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摘要翻译：
多核学习(MKL)用于复制交易规则在预测资产价格变化时聚合多个金融信息来源时所体现的信号组合过程。为EURUSD货币对构建了一组金融驱动的内核，并用于预测该货币在多个时间范围内的价格移动方向。MKL在预测精度方面优于每一个核。此外，MKL选择的内核权重突出了内核所代表的金融特征中哪些对预测任务最有信息。
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英文标题：
《Currency Forecasting using Multiple Kernel Learning with Financially
Motivated Features》
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作者：
Tristan Fletcher, Zakria Hussain and John Shawe-Taylor
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最新提交年份：
2010
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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英文摘要：
Multiple Kernel Learning (MKL) is used to replicate the signal combination process that trading rules embody when they aggregate multiple sources of financial information when predicting an asset's price movements. A set of financially motivated kernels is constructed for the EURUSD currency pair and is used to predict the direction of price movement for the currency over multiple time horizons. MKL is shown to outperform each of the kernels individually in terms of predictive accuracy. Furthermore, the kernel weightings selected by MKL highlights which of the financial features represented by the kernels are the most informative for predictive tasks.
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PDF链接：
https://arxiv.org/pdf/1011.6097
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