Foreign Exchange Trading with Support Vector Machines

機(jī)譯：外匯交易與支持矢量機(jī)器

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This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel functions are considered. Each SVM model is benchmarked against traditional forecasting techniques in order to ascertain its potential value as out-of-sample forecasting and quantitative trading tool. It is found that hyperbolic SVMs perform well in terms of forecasting accuracy and trading results via a simulated strategy. This supports the idea that SVMs are promising learning systems for coping with nonlinear classification tasks in the field of financial time series applications.

機(jī)譯：本文分析并檢查了支持向量機(jī)（SVM）模型的一般能力，以正確預(yù)測(cè)和貿(mào)易每日歐元匯率方向。考慮具有不同內(nèi)核功能的七種模型。每個(gè)SVM模型都是針對(duì)傳統(tǒng)預(yù)測(cè)技術(shù)的基準(zhǔn)測(cè)試，以確定其潛在價(jià)值作為樣本外預(yù)測(cè)和定量交易工具。結(jié)果發(fā)現(xiàn)，通過(guò)模擬策略，雙曲線SVMS在預(yù)測(cè)準(zhǔn)確性和交易結(jié)果方面表現(xiàn)良好。這支持SVMS在財(cái)務(wù)時(shí)間序列應(yīng)用領(lǐng)域應(yīng)對(duì)非線性分類任務(wù)的有前途的學(xué)習(xí)系統(tǒng)的想法。

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Christian Ullrich; Detlef Seese; Stephan Chalup;
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年度 2007
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原文格式 PDF
正文語(yǔ)種 eng
中圖分類

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Foreign Exchange Trading with Support Vector Machines

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