Fundamental research questions and proposals on predicting cryptocurrency prices using DNNs

• Κύριοι συγγραφείς: Pintelas, Emmanuel, Livieris, Ioannis, Stavroyiannis, Stavros, Kotsilieris, Theodore, Pintelas, Panagiotis
• Άλλοι συγγραφείς: Πιντέλας, Εμμανουήλ
• Μορφή: Technical Report
• Γλώσσα: English
• Έκδοση: 2020
• Θέματα: Deep learning; CNN; LSTM; BiLSTM; Cryptocurrency price prediction; Time-series; Βαθιά μάθηση; Χρονοσειρές

In last decade, cryptocurrency has emerged in financial area as a key factor in businesses and financial market opportunities. Accurate predictions can assist cryptocurrency investors towards right investing decisions and lead to potential increased profits. Additionally, they can also support policy makers and financial researchers in studying cryptocurrency markets behavior. Nevertheless, cryptocurrency price prediction is considered a very challenging task, due to its chaotic and very complex nature. In this study we investigate three major research questions: i) Can deep learning efficiently predict cryptocurrency prices? ii) Are cryptocurrency prices a random walk process? iii) Is there a proper validation method of cryptocurrency price prediction models? To this end, we evaluate some of the most successful and widely used in bibliography deep learning algorithms forecasting cryptocurrency prices. The results obtained, provide significant evidence that deep learning models are not able to solve this problem efficiently and effectively. Following detailed experimentation and results analysis, we conclude that it is essential to invent and incorporate new techniques, strategies and alternative approaches such as more sophisticated prediction algorithms, advanced ensemble methods, feature engineering techniques and other validation metrics.