Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression
نویسندگان
چکیده
Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are nonstationary and noisy in nature. The volatility, usually time-varying, of the time series therefore contains some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively changing the width of the margin in SVR. We have noticed that upside margin and downside margin would not necessary be the same, and we have 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 adapt the asymmetrical margins using momentum. We applied and compared this method to predict the Hang Seng Index and Dow Jones Industrial Average.
منابع مشابه
Non-fixed and Asymmetrical Margin Approach to Stock Market Prediction Using Support Vector Regression
Recently, Support Vector Regression (SVR) has been applied to financial time series prediction. Typical characteristics of financial time series are non-stationary and noisy in nature. The volatility, usually time-varying, of the time series is therefore some valuable information about the series. Previously, we had proposed to use the volatility to adaptively change the width of the margin of ...
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