Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump (2204.12929v2)

Abstract: With the proliferation of pump-and-dump schemes (P&Ds) in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance to alert potentially susceptible investors. In this paper, we focus on predicting the pump probability of all coins listed in the target exchange before a scheduled pump time, which we refer to as the target coin prediction task. Firstly, we conduct a comprehensive study of the latest 709 P&D events organized in Telegram from Jan. 2019 to Jan. 2022. Our empirical analysis reveals some interesting patterns of P&Ds, such as that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity. Here channel refers a form of group in Telegram that is frequently used to coordinate P&D events. This observation inspires us to develop a novel sequence-based neural network, dubbed SNN, which encodes a channel's P&D event history into a sequence representation via the positional attention mechanism to enhance the prediction accuracy. Positional attention helps to extract useful information and alleviates noise, especially when the sequence length is long. Extensive experiments verify the effectiveness and generalizability of proposed methods. Additionally, we release the code and P&D dataset on GitHub: https://github.com/Bayi-Hu/Pump-and-Dump-Detection-on-Cryptocurrency, and regularly update the dataset.

• The paper presents a sequence-based neural network that leverages historical pump-and-dump events to accurately predict target coins.
• It uses a positional attention mechanism to capture intra-channel homogeneity and inter-channel heterogeneity, outperforming baseline models.
• The study highlights that coins with mid-cap values and strong social media presence are more susceptible to pumps, offering practical market insights.

Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

The paper "Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump" presents a focused paper on the detection and prediction of pump-and-dump (P&D) schemes prevalent in cryptocurrency markets. Initiating the work with an in-depth analysis of 709 recent P&D events curated from Telegram channels, the research primarily targets predicting the probability of a coin being pumped on a cryptocurrency exchange, known as the target coin prediction task.

Overview and Novelty

The authors introduce an innovative sequence-based neural network (SNN) leveraging the history of a channel's P&D events. The SNN effectively utilizes the positional attention mechanism to encode historic events into robust sequence representations, ultimately enhancing prediction accuracy. This method highlights the versatility of adapting sequence-based models to extract meaningful patterns from historic data and integrate them into predictive models.

Key Observations

A prominent empirical observation in their paper shows intra-channel homogeneity and inter-channel heterogeneity in pumped coins. This implies uniformity in coin selection within individual channels while showcasing significant diversity across different channels. Such insights prompted the development of SNN, facilitating better encoding of a channel's unique coin selection strategy.

The detailed analysis further elucidates that coins with middle-cap value and considerable social media presence are more susceptible to being targeted. Furthermore, significant actions preceding P&D events, noted as price and volume surges, serve as potential precursors to predict these fraudulent events beforehand. This reaffirms the predictive capacity of channel-specific strategies encoded in sequence-based models.

Numerical Results and Effectiveness

Extensive evaluations demonstrate the effectiveness of SNN over various baseline models, including traditional machine learning models such as Logistic Regression (LR) and Random Forest (RF), and deep learning architectures like LSTM and TCN. The results indicate that SNN consistently achieves higher hit ratios due to its adeptness in capturing skip-correlation within coin sequences while maintaining computational efficiency. This performance edge is particularly pronounced when compared to that of previous methodologies reliant on non-sequential data interpretations.

Contribution to the Field and Practical Implications

This paper contributes notably by offering a robust pipeline for target coin prediction, which can be directly applicable in real-world scenarios. The methodology not only enhances prediction accuracy but also provides significant insights into the characteristics driving P&D schemes, thereby enriching the understanding of market manipulations in cryptocurrency economies.

Furthermore, the generalizability of their method extends to tasks beyond the scope of P&D prediction. The authors exemplify its applicability to cryptocurrency price forecasting, signifying the broader potential of incorporating sentiment analysis from heterogeneous data, such as Telegram discussions, into predictive models.

Future Directions

Possible directions for future research include refining the predictive capabilities of SNN by integrating more diverse data sources and experimenting with advanced sequential modeling techniques. Additionally, researchers could explore real-time implementations of such models to provide a more immediate response to P&D schemes, potentially diminishing financial impacts on unsuspecting investors.

In conclusion, this paper presents a methodologically sound and practically significant contribution to cryptocurrency market risk management, advocating for the continued exploration of sequence-based models in financial fraud detection and beyond.