Abstract
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 F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> 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.
Highlights
Manipulations play the most important role in the upward and downward trend of prices
Through implementation of Deep Neural Decision Forest (DNDF) [30] method, it is proved that DNDF presents similar results with the Iterative SemiSupervised Feature Selection (ISSFS) method [5]
According to the latest performance results shown in Table 8 in the studies up to this stage, the machine learning method “Support Vector Machine (SVM)” and the time series forecasting method “SARIMAX” achieved most successful results in terms of estimating “Bitcoin Trend Detection problem for the crisis period
Summary
Manipulations play the most important role in the upward and downward trend of prices. F. Akba et al.: Manipulator Detection in Cryptocurrency Markets Based on Forecasting Anomalies especially deep learning, has been used in multiple fields and industries [2,3,4]. The experiments carried out are based on the analysis of daily, monthly, weekly or annual price data with artificial intelligence methods, and consider the effect of sentiment analysis on social media on manipulations. Manipulation points are determined with successful forecasting methods, followed by determining potential manipulation periods with the help of anomaly detection. This is the first study that shared feasible business solution model to detect potential stock market manipulators on finance sector. Elements that might affect the performance of successful forecasting methods used at the stock markets have been investigated in detail.
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