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The results may be attributable to the fact that support vector machine implements the structural risk minimization principle and this leads to better generalization than conventional techniques

Financial time series forecasting using support vector machines

Neurocomputing, no. 1 (2003): 307-319

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摘要

Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study exa...更多

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简介
  • Stock market prediction is regarded as a challenging task of ÿnancial time-series prediction.
  • Kim and Han [12] proposed a genetic algorithms approach to feature discretization and the determination of connection weights for ANN to predict the stock price index.
  • They suggested that their approach reduced the dimensionality of the feature space and enhanced the prediction performance.
  • Some of these studies, showed that ANN had some limitations in learning the patterns because stock market data has tremendous noise and complex dimensionality.
重点内容
  • Stock market prediction is regarded as a challenging task of ÿnancial time-series prediction
  • The research data used in this study is technical indicators and the direction of change in the daily Korea composite stock price index (KOSPI)
  • The experimental result showed that the prediction performances of support vector machine (SVM) are sensitive to the value of these parameters
  • The experimental results showed that SVM outperformed BPN and case-based reasoning (CBR)
  • The results may be attributable to the fact that SVM implements the structural risk minimization principle and this leads to better generalization than conventional techniques
  • This study concluded that SVM provides a promising alternative for ÿnancial time-series forecasting
表格
  • Table1: Initially selected features and their formulas
  • Table2: Summary statistics
  • Table3: The prediction performance of various parameters in SVMs
  • Table4: The results of various BP models
  • Table5: The best prediction performances of SVM, BP, and CBR (hit ratio: %)
  • Table6: McNemar values (p values) for the pairwise comparison of performance
Download tables as Excel
基金
  • This work was supported by the Dongguk University Research Fund
研究对象与分析
technical indicators: 12
Since we attempt to forecast the direction of daily price change in the stock price index, technical indicators are used as input variables. This study selects 12 technical indicators to make up the initial attributes, as determined by the review of domain experts and prior research [12]. The descriptions of initially selected attributes are presented in Table 1

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