Financial forecasting using support vector machines

被引：271
|
作者：
Cao, L [1 ]
Tay, FEH [1 ]
机构：
[1] Natl Univ Singapore, Dept Mech & Prod Engn, Singapore 117548, Singapore
来源：
NEURAL COMPUTING & APPLICATIONS | 2001年 / 10卷 / 02期
关键词：
back propagation algorithm; financial time series forecasting; generalisation; multi-layer perceptron; support vector machines;
D O I：
10.1007/s005210170010
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE). Mean Absolute Error (MAE), Directional Symmetry (DS) Correct Up (CP) trend and Correct Down (CD) trend S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial rime series.
引用
收藏
页码：184 / 192
页数：9
相关论文
共 50 条
  • [1] Financial Forecasting Using Support Vector Machines
    Lijuan Cao
    Francis E.H Tay
    [J]. Neural Computing & Applications, 2001, 10 : 184 - 192
  • [2] Financial time series forecasting using support vector machines
    Kim, KJ
    [J]. NEUROCOMPUTING, 2003, 55 (1-2) : 307 - 319
  • [3] Modified support vector machines in financial time series forecasting
    Tay, FEH
    Cao, LJ
    [J]. NEUROCOMPUTING, 2002, 48 : 847 - 861
  • [4] ε-Descending Support Vector Machines for Financial Time Series Forecasting
    Francis E. H. Tay
    L. J. Cao
    [J]. Neural Processing Letters, 2002, 15 : 179 - 195
  • [5] Support Vector Machines through Financial Time Series Forecasting
    Kewat, Pooja
    Sharma, Roopesh
    Singh, Upendra
    Itare, Ravikant
    [J]. 2017 INTERNATIONAL CONFERENCE OF ELECTRONICS, COMMUNICATION AND AEROSPACE TECHNOLOGY (ICECA), VOL 2, 2017, : 471 - 477
  • [6] ε-descending support vector machines for financial time series forecasting
    Tay, FEH
    Cao, LJ
    [J]. NEURAL PROCESSING LETTERS, 2002, 15 (02) : 179 - 195
  • [7] Application of support vector machines in financial time series forecasting
    Tay, FEH
    Cao, LJ
    [J]. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2001, 29 (04): : 309 - 317
  • [8] Flood forecasting using support vector machines
    Han, D.
    Chan, L.
    Zhu, N.
    [J]. JOURNAL OF HYDROINFORMATICS, 2007, 9 (04) : 267 - 276
  • [9] Financial Time Series Forecasting Using Hybridized Support Vector Machines and ARIMA Models
    Khairalla, Mergani A.
    Ning, Xu
    [J]. PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND APPLICATIONS (WCNA2017), 2017, : 94 - 98
  • [10] Rejection Based Support Vector Machines for Financial Time Series Forecasting
    Rosowsky, Yasin I.
    Smith, Robert E.
    [J]. 2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2013,
12345