First-authored papers

1. Li Z, Li A, Bellotti A & Yao X. (2023). The profitability of online loans: a competing risks analysis on default and prepayment. European Journal of Operational Research. 306(2): 968-985. (ABS 4, JCR Q1). DOI/PDF. (Cites: 20)
2. Li Z, Feng C & Tang Y. (2022). Bank efficiency and failure prediction: A nonparametric and dynamic model based on data envelopment analysis. Annals of Operations Research. 315: 279–315 (ABS 3, JCR Q1). DOI/PDF. (Cites: 51)
3. Li Z, Zhang J, Yao X & Kou G. (2021). How to identify early defaults in online lending: A cost-sensitive multi-layer learning framework. Knowledge-Based Systems. 221. 106963. (JCR Q1). DOI/PDF. (Cites: 48)
4. Li Z, Crook J, Andreeva G & Tang Y. (2021). Predicting the risk of financial distress using corporate governance measures. Pacific-Basin Finance Journal. 68, 101334. (ABS 2, JCR Q1). DOI/PDF. (Cites: 253)
5. Li Z, Hu X, Li K, Zhou F & Shen F. (2020). Inferring the outcomes of rejected loans: an application of semi-supervised clustering algorithms. Journal of the Royal Statistical Society: Series A (Statistics in Society). 183(2):631-654. (ABS 3, JCR Q1). DOI/PDF. (Cites: 10)
6. Li Z, Tang Y, Wu J, Zhang J & Lv Q. (2020). The interest costs of green bonds: credit ratings, corporate social responsibility, and certification. Emerging Markets Finance and Trade. 56(12):2679-2692. (ABS 2, JCR Q1). DOI/PDF. (Cites: 182)
7. Li Z, Li K, Yao X & Wen Q. (2019). Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerging Markets Finance and Trade. 55(1):118-132. (ABS 2, JCR Q1). DOI/PDF. (Cites: 35)
8. Li Z, Crook J & Andreeva G. (2017). Dynamic prediction of financial distress using Malmquist DEA. Expert Systems with Applications. 80:94-106. (ABS 1, JCR Q1). DOI/PDF. (Cites: 134)
9. Li Z, Tian Y, Li K, Zhou F & Yang W. (2017). Reject inference in credit scoring using Semi-supervised Support Vector Machines. Expert Systems with Applications. 74:105-114. (ABS 1, JCR Q1). DOI/PDF. (Cites: 158)
10. Li Z, Crook J & Andreeva G. (2014). Chinese Companies Distress Prediction: An Application of Data Envelopment Analysis. Journal of the Operational Research Society. 65(3):466-479. (ABS 3, JCR Q2). DOI/PDF. (Cites: 85)

（注：被引数据取自谷歌学术2025年6月）

Co-authored papers

1. Yao X, Yang W, Feng C & Li Z. (2025). Reading Between the Lines: Detecting Corporate Financial Fraud Using Multi-dimensional Textual Features. Information Systems Frontiers. forthcoming. (ABS 3, JCR Q1). DOI/PDF
2. Yi T, Li L, Li Z & Zhang J. (2025). Evaluating electricity transmission and distribution efficiency using Data Envelopment Analysis Forest with feature importance. Energy. 330: 136580. (JCR Q1). DOI/PDF
3. Zhang X, Li Z, Zhao Y & Wang L. (2025). Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting. Annals of Operations Research. 345: 1267–1295. (ABS 3, JCR Q1). DOI/PDF.
4. Leng M, Li Z, Dai W & Shi B. (2024). The power of satellite imagery in credit scoring: a spatial analysis of rural loans. Annals of Operations Research. forthcoming. (ABS 3, JCR Q1). DOI/PDF.
5. Liao Y, Wang J, Liao W & Shu X, Li Z. (2024). Buffer orsubstitute? Corporate financialization and leverage manipulation. Pacific-Basin Finance Journal. 87. 102508. (ABS2, JCR Q1). DOI/PDF.
6. Yao X, Wu D, Li Z & Xu H. (2024). On the Prediction of Stock Price Crash Risk Using Textual Sentiment of Management Statement. China Finance Review International. 14(2): 310-331. (ESCI). DOI/PDF.
7. Xuan Q, Li Z & Zhao T. (2024). Does systemic risk affect fund managers' tail-risk taking. Pacific-Basin Finance Journal. 83. 102269. (ABS 2, JCR Q1). DOI/PDF.
8. Tang Y, Wang B, Pan N & Li Z. (2023). The impact of environmental information disclosure on the cost of green bond: Evidence from China. Energy Economics. 126. 107008. (ABS 3, JCR Q1). DOI/PDF.
9. Li A, Li Z, & Bellotti A. (2023). Predicting loss given default of unsecured consumer loans with time-varying survival scores. Pacific-Basin Finance Journal. 78. 101949. (ABS 2, JCR Q1). DOI/PDF.
10. Zhou F, Fu L, Li Z & Xu J. (2022). The recurrence of financial distress: a survival analysis. International Journal of Forecasting. 38(3): 1100-1115. (ABS 3, JCR Q1). DOI/PDF.
11. Zeng X, Li Z, Yang W & Huang Z. (2022). The risk interdependence of cryptocurrencies: Before and during the COVID-19 pandemic. International Journal of Financial Engineering. 9(4). 2150044. (ESCI). DOI/PDF.
12. Wu Z & Li Z. (2021). Customer churn prediction for commercial banks using customer value weighted machine learning models. Journal of Credit Risk. 17(4): 15-42. (ABS 1, JCR Q4). DOI/PDF.
13. Li K, Zhou F, Li Z, Li W & Shen F. (2021). A semi-parametric ensemble model for profit evaluation and investment decisions in online consumer loans with prepayments. Applied Soft Computing. 107. 107485. (JCR Q1). DOI/PDF.
14. Li K, Zhou F, Li Z, Yao X & Zhang Y. (2021). Predicting loss given default using post-default information, Knowledge-Based Systems. 224. 107068. (JCR Q1). DOI/PDF.
15. Tang Y, Li Z, Chen J & Deng C. (2021). Liquidity creation cyclicality, capital regulation and interbank credit: Evidence from Chinese commercial banks. Pacific-Basin Finance Journal. 101523. (ABS 2, JCR Q1). DOI/PDF.
16. Shen F, Zhao X, Li Z, Li K & Meng Z. (2019). A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation. Physica A: Statistical Mechanics and its Applications. 526. 121073. (JCR Q2). DOI/PDF.
17. Tang Y, Moro A, Sozzo S & Li Z. (2018). Modelling trust evolution within small business lending relationships. Financial Innovation. 4:1-19. (JCR Q1). DOI/PDF.
18. Shen F, Ma X, Li Z, Xu Z & Cai D. (2018). An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Information Sciences. 428:105-119. (JCR Q1). DOI/PDF.
19. Tian Y, Li K, Yang W & Li Z. (2017). A new effective branch-and-bound algorithm to the high order MIMO detection problem. Journal of Combinatorial Optimization. 33(4):1395-1410. (ABS 2, JCR Q3). DOI/PDF.