Manipulator detection in cryptocurrency markets based on forecasting anomalies


Akba F., MEDENİ İ. T. , Guzel M. S. , Askerzade I.

IEEE Access, vol.9, pp.108819-108831, 2021 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 9
  • Publication Date: 2021
  • Doi Number: 10.1109/access.2021.3101528
  • Title of Journal : IEEE Access
  • Page Numbers: pp.108819-108831
  • Keywords: Anomaly detection, Covid-19 pandemic, Cryptocurrency markets, Deep learning, Machine learning, Manipulator detection, Sentiment analysis, Time series analysis

Abstract

© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Today, there are constant changes in terms of securities in stock markets. In these stock market investments, investors use fundamental analysis tools and indicators very widely. In this way, it is possible to have some knowledge of the situations experienced in the markets and to make a profit. In this study, manipulations on Bitcoin are discussed. Popular machine and statistical forecasting methods have been used to detect these manipulations and the road maps to be followed in order to be detected in the most successful way have been shared. Social media sentiments, which were thought to have an effect on manipulations during the studies, were also evaluated with the most advanced text analysis methods and evaluated together with these price changes. The allegations that the prediction methods carried out before the crisis were more successful were investigated. The Covid-19 pandemic was evaluated as a period of global crisis and the studies that might be relevant were examined. It would not be wrong to say that the actors that make big gains in the stock markets are the ones that determine the direction of the stock market. The manipulation periods of the market actors to be successful in the virtual money markets have been tried to be verified by various estimation methods. These estimations can achieve up to F1 score of 93% success according to our experimental result. Besides, it is stated that accounts with the highest volume of transactions in the periods, when anomalies were detected, were labeled as potential manipulators.