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**Forecasting stock market movement direction with support vector machine.**
*(English)*
Zbl 1068.90077

Summary: Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.

### MSC:

90B60 | Marketing, advertising |

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\textit{W. Huang} et al., Comput. Oper. Res. 32, No. 10, 2513--2522 (2005; Zbl 1068.90077)

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### References:

[1] | Abu-Mostafa, Y.S.; Atiya, A.F., Introduction to financial forecasting, Applied intelligence, 6, 205-213, (1996) |

[2] | Hall, J.W., Adaptive selection of US stocks with neural nets, (), 45-65 |

[3] | Blank, S.C., Chaos in futures market? a nonlinear dynamical analysis, Journal of futures markets, 11, 711-728, (1991) |

[4] | DeCoster, G.P.; Labys, W.C.; Mitchell, D.W., Evidence of chaos in commodity futures prices, Journal of futures markets, 12, 291-305, (1992) |

[5] | Frank, M.; Stengos, T., Measuring the strangeness of gold and silver rates of return, The review of economic studies, 56, 553-567, (1989) |

[6] | Vapnik, V.N., Statistical learning theory, (1998), Wiley New York · Zbl 0934.62009 |

[7] | Vapnik, V.N., An overview of statistical learning theory, IEEE transactions of neural networks, 10, 988-999, (1999) |

[8] | Cristianini, N.; Taylor, J.S., An introduction to support vector machines and other kernel-based learning methods, (2000), Cambridge University Press New York |

[9] | Cao, L.J.; Tay, F.E.H., Financial forecasting using support vector machines, Neural computing applications, 10, 184-192, (2001) · Zbl 1002.68689 |

[10] | Tay, F.E.H.; Cao, L.J., Application of support vector machines in financial time series forecasting, Omega, 29, 309-317, (2001) |

[11] | Tay, F.E.H.; Cao, L.J., A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines, Intelligent data analysis, 5, 191-209, (2001) · Zbl 1088.68749 |

[12] | Tay, F.E.H.; Cao, L.J., Improved financial time series forecasting by combining support vector machines with self-organizing feature map, Intelligent data analysis, 5, 339-354, (2001) · Zbl 1083.91567 |

[13] | Tay, F.E.H.; Cao, L.J., Modified support vector machines in financial time series forecasting, Neurocomputing, 48, 847-861, (2002) · Zbl 1006.68777 |

[14] | Burges, C., A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery, 2, 121-167, (1998) |

[15] | Evgeniou, T.; Pontil, M.; Poggio, T., Regularization networks and support vector machines, Advances in computational mathematics, 13, 1-50, (2000) · Zbl 0939.68098 |

[16] | Fletcher, R., Practical methods of optimization, (1987), Wiley New York · Zbl 0905.65002 |

[17] | Vapnik, V.N., The nature of statistical learning theory, (1995), Springer New York · Zbl 0934.62009 |

[18] | Smola AJ. Learning with kernels. PhD Dissertation, GMD, Birlinghoven, Germany, 1998. |

[19] | Ross, S., The arbitrage theory of capital asset pricing, Journal of economic theory, 13, 341-360, (1976) |

[20] | Fama, E.; Schwert, W., Asset returns and inflation, Journal of financial economics, 5, 115-146, (1977) |

[21] | Campbell, J., Stock returns and the term structure, Journal of financial economics, 18, 373-399, (1987) |

[22] | Chen, N.; Roll, R.; Ross, S., Economic forces and the stock market, Journal of business, 59, 383-403, (1986) |

[23] | Fama, E.; French, K., Dividend yields and expected stock returns, Journal of financial economics, 22, 3-25, (1988) |

[24] | Fama, E.; French, K., Permanent and temporary components of stock prices, Journal of political economy, 96, 246-273, (1988) |

[25] | Fama, E.; French, K., The cross-section of expected stock returns, Journal of finance, 47, 427-465, (1992) |

[26] | Lakonishok, J.; Shleifer, A.; Vishny, R.W., Contrarian investment, extrapolation, and risk, Journal of finance, 49, 1541-1578, (1994) |

[27] | Leung, M.T.; Daouk, H.; Chen, A.S., Forecasting stock indicesa comparison of classification and level estimation models, International journal of forecasting, 16, 173-190, (2000) |

[28] | Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C., Multivariate data analysis, (1995), Prentice-Hall New York |

[29] | Elman, J.L., Finding structure in time, Cognitive science, 14, 179-211, (1990) |

[30] | Kasabov N, Watts M. Spatial-temporal adaptation in evolving fuzzy neural networks for on-line adaptive phoneme recognition. Technical Report TR99/03, Department of Information Science, University of Otago, 1999. |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.