Li Peizhi
Contact Information
Email: peizhi.li@dufe.edu.cn
Education Background
Highest Degree & Major
Ph.D in economics, Statistics
Optional
Dongbei University of Finance and Economics, Statistics
Research Interests
Financial Risk Management and Quantitative Modeling, Financial Technology
Academic Positions & Professional Titles
● Professor of Finance, Dongbei University of Finance and Economics, 2022 – Present
● Deputy Director of the Department of Financial Engineering, Dongbei University of Finance and Economics, 2024 – Present
Editorial & Reviewer Experience
1.Editorial Board Memberships
● Journal of Economic Statistics, Editorial Board Member, 2022– Present
2.Regular Reviewer for Top Journals
● Pacific-Basin Finance Journal, Annals of Applied Statistics,International Review of Financial Analysis, Structural Change and Economic Dynamics, 2019 – Present
Representative Publications
1.Key Publications
● Li, P., et al. (2025). Measuring the dynamics of the stock market’s volume-price relationship: a new Hurst-based market-trend index. Applied Economics, 57(39), 6026–6043. https://doi.org/10.1080/00036846.2024.2376775. (SSCI/SCI Q4, Impact Factor 2.1)
● Zheng, J.,et al. (2025). Improved multi-step prediction of daily tourism demand: an innovative hybrid machine learning framework with search engine data. Current Issues in Tourism, 1–20. https://doi.org/10.1080/13683500.2025.2554872. (SSCI/SCI Q3, Impact Factor 4.6, Ranked as an 'A' journal in the Australian ABDC list)
● Zhao, D., et al. (2024). How do risk shocks reshape the spillovers among the oil, gold, emerging, and developed markets? Evidence from a new TVP-VAR-based wavelet coherence framework. Applied Economics, 1–17. https://doi.org/10.1080/00036846.2024.2386862
● Zhao, D., et al. Uncovering the Switching Impact of Economic Policy Uncertainty on the Cross-Correlation Between Stock Markets: An Innovative Hurst-Based Wavelet Coherence Approach. Comput Econ (2025). https://doi.org/10.1007/s10614-025-10952-x
● Li P., et al. (02 Feb 2024): An empirical comparison between gradient boosting methods and cox’s proportional hazards model for right-censored survival data, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2024.2306541
● Yang M., et al. (2024). Improved prediction of global gold prices: An innovative Hurst-reconfiguration-based machine learning approach. Resources Policy, 88, 104430.
● Li Peizhi; Optimization of Support Vector Machine Models and Their Applications, Dongbei University of Finance and Economics Press, 2023
● Li P., et al. (2020). A support vector machine based semiparametric mixture cure model. Computational Statistics, 35(3), 931-945.
● Li Peizhi, Dong Qingli. A Box Office Prediction Model Based on Web Search Data and Machine Learning[J]. Operations Research and Management Science, 2021, 30(11):8.
● Zheng Jianing, Luan Dong, Li Peizhi. Research on Survival and Delisting Motives of Chinese Over-the-Counter (OTC) Listed Companies Based on Survival Analysis [J]. Journal of Dalian University of Finance and Economics,2022(06):51-62.DOI:10.19653/j.cnki.dbcjdxxb.2022.06.005.
● Dong Q., et al. (2021) Time to delisted status for listed firms in Chinese stock markets: An analysis using a mixture cure model with time-varying covariates, Journal of the Operational Research Society, DOI: 10.1080/01605682.2021.1992308.
● Li P., et al. The analysis and application of a new hybrid pollutants forecasting model using modified Kolmogorov-Zurbenko filter, Science of the Total Environment, 2017, 583: 228-240.
● Jiang P., et al. Research and Application of a New Hybrid Wind Speed Forecasting Model on BSO Algorithm , Journal of Energy Engineering, 2017, 143(1): 0-04016019.
● Li Peizhi. Research and Application of PM2.5 Concentration Prediction Model - Based on Support Vector Machine Model Optimized by Population Search Algorithm, World Survey, 2016, (02): 19-24.
● Dong Q., et al. Multifractal behavior of an air pollutant time series and the relevance to the predictability[J]. Environmental Pollution, 2017, 222(mar.):444-457.
● Dong Q., et al. (2017). A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China.Renewable Energy.
● Jiang P., et al. (2017). A novel hybrid strategy for pm2.5 concentration analysis and prediction. Journal of Environmental Management, 196(JUL.1), 443-457.
● Jiang P., et al., A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction, Applied Soft Computing, Volume 55, 2017, Pages 44-62, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2017.01.043.
2.Research Grants
● Principal Investigator, Grant Name, e.g., National Social Science Fund of China, 21CTJ012, 200000 RMB, 2021-2024
● Co-Investigator, Major Project of the National Social Science Fund of China, 24&ZD089, 2024 - Present