Multiple - output support vector regression with a firefly algorithm for 1 interval - valued stock price index forecasting
نویسندگان
چکیده
6 Highly accurate interval forecasting of a stock price index is fundamental to 7 successfully making a profit when making investment decisions, by providing a range 8 of values rather than a point estimate. In this study, we investigate the possibility of 9 forecasting an interval-valued stock price index series over short and long horizons 10 using multi-output support vector regression (MSVR). Furthermore, this study 11 proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for 12 determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally 13 traded broad market indices are used to compare the performance of the proposed 14 FA-MSVR method with selected counterparts. The quantitative and comprehensive 15 assessments are performed on the basis of statistical criteria, economic criteria, and 16 computational cost. In terms of statistical criteria, we compare the out-of-sample 17 forecasting using goodness-of-forecast measures and testing approaches. In terms of 18 economic criteria, we assess the relative forecast performance with a simple trading 19 strategy. The results obtained in this study indicate that the proposed FA-MSVR 20 method is a promising alternative for forecasting interval-valued financial time series. 21
منابع مشابه
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...
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