李维

基本信息 科学研究 新建主栏目 教育经历 名称 2018.08-2021.12： 挪威科技大学, 金融计算，博士 2016.09-2017.09： 伦敦玛丽女王大学，金融计算，硕士 2015.09-2016.09： 格拉斯哥大学，金融预测与投资，硕士 2007.09-2009.07： 吉林大学，工商管理，学士 2005.09-2009.07： 吉林大学，动物科学，学士 工作经历 名称 2024.03-至今： 哈尔滨工业大学，经济管理学院, 副教授 2022.02-2024.02: 新加坡国立大学，商业分析与运营学院, 博士后研究员 2021.02-2022.01： 柏林洪堡大学，经济商学院，访问学者 研究方向 名称 金融计算，可解释性机器学习，能源金融，供应链金融 期刊论文 名称 [1] Li, W.*, Paraschiv, F. (2021). Modelling the Evolution of Wind and Solar Power Infeed Fore- casts. Journal of Commodity Markets. DOI: https://doi.org/10.1016/j.jcomm.2021.100189. [2] Li, W.*, Becker, M. D. (2021). Day-ahead Electricity Price Prediction Applying Hybrid Models of LSTM-based Deep Learning Methods and Feature Selection Algorithms under Consideration of Market Coupling. Energy. DOI: https://doi.org/10.1016/j.energy.2021.121543. [3] 王茹婷，彭方平*，李维，王春丽 (2022). 打破刚性兑付能降低企业融资成本吗? 管理世界. DOI: https://doi.org/10.19744/ j.cnki.11-1235/f.2022.0051. [4] Li, W., Paraschiv, F., Sermpinis, G.* (2022). A Data-driven Explainable Case-based Reasoning for Financial Risk Detection. Quantitative Finance. DOI: https://doi.org/10.1080/14697688. 2022.2118071. [5] Sun, Q., Li, W., Meng, Q.* (2024). Single-leg shipping revenue management for expedited services with ambiguous elasticity in transit-time-sensitive demand. Transportation Research Part B: Methodological. DOI: https://doi.org/10.1016/j.trb.2024.10288. 1. Li, W.*, Paraschiv, F. (2021). Modelling the Evolution of Wind and Solar Power Infeed Fore-casts. Journal of Commodity Markets. DOI: https://doi.org/10.1016/j.jcomm.2021.100189.2. Li, W.*, Becker, M. D. (2021). Day-ahead Electricity Price Prediction Applying Hyb Li, W.*, Paraschiv, F. (2021). Modelling the Evolution of Wind and Solar Power Infeed Fore- casts. Journal of Commodity Markets. DOI: https://doi.org/10.1016/j.jcomm.2021.100189. 2. Li, W.*, Becker, M. D. (2021). Day-ahead Electricity Price Prediction Applying Hybrid Models of LSTM-based Deep Learning Methods and Feature Selection Algorithms under Consideration of Market Coupling. Energy. DOI: https://doi.org/10.1016/j.energy.2021.121543. 3. Wang, R.T., Peng, F.P.*, Li, W., Wang, C.L. (2022). Does Terminating Rigid Payment Diminish Financing Cost? Management World. (Language: Chinese). DOI: https://doi.org/10.19744/ j.cnki.11-1235/f.2022.0051. 4. Li, W., Paraschiv, F., Sermpinis, G.* (2022). A Data-driven Explainable Case-based Reasoning for Financial Risk Detection. Quantitative Finance. DOI: https://doi.org/10.1080/14697688. 2022.2118071.of LSTM-based Deep Learning Methods and Feature Selection Algorithms under Consideration ofMarket Coupling. Energy. DOI: https://doi.org/10.1016/j.energy.2021.121543. Modelsof LSTM-based Deep Learning Methods and Feature Selection Algorithms under Consideration ofMarket Coupling. Energy. DOI: https://doi.org/10.1016/j.energy.2021.121543.3. Wang, R.T., Peng, F.P.*, Li, W., Wang, C.L. (2022). Does Terminating Rigid Payment DiminishFinancing Cost? Management World. (Language: Chinese). DOI: https://doi.org/10.19744/j.cnki.11-1235/f.2022.0051.4. Li, W., Paraschiv, F., Sermpinis, G.* (2022). A Data-driven Explainable Case-based Reasoningfor Financial Risk Detection. Quantitative Finance. DOI: https://doi.org/10.1080/14697688.2022.2118071.