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关于举办铜价预测的深度学习方法讲座的通知
2019-12-12 10:43 管理可计算建模协同创新中心 

 目:A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper pric7e volatility prediction

主讲人:西南财经大学倪剑教授

 间:2019年12月16日下午3点

 点:E206

讲座内容:

Forecasting the copper price volatility is an important yet challenging task. Given the nonlinear and time-varying characteristics ofnumerous factors affecting the copper price, we propose a novel hybrid method to forecast copper price volatility. Two important techniques are synthesized in this method. One is the classic GARCH model which encodes useful statistical information about the time-varying copper price volatility in a compact form via the GARCH forecasts. The other is the powerful deep neural network which combines the GARCH forecasts with both domestic and international market factors to search for better nonlinear features; it also combines the long short-term memory (LSTM) network with traditional artificial neural network (ANN) to generate better volatility forecasts. Our method synthesizes the merits ofthese two techniques and is especially suitable for the task of copper price volatility prediction. The empirical results show thatthe GARCH forecasts can serve as informative features to significantly increase the predictive power of the neural network model, and the integration of the LSTM and ANN networks is an effective approach to construct useful deep neural network structures to boost the prediction performance. Further, we conducted a series of sensitivity analyses of the neural network architecture to optimize the prediction results. The results suggest that the choice between LSTM and BLSTM networks for the hybrid model should consider the forecast horizon, while the ANN configurations should be fine-tuned depending on the choice of the measure of prediction errors.  

主讲人简介:倪剑,西南财经大学金融学院教授、博士生导师,主要研究方向有金融科技,深度学习的金融应用研究,金融决策,金融与管理交叉学科等,在国际期刊上发表了多篇论文。

主办单位:管理可计算建模协同创新中心

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