## AI帮你理解科学

## AI 精读

AI抽取本论文的概要总结

微博一下：

# Financial time series forecasting using support vector machines

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

EI

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

代码：

数据：

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

- 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

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

引用论文

- S.B. Achelis, Technical Analysis from A to Z, Probus Publishing, Chicago, 1995.
- H. Ahmadi, Testability of the arbitrage pricing theory by neural networks, in: Proceedings of the International Conference on Neural Networks, San Diego, CA, 1990, pp. 385 –393.
- J. Chang, Y. Jung, K. Yeon, J. Jun, D. Shin, H. Kim, Technical Indicators and Analysis Methods, Jinritamgu Publishing, Seoul, 1996.
- C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, 2001, Available at http://www.csie.edu.tw/∼cjlin/papers/libsvm.pdf.
- J. Choi, Technical Indicators, Jinritamgu Publishing, Seoul, 1995.
- J.H. Choi, M.K. Lee, M.W. Rhee, Trading S& P 500 stock index futures using a neural network, in: Proceedings of the Annual International Conference on Artiÿcial Intelligence Applications on Wall Street, New York, 1995, pp. 63–72.
- D.R. Cooper, C.W. Emory, Business Research Methods, Irwin, Chicago, 1995.
- H. Drucker, D. Wu, V.N. Vapnik, Support vector machines for spam categorization, IEEE Trans. Neural Networks 10 (5) (1999) 1048–1054.
- E. Gi ord, Investor’s Guide to Technical Analysis: Predicting Price Action in the Markets, Pitman Publishing, London, 1995.
- Y. Hiemstra, Modeling structured nonlinear knowledge to predict stock market returns, in: R.R. Trippi (Ed.), Chaos & Nonlinear Dynamics in the Financial Markets: Theory, Evidence and Applications, Irwin, Chicago, IL, 1995, pp. 163–175.
- K. Kamijo, T. Tanigawa, Stock price pattern recognition: a recurrent neural network approach, in: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 215 –221.
- K. Kim, I. Han, Genetic algorithms approach to feature discretization in artiÿcial neural networks for the prediction of stock price index, Expert Syst. Appl. 19 (2) (2000) 125–132.
- T. Kimoto, K. Asakawa, M. Yoda, M. Takeoka, Stock market prediction system with modular neural network, in: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 1– 6.
- K. Kohara, T. Ishikawa, Y. Fukuhara, Y. Nakamura, Stock price prediction using prior knowledge and neural networks, Int. J. Intell. Syst. Accounting Finance Manage. 6 (1) (1997) 11–22.
- S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector machines, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, 1997, pp. 511–520.
- J.J. Murphy, Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications, Prentice-Hall, New York, 1986.
- T.-S. Quah, B. Srinivasan, Improving returns on stock investment through neural network selection, Expert Syst. Appl. 17 (1999) 295–301.
- F.E.H. Tay, L. Cao, Application of support vector machines in ÿnancial time series forecasting, Omega 29 (2001) 309–317.
- R.R. Trippi, D. DeSieno, Trading equity index futures with a neural network, J. Portfolio Manage. 19 (1992) 27–33.
- R. Tsaih, Y. Hsu, C.C. Lai, Forecasting S& P 500 stock index futures with a hybrid AI system, Decision Support Syst. 23 (2) (1998) 161–174.
- V.N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
- I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, San Francisco, CA, 2000.
- Y. Yoon, G. Swales, Predicting stock price performance: a neural network approach, in: Proceedings of the 24th Annual Hawaii International Conference on System Sciences, Hawaii, 1991, pp. 156 –162.
- G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artiÿcial neural networks: the state of the art, Int. J. Forecasting 14 (1998) 35–62.

标签

评论

数据免责声明

页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果，我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问，可以通过电子邮件方式联系我们：report@aminer.cn