Research
Research
Digital Bonds: The Role of Costs and Document Information on Trading Volume
This paper examines digital bonds issued on blockchain platforms, classifying them as native or non-native (tokenized) based on issuance architecture. Using a dataset of 7,719 transactions in tokenized treasury products backed by U.S. government securities, we analyze how transaction costs, management fees, macroeconomic conditions, and regulatory structures affect trading volume. Higher transaction costs are associated with lower trading activity, while management fees exhibit a positive association conditional on documentation quality. To measure disclosure content, we apply large language models to quantify the informativeness of legal, financial, and technological product documentation. Financial disclosures increase trading volume, while legal disclosures reduce it. Interaction effects show that financial information attenuates investor sensitivity to management fees, whereas legal and technological clarity amplify it. Instrumental variable estimation, placebo simulations, and heterogeneity analysis confirm the robustness of the results. Our findings contribute to the literature on digital finance and tokenized assets by identifying how cost structures and documentation quality jointly influence participation in decentralized fixed-income markets.
Keywords: Digital Bonds, Tokenized Treasury Products, Trading Volume, Decentralized Finance (DeFi)
JEL Classification: G12, G23, O33
The Digital Bond Premium: Early Evidence from Blockchain-Based Debt Issuance
Shufan Ma and Bertram I. Steininger
Abstract
We examine how blockchain technology affects bond pricing in primary markets. Using 37 digital bonds and over 15,000 conventional bonds issued between 2014 and 2025, we find that digital bonds carry yields 50-60 basis points higher than comparable conventional bonds. We investigate this premium through four mechanisms: technology adoption risk generates a 66 basis point maturity gradient, regulatory frameworks impose a 196 basis point premium, underdeveloped market infrastructure outside Europe adds 248 basis points, and public sector issuers face a 193 basis point penalty. Exploiting the staged implementation of the EU's Markets in Crypto-Assets (MiCA) regulation, we show that regulatory clarity exhibits non-monotonic effects. Initial policy proposals reduce spreads, but subsequent enforcement provisions elevate costs through compliance burdens. Our findings demonstrate that operational efficiencies from financial innovation do not automatically translate into lower borrowing costs when adoption occurs in nascent markets with immature infrastructure, uncertain regulation, and untested governance structures.
Keywords: Digital Bond, Blockchain Finance, Bond Pricing, Digital Premium, Financial Innovation
JEL Codes: G12; G18; G23; O33
Digital Bonds as Alternative Market Infrastructure: From Costly Experimentation to Crisis Resilience
Shufan Ma and Bertram I. Steininger
Multi-dimensional Housing Inequality Index: The Provincial Evidence from China
Junhua Chen*, Shufan Ma, and Na Liu.
Social Indicators Research, 2022
This research constructed a multi-dimensional inequality index to measure China's housing inequality from three dimensions: housing living conditions, housing wealth, and housing welfare based on household registration (hukou). Using cross-sectional data from the four-times China Household Finance Survey, the article showed the degree of China's housing inequality has an overall upward trend from 2011 to 2017. Furthermore, this research calculated the provincial housing inequality and conducted the regression analysis to investigate the factors that affect housing inequality in each province and suggests the central government focus on housing welfare and reform the hukou system in the future to alleviate housing inequality and the contradiction of housing interests
Lu Wei*, Shufan Ma, Maoze Wang
Electronic Commerce Research, 2023
Online reviews are essential to consumers' decision-making when purchasing products on e-commerce platforms. Most of the existing research conducts sentiment analysis on online reviews but needs to pay more attention to the information features of the text of online reviews. Based on Chinese review texts, this study collected 18,131 online clothing review information and applied Latent Dirichlet allocation (LDA) to divide the review texts into nine topics. Then, the informative characteristics of online reviews were evaluated using review length, readability, redundancy, and specificity indicators. We also investigate the relationship between review text informative features and review sentiment and verify the robustness of the results using different regression models. Our research will help e-commerce platforms construct general review guidelines to improve consumer satisfaction.
International Carbon Financial Market Prediction Using PSO and SVM Algorithm
Junhua Chen, Shufan Ma, and Ying Wu*
Journal of Ambient Intelligence and Humanized Computing, 2021
This research presented a novel approach, PSO-SVM, by combining support vector machine (SVM) and particle swarm optimization (PSO) algorithm and provided the optimal parameters for SVM to improve the prediction performance of European Allowance (EUA) carbon emission futures. This research used the realistic trading dataset containing 30,762 EUA futures closing prices to effectively predict extreme price fluctuations and overcome the problem of high prediction error caused by parameter constraints.
Note: * means the corresponding author.
Mass media, medical insurance and heterogeneous growth of companies: Evidence from the “Under the Dome” in China
Shufan Ma, Weizeng Sun
Land Financing Mode and Urban Total Factor Productivity Growth: Evidence from Chinese Cities
Junhua Chen, Shufan Ma, Yi Yang