Financial Forecasting with Gompertz Multiple Kernel Learning

Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from multiple data sources is still one of the main challenges in the financial forecasting area. In this paper, we focus on applying the multiple kernel learning models to the multiple major international stock indexes. Our experiment results indicate that applying multiple kernel learning to the financial forecasting problem suffers from both the short training period problem and nonstationary problem. Therefore we propose a novel multiple kernel learning model to address the challenge by introducing the Gompertz model and considering a non-linear combination of different kernel matrices. The e...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Han Qin, Dejing Dou, Yue Fang
• Keywords: Data Mining, Financial Forecasting, Gompertz Multiple Kernel, ICDM 2010, Multiple Kernel