研究生workshop由首都经济贸易大学国际经济管理学院主办。主要内容：一是研究生报告前沿或经典文献，二是研究生报告自己的研究或研究设想。论坛宗旨是：为学院师生搭建一个学术交流平台，营造浓厚学术氛围；通过对经典论著或前沿文献的研讨，拓宽研究生的理论视野，提升研究生的前沿方法运用能力，帮助研究生提高论文写作质量。

　　本期workshop

　　报告人：崔同玮（2022级博士研究生）

　　导师：牛毅

　　报告论文：Zipf's Law for Cities: An Explanation, QJE, 1999

　　作者：Xavier Gabaix

　　

　　报告摘要：

　　Zipf's law is a very tight constraint on the class of admissible models of local growth. It says that for most countries the size distribution of cities strikingly fits a power law: the number of cities with populations greater than S is proportional to 1/S. Suppose that, at least in the upper tail, all cities follow some proportional growth process (this appears to be verified empirically). This automatically leads their distribution to converge to Zipf's law.

　　

　　报告人：王璞（2020级博士研究生）

　　导师：李鲲鹏

　　报告论文：Stock Price Prediction Using CNN-BiLSTM-Attention Model，Mathematics, 2023

　　作者：Jilin Zhang, Lishi Ye and Yongzeng Lai

　　

　　报告摘要：

　　Predicting stock prices precisely is challenging, because stock price data are characterized by high frequency, nonlinearity, and long memory.

　　Various forecasting methods(Classical time series；RF、CNN、LSTM，etc) have been proposed, each method can reach a certain level of accuracy but also has its limitations.

　　A CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices.The proposed model was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention.