Prediction of initial coin offering success based on team knowledge and expert evaluation

Abstract

Initial coin offering (ICO) is a new financing method that has been widely used in cryptocurrency projects. However, it has been reported that nearly 30% of cryptocurrency projects fail during ICO, indicating an important gap in research and an opportunity for more advanced research on ICO project assessment. This study reveals that previous studies primarily used project-related factors to predict ICO success while neglecting social factors such as team information and expert evaluation. Inspired by the knowledge-based theory (KBT) of the firm, we set out to examine the impact of heterogeneous team knowledge and expert evaluation on ICO success. One primary contribution of this study is the design of novel knowledge measures based on KBT. In addition, we propose a deep-learning model – an attention-based bidirectional recurrent neural network (A-BiRNN) – to automatically extract features from online comments. We validate the proposed model on a real-world dataset, and experiments show that the accuracy of the proposed prediction model outperforms those of existing models by more than 6%, highlighting the effectiveness of the proposed approach in predicting ICO success. This study's results provide useful ideas for both investors and ICO platforms to assess the quality of cryptocurrency projects, thus improving information symmetry in ICO markets. Also, this study demonstrates the value of applying KBT in assessing firm performance in ICO markets. The generalized value of the proposed approach should be tested in more business contexts, such as crowdfunding and peer-to-peer (P2P) lending.