Technology and Cryptocurrency Valuation: Evidence from Machine Learning

Jinfei Sheng is an Assistant Professor of Finance at University of California Irvine, Merage School of Business. He holds a PhD degree in finance from University of British Columbia.

His primary research fields are empirical asset pricing, big data and FinTech. He uses textual analysis and machine learning to tackle fundamental questions in finance and economics. His paper has been accepted in top finance journal such as Journal of Financial Economics. He has given many presentations and discussions at top finance and economic conferences such as American Finance Association conference, American Economic Association conference, and European Finance Association conference. He is also a reviewer for top finance journals and conferences.

As a passionate teacher, he created a new course on FinTech at UCI and taught Corporate Finance at University of British Columbia, both of which won excellent teaching awards.

This paper studies the role of technological sophistication in Initial Coin Offering (ICO) successes and valuations. Using various machine learning methods, we construct technology indexes from ICO whitepapers to capture technological sophistication for all cryptocurrencies. We find that the cryptocurrencies with high technology indexes are more likely to succeed and less likely to be delisted subsequently. Moreover, the technology indexes strongly and positively predict the long-run performances of the ICOs. Overall, the results suggest that technological sophistication is an important determinant of cryptocurrency valuations.