M. Serdar Yümlü and Fikret S. Gürgen
Support Vector Predictors (SVP), Financial Time Series Prediction (FTSP), Artificial Neural Networks (ANN)
This paper makes a comparison of support vector predictors and neural prediction models for the financial time series prediction problem. By considering unique and globally optimal property, we have used SVP as a prediction approach to financial time series forecasting. This paper discusses the advantages and disadvantages of each model by using a real-world data: 22 years Istanbul Stock Exchange ISE 100 index data from 1988 to 2010. The comparison for each model is done based on well-known criterions of index return series of market. Several performance metrics are used to compare these models including regression metrics, prediction trend accuracy metrics and special metrics such as Spearman’s and Kendall’s rank correlation coefficients Finally, it is observed that the support vector predictors becomes advantageous in capturing volatility in index return series when it is compared to global and feedback neural models.
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