Modeling cryptocurrency market trends using textual data

Benjamin Fele (2017) Modeling cryptocurrency market trends using textual data. EngD thesis.

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Abstract

The aim of this work is to build a model that predicts cryptocurrency trends based on data from the Poloniex online market. The problem is interesting because of the potential for automated cryptocurrency trading that a successful model would allow. For the forecast, we used the news from the subject area, which were obtained through the Reddit website. In addition to textual data, numerical market data before publishing the news is also used for forecasting. We approached the problem as a three-class classification problem. Using the TF-IDF method, sentiment information, polarity, and article and market information before the publication of the text, we achieved 50,8% classifying accuracy on the validation set and 49,3% classification accuracy on the test set. We used support vector machines for learning and prediction. We have found that in practice, despite the significant classification accuracy, the model is unlikely to yield profitable returns.

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