Forecasting foreign exchange rates using kernel methods

详细信息	查看全文 | 推荐本文 |

• 作者：Martin Sewell^a ; ^{mvs25@cam.ac.uk} ; John Shawe-Taylor^b ; ¹ ; ^{J.Shawe-Taylor@cs.ucl.ac.uk}
• 关键词：Forecasting ; Foreign exchange ; Kernel methods
• 刊名：Expert Systems with Applications
• 出版年：2012
• 期刊代码：62_09574174
• 类别：et
• 出版时间：July, 2012
• 卷：39
• 期：9
• 页码：7652-7662
• 文件大小：534 K

摘要

First, the all-important no free lunch theorems are introduced. Next, kernel methods, support vector machines (SVMs), preprocessing, model selection, feature selection, SVM software and the Fisher kernel are introduced and discussed. A hidden Markov model is trained on foreign exchange data to derive a Fisher kernel for an SVM, the DC algorithm and the Bayes point machine (BPM) are also used to learn the kernel on foreign exchange data. Further, the DC algorithm was used to learn the parameters of the hidden Markov model in the Fisher kernel, creating a hybrid algorithm. The mean net returns were positive for BPM; and BPM, the Fisher kernel, the DC algorithm and the hybrid algorithm were all improvements over a standard SVM in terms of both gross returns and net returns, but none achieved net returns as high as the genetic programming approach employed by and published in . Two implementations of SVMs for Windows with semi-automated parameter selection are built.