| 摘要(英) |
Abstract
Initial Coin Offering (ICO) is a fundraising method derived from blockchain and cryptocurrency. Compared to Initial Public Offering (IPO) , ICO make good use of blockchain to enhance transaction transparency and speed, which not only avoid the complex application process but also has the potential to attract funds from global investors. However, with the increasing number of ICOs, cases of fraud have proliferated. Since ICOs are not required to disclose information compulsorily, issuers have the right to determine the extent of information disclosure. Therefore, investors often can only obtain related information from the ICO’s website and white paper before investing. To address the frequent fraud faced by ICO investors, this study establishes a prediction model using white paper information to predict the likelihood of an ICO being fraudulent. The study employs Random Forest, KNN, SVM, Naïve Bayes, and Probit as prediction models, using white papers of ICOs between 2016 and 2020 as research samples, and further investigates the impact of various information provided in the white papers on ICO fraud. The results show that among the five prediction models, SVM performs the best. Additionally, among the features used to predict ICO fraud in this study, detailed information on smart contracts and vision blueprints had the most significant impact on the result, indicating that these two pieces of information are the most indicative when it comes to predicting future fraud. This study provides a new evaluation method for future ICO investors, reducing the likelihood of investors getting defrauded.
Keywords: initial coin offering, fraud prediction, machine learning |
| 參考文獻 |
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至系統瀏覽論文 (2029-7-17以後開放)
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