A document analysis deep learning regression model for initial coin offerings success prediction. (30th December 2022) - Wang, Jiayue; Chen, Runyu; Xu, Wei; Tang, Yuanyuan; Qin, Yu
Highlights: We design a document analysis model to predict the success of initial coin offerings. The fine-grained model extracts textual and layout features from whitepapers. The model can mitigate information asymmetry problems for investors and platforms. Business documents' content and presentation can affect investment decisions. Abstract: Initial coin offerings (ICOs) provide an early-stage financing method for blockchain-based ventures. During the ICO process, whitepapers are important not only as promotional material through which ventures can demonstrate the technical and financial project details but also as references for investors. Persuasion theory and the related literature suggest that the presentation and order of information have a significant impact on the attitude of the audience. Therefore, in addition to projects' metadata features, we construct a document analysis deep regression model (DADRM) to innovatively extract deep text and layout features from whitepapers. Based on a real-life dataset, we conduct a comparative study to assess the effectiveness of the proposed framework in predicting ICO success in terms of the funding amount. The empirical results show that our model that both extracts text content and retains the original 2D structure of the document can significantly reduce prediction error. Based on our proposed model, both ICO platforms and investors can prejudge the funding amount of cryptocurrency projects and mitigate information asymmetry. Additionally, this study demonstrates that both what is written in the business document and how the document is presented affect investor decisions.