Automated theme search in ICO whitepapers

• Main Authors: FU, Chuanjie, KOH, Andrew, GRIFFIN, Paul Robert
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Statistical methods; simulations; big data/machine learning; cryptocurrency; ICO; Databases and Information Systems; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/sis_research/4720
• Institution: Singapore Management University

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test show a statistically significant difference between topics in the success of an ICO being funded, indicating that the topics are usefully differentiated and suggesting that the topic model could be used to help predict whether an ICO will be successful.