and efficient method to avoid financial and social blockages worldwide. Due the popularity of deep learning for analysing text data, with really promising results, for instance, in financial forecasting from news focused on specific topics, a new vision of predicting an economic crisis may be the foundation of an intelligent economic theory. This study explores five algorithms - Exchange Rate Processing (including BNR validation), Logistic Regression, Linear Regression, Recurrent Neural Network, Sentiment Analysis - to predict different economic stressful events. Also, it addresses the problem of correlating the open information provided by economic publications and social media with impact on the economic behaviour with a visible and rapid spread worldwide. The goal of this paper is to implement an e-learning system able to provide a set of valid and credible information about a potential economic crisis, using a dataset of financial and economic topics, chronologically ordered from 2008 to 2018. Consequently, there is a need to evaluate the performance of e-learning in the domain of financial economics as part of ongoing quality of life improvements efforts. The results can be seen as a starting point for broader research in predicting the future economic or financial crises. The entire research was based on history of previous economic crisis and the entire chain of events extracted from the dataset consisting of economic news.

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