Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe.
This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models.
The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated.
The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2020R1G1A1013221).
Yasir, M., Attique, M., Latif, K., Chaudhary, G.M., Afzal, S., Ahmed, K. and Shahzad, F. (2020), "Deep-learning-assisted business intelligence model for cryptocurrency forecasting using social media sentiment", Journal of Enterprise Information Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEIM-02-2020-0077Download as .RIS
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