题  目：Stock price prediction using CNN-BiLSTM-Attention model

主讲人：赖永增  教授

时  间：2025年6月10日上午10:00-12:00

地  点：段家滩校区 贵和楼 408

主讲人简介:

赖永增，加拿大劳瑞尔大学数学系教授，主要研究领域包括金融数学（衍生产品的定价与风险管理、金融计算、投资组合优化、随机分析在金融和保险中的应用）、微分方程在金融和经济学中的应用、蒙特卡洛和拟蒙特卡洛仿真方法及应用;机器学习及其应用，尤其在经济金融中的应用。他在Automatica，Computers & Operations Research，Economic Modeling，Expert Systems with Applications，Energy Economics，Finance Research Letters，Insurance Mathematics and Economics，Journal of Computational Finance，Nature-Humanities and Social Sciences Communications，North American Journal of Finance and Economics，Nonlinear Analysis，Resources Policy等国际期刊已经发表了70多篇论文。主持加拿大国家自然科学与工程基金多项。是两个学术杂志的副主编，及四十多个杂志的审稿人。

内容摘要：

Accurate stock price prediction is important in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but has limitations. This paper proposes a CNN-BiLSTM-Attention-based model to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural net work (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; finally, the final prediction results are output through the dense layer. The proposed method 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. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases.