A document analysis deep learning regression model for initial coin offerings success prediction

【Author】 Wang, Jiayue; Chen, Runyu; Xu, Wei; Tang, Yuanyuan; Qin, Yu

【Source】EXPERT SYSTEMS WITH APPLICATIONS

【影响因子】8.665

【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 demon-strate 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.

【Keywords】Initial Coin Offering; Cryptocurrency; Text Mining; Document Layout Analysis

【发表时间】2022 DEC 30

【收录时间】2022-11-30

【文献类型】理论模型

【主题类别】

区块链治理-市场治理-ICO