Elsevier

Neurocomputing

Volume 55, Issues 1–2, September 2003, Pages 307-319
Neurocomputing

Financial time series forecasting using support vector machines

https://doi.org/10.1016/S0925-2312(03)00372-2Get rights and content

Abstract

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 examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

Introduction

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks (ANNs) in this area. A large number of successful applications have shown that ANN can be a very useful tool for time-series modeling and forecasting [24]. The early days of these studies focused on application of ANNs to stock market prediction (for instance [2], [6], [11], [13], [19], [23]). Recent research tends to hybridize several artificial intelligence (AI) techniques (for instance [10], [22]). Some researchers tend to include novel factors in the learning process. Kohara et al. [14] incorporated prior knowledge to improve the performance of stock market prediction. Tsaih et al. [20] integrated the rule-based technique and ANN to predict the direction of the S&P 500 stock index futures on a daily basis.

Quah and Srinivasan [17] proposed an ANN stock selection system to select stocks that are top performers from the market and to avoid selecting under performers. They concluded that the portfolio of the proposed model outperformed the portfolios of the benchmark model in terms of compounded actual returns overtime. 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, however, showed that ANN had some limitations in learning the patterns because stock market data has tremendous noise and complex dimensionality. ANN often exhibits inconsistent and unpredictable performance on noisy data. However, back-propagation (BP) neural network, the most popular neural network model, suffers from difficulty in selecting a large number of controlling parameters which include relevant input variables, hidden layer size, learning rate, momentum term.

Recently, a support vector machine (SVM), a novel neural network algorithm, was developed by Vapnik and his colleagues [21]. Many traditional neural network models had implemented the empirical risk minimization principle, SVM implements the structural risk minimization principle. The former seeks to minimize the mis-classification error or deviation from correct solution of the training data but the latter searches to minimize an upper bound of generalization error. In addition, the solution of SVM may be global optimum while other neural network models may tend to fall into a local optimal solution. Thus, overfitting is unlikely to occur with SVM.

This paper applies SVM to predicting stock price index. In addition, this paper examines the feasibility of applying SVM in financial forecasting by comparing it with ANN and case-based reasoning (CBR).

This paper consists of five sections. Section 2 introduces the basic concept of SVM and their applications in finance. Section 3 proposes a SVM approach to the prediction of stock price index. Section 4 describes research design and experiments. In Section 4, empirical results are summarized and discussed. Section 5 presents the conclusions and limitations of this study.

Section snippets

SVMs and their applications in finance

The following presents some basic concepts of SVM theory as described by prior research. A detailed explanation may be found in the references in this paper.

Research data

The research data used in this study is technical indicators and the direction of change in the daily Korea composite stock price index (KOSPI). 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

Experimental results

One of the advantages of linear SVM is that there is no parameter to tune except the constant C. But the upper bound C on the coefficient αi affects prediction performance for the cases where the training data is not separable by a linear SVM [8]. For the nonlinear SVM, there is an additional parameter, the kernel parameter, to tune. First, this study uses two kernel functions including the Gaussian radial basis function and the polynomial function. The polynomial function, however, takes a

Conclusions

This study used SVM to predict future direction of stock price index. In this study, the effect of the value of the upper bound C and the kernel parameter δ2 in SVM was investigated. The experimental result showed that the prediction performances of SVMs are sensitive to the value of these parameters. Thus, it is important to find the optimal value of the parameters.

In addition, this study compared SVM with BPN and CBR. The experimental results showed that SVM outperformed BPN and CBR. The

Acknowledgements

This work was supported by the Dongguk University Research Fund.

Kyoung-jae Kim received his M.S. and Ph.D. degrees in Management Information Systems from the Graduate School of Management at the Korea Advanced Institute of Science and Technology and his B.A. degree from the Chung-Ang University. He is currently a faculty member of the Department of Information Systems at the Dongguk University. His research interests include data mining, knowledge management, and intelligent agents.

References (24)

  • J.H. Choi, M.K. Lee, M.W. Rhee, Trading S&P 500 stock index futures using a neural network, in: Proceedings of the...
  • D.R. Cooper et al.

    Business Research Methods

    (1995)
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    Kyoung-jae Kim received his M.S. and Ph.D. degrees in Management Information Systems from the Graduate School of Management at the Korea Advanced Institute of Science and Technology and his B.A. degree from the Chung-Ang University. He is currently a faculty member of the Department of Information Systems at the Dongguk University. His research interests include data mining, knowledge management, and intelligent agents.

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