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- W150751777 abstract "Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. The financial time series usually contains the characteristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have noticed that up margin and down margin would not necessary be the same, and also observed that their choice would affect the upside risk, downside risk and as well as the overall prediction performance. In this work, we introduce a novel approach to adopt the momentum in the asymmetrical margins setting. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average.Key wordsNon-fixed and Asymmetrical MarginMomentumSupport Vector RegressionFinancial Time Series Prediction" @default.
- W150751777 created "2016-06-24" @default.
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- W150751777 date "2004-01-01" @default.
- W150751777 modified "2023-10-18" @default.
- W150751777 title "Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression" @default.
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- W150751777 doi "https://doi.org/10.1007/978-3-540-39935-3_18" @default.
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