amjs澳金沙门欢迎您讲师，中央财经大学经济学博士。研究方向包括金融资产定价与经济绿色发展，已在Expert Systems with Applications、Journal of Forecasting、Electronic Commerce Research and Applications等权威期刊发表学术论文，曾参与国家自然科学基金项目。

中央财经大学  经济学博士

贵州大学     金融硕士

华南理工大学  土木工程学士

研究方法主要包括机器学习、深度学习、图神经网络、可解释性算法、复杂网络等

代表论文：

[1]Liang, L., & Cai, X*. (2022). Time-sequencing European options and pricing with deep learning–Analyzing based on interpretable ALE method. Expert Systems with Applications, 187, 115951. (SCI, JCR Q1, 中科院一区 Top, IF 7.5, 通讯作者)

[2]Yu, J., Cai, X., Ji, X., Liang, L., & Yang, J. (2025). Promoting urban energy transitions: Lessons from interpretable machine learning with evidence from China. Energy, 334, 137812. https://doi.org/10.1016/j.energy.2025.137812  (SCI, JCR Q1,中科院一区 Top, IF 9.4, 共同第一作者)

[3]Liang, L., Liu, B., Su, Z. *, & Cai, X*. (2024), Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China. Journal of Forecasting. (SSCI, JCR Q1, IF 3.4, 通讯作者)

[4]Liang, L., & Cai, X. (2020). Forecasting peer-to-peer platform default rate with LSTM neural network. Electronic Commerce Research and Applications, 43, 100997. (SSCI, SCI, JCR Q1, IF 5.9)

[5]Wang, Y., Su, Z., Cai, X.,* & Yu, J. (2025). The dual carbon emission effects of digital economy: Evidence from China. Heliyon, 11(4). (SCI, JCR Q1, IF 3.4, 通讯作者)

[6]Su, Z., Cai, X., & Wu, Y*. (2023). Exchange rates forecasting and trend analysis after the COVID-19 outbreak: new evidence from interpretable machine learning. Applied Economics Letters, 30(15), 2052-2059. (SSCI, JCR Q3, IF 1.2)

Yu, J., Cai, X.,* Su, Z. (2025). Drivers of Energy Transitions and Future Trends: Evidence from East Asian Countries. In: Phoumin, H., Shi, X., Kimura, F. (eds) Navigating the Complexities of Energy Transitions in East Asia. Climate Change and Energy Transition. Springer, Singapore. (专著章节，通讯作者)