Our research spans digital finance and risk transmission, machine learning and financial forecasting, financial econometrics, and fintech development in banking. This page brings together the group’s index project and selected publications by members and their coauthors.
Public data on small and medium-sized banks are limited, and banks differ in how they invest in fintech and how they report it, which makes direct comparison difficult. Using annual reports as the main source, combined with information on organisational structure, board and senior management backgrounds, patents, software copyrights and violation records, the group built an evaluation framework to assess the fintech competitiveness of these banks.
The edition released in 2024 is based on 2023 data and covers 412 banks, of which 406 are small and medium-sized banks. Results are grouped into tiers separately for joint-stock commercial banks, city commercial banks, rural commercial banks, privately owned banks, and village and township banks.
Selected by research theme, with full authorship and links to the original records.
Supply Chain Shocks and the Reconfiguration of Green Finance Markets: A Quantile-on-Quantile Connectedness Analysis
Yao, J., Wu, J., Feng, H., & Sun, J. (2026). Systems, 14(6), 652.
Studies nonlinear connections between supply-chain bottlenecks, clean-energy equities, green bonds and carbon prices.
Risk Propagation and System Resilience in Sustainable-Digital Finance: A Time-Frequency Higher-Order Moment Analysis of Green Finance, Green Cryptocurrency, and Non-Green Cryptocurrency Markets
Wu, J., Yao, J., & Sun, J. (2026). Systems, 14(6), 628.
Examines risk connections across green finance and cryptocurrency markets using time-frequency and higher-order moment analysis.
Stablecoins, Risk Transmission and Systemic Reconfiguration in a Fragmented USD Access System: Evidence from Quantile Time-Frequency Analysis
Wu, J., Sun, J., Feng, H., & Long, F. (2026). Systems, 14(5), 562.
Studies risk transmission between official, parallel-market and stablecoin-based dollar channels in Argentina.
An information diffusion-based model of oil futures price
Li, Z., Sun, J., & Wang, S. (2013). Energy Economics, 36, 518-525.
Models oil futures prices through an information-diffusion approach.
Using Machine Learning for Prediction and Policy Analysis in Economics
Sun, J., Cole, M., & Härdle, W. K. (2026). The Review of Mathematical Economics, 1(2), 67-108.
Reviews machine learning for economic prediction, causal inference and policy analysis.
Sparse Interval-valued Time Series Modeling with Machine Learning
Bao, H., Hong, Y., Sun, Y., & Wang, S. (2024). arXiv:2411.09452.
Uses LASSO and adaptive LASSO for interval-valued time series, with applications to crude-oil forecasting and sparse index-tracking portfolios.
ChatGPT in Finance and Banking
Huang, K., Chen, X., Yang, Y., Ponnapalli, J., & Huang, G. (2023). In Beyond AI, pp. 187–218. Springer.
Discusses ChatGPT in financial services, fraud detection and risk assessment, alongside security, privacy and institutional adoption.
Adjusted-range-based self-normalized autocorrelation tests
Sun, J., Zhu, M., & Linton, O. (2025). Economics Letters, 251, 112315.
Develops adjusted-range self-normalized autocorrelation tests for time-series model diagnostics.
Kolmogorov-Smirnov type testing for structural breaks: A new adjusted-range-based self-normalization approach
Hong, Y., Linton, O., McCabe, B., Sun, J., & Wang, S. (2024). Journal of Econometrics, 238(2), 105603.
Develops structural-break tests to detect changes in time-series relationships.
A score statistic for testing the presence of a stochastic trend in conditional variances
Hong, Y., Linton, O., McCabe, B., & Sun, J. (2022). Economics Letters, 213, 110394.
Develops a score test for a stochastic trend in conditional variances.
Web3: Blockchain, the New Economy, and the Self-Sovereign Internet
Huang, K., Yang, Y., Zhang, F., Chen, X., & Zhu, F. (2024). Cambridge University Press.
Examines the technological foundations, applications and governance challenges of Web3 and blockchain.
The article quotes Jiajing Sun on capacity adjustment, technological competitiveness, and the distinction between growth in clean energy and investment returns.
The article quotes Jiajing Sun on China’s capital-market reforms, long-term investment returns, and growing differences between companies and sectors.
Evaluation Index System for the Digital Finance Competitiveness of Small and Medium-Sized Commercial Banks sets out the research background, data collection, text analysis and index construction. The method pages on this site are based mainly on this document.
At the China Finance Forum and 15th Journal of Financial Research Forum at Xiamen University, Jiajing Sun presented Evaluation Index System for the FinTech Competitiveness of Small and Medium-Sized Commercial Banks on behalf of the group, with Professor Lihui Tian of Nankai University as discussant.
Xiamen University conference report (Chinese) ↗
Chapter 14 of Financial Technology, “Regional Financial Innovation and Practices of Small and Medium-Sized Banks”, covers the current state of fintech in small and medium-sized banks with case studies, digitalisation differences between city commercial banks and rural credit cooperatives, and cloud architecture and open banking in smaller banks. It can be read alongside this research.