Ms. Sisi Shao Profile

Sisi Shao

at Leonard N. Stern School of Business

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Proceedings Article | 1 December 2021 Paper

The effectiveness of supervised learning models in detection of pump and dump activity in Dogecoin

Sisi Shao

Proceedings Volume 12079, 120791I (2021) https://doi.org/10.1117/12.2622877

KEYWORDS: Data modeling, Machine learning, Performance modeling, Web 2.0 technologies, Signal detection, Data conversion, Analytical research, Statistical modeling, Visualization, Solids

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B Investment into hazardous assets should always be made with caution, if not avoided completely by risk-averse groups, such as the older workforce. However, as society and technology advance, it becomes almost impossible for regulators to make effective enough laws to mitigate the risk of newly invented financial instruments, such as cryptocurrency. This paper analyzed how effective are modern data science techniques: supervised learning models, such as random forest, K Nearest Neighbors, decision tree, combined with bagging and stacking techniques, could be used to catch the notorious "pump and dump" activities in the Dogecoin market, which is the cryptocurrency that had a close to positive infinity return in May 2021 during the COVID-19 pandemic. This paper concluded that random forest algorithm, when trained with a five-folded cross-validation technique, could reach an out-of-sample testing accuracy of 100%. Furthermore, the F1 score of 0.84101, precision score of 0.94402, and recall score of 0.77700 could alleviate one's concern about overfitting. In conclusion, the model results suggest modern supervised learning techniques are quite effective in catching suspicious activities in modern financial instruments

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