Conference Paper

A Hybrid Unscented Kalman Filter and Support Vector Machine Model in Option Price Forecasting.

DOI: 10.1007/11881070_44 Conference: Advances in Natural Computation, Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006. Proceedings, Part I
Source: DBLP

ABSTRACT This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement
an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based
on the Black-Scholes formula, while the SVM is employed to capture the nonlinear residuals between the actual option prices
and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy
of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models
in terms of three types of options. This model can also help investors for reducing their risk in online trading.

0 Followers
 · 
112 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Urinary incontinence is one of the largest diseases affecting between 10% and 30% of the adult population and an increase is expected in the next decade with rising treatment costs as a consequence. There are many types of urological dysfunctions causing urinary incontinence, which makes cheap and accurate diagnosing an important issue. This paper proposes a support vector machine (SVM) based method for diagnosing urological dysfunctions. 381 registers collected from patients suffering from a variety of urological dysfunctions have been used to ensure the (generalization) performance of the decision support system. Moreover, the robustness of the proposed system is examined by fivefold cross-validation and the results show that the SVM-based method can achieve an average classification accuracy at 84.25%.
    Expert Systems with Applications 06/2010; 37(6-37):4713-4718. DOI:10.1016/j.eswa.2009.12.055 · 1.97 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Due to the implicit characteristics of learning disabilities (LD), the identification or diagnosis of students with learning disabilities has long been a difficult issue. The LD diagnosis procedure usually involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In this paper, we apply two well-known artificial intelligence techniques, artificial neural network (ANN) and support vector machine (SVM), to the LD diagnosis problem. To improve the overall identification accuracy, we also experiment with GA-based feature selection algorithms as the pre-processing step. To the best of our knowledge, this is the first attempt in applying ANN or SVM to similar application. The experimental results show that ANN in general performs better than SVM in this application, and the wrapper-based GA feature selection procedure can improve the LD identification accuracy, and among all, the combination of using SVM learner in the feature selection procedure and ANN learner in the classification stage results in feature set that achieves the best prediction accuracy. Most important of all, the study indicates that the ANN classifier can correctly identify up to 50% of the LD students with 100% confidence, which is much better than currently used LD diagnosis predictors derived through the statistical method. Consequently, a properly trained ANN classification model can be a strong predictor for use in the LD diagnosis procedure. Furthermore, a well-trained ANN model can also be used to verify whether a LD diagnosis procedure is adequate. In conclusion, we expect that AI techniques like ANN or SVM will certainly play an essential role in future LD diagnosis applications.
    Expert Systems with Applications 04/2008; 34(3-34):1846-1856. DOI:10.1016/j.eswa.2007.02.026 · 1.97 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This study combines a modified Kalman filter (MKF) and support vector machines (SVMs, a type of kernel machines) to implement a fast online predictor for option prices. The latent variables in Black-Scholes formula are estimated by the MKF. The residuals in MKF predictions are handled by an SVM. Using option data of Taiwan Futures Exchange, the proposed model is compared with traditional predictors. Empirical results confirmed that the new model is superior to traditional neural network models, which remarkably reduce the root-mean-squared forecasting errors.
    10/2013; 16(2):163-176. DOI:10.1080/09720510.2013.777575