Financial forecasting using support vector machines

被引：274
|
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
  • [21] Using Support Vector Machines to Evaluate Financial Fate of Dotcoms
    Bose, Indranil
    Pal, Raktim
    PACIFIC ASIA CONFERENCE ON INFORMATION SYSTEMS 2005, SECTIONS 1-8 AND POSTER SESSIONS 1-6, 2005, : 521 - 528
  • [22] Financial Time Series Forecasting Using Support Vector Machine
    Gui, Bin
    Wei, Xianghe
    Shen, Qiong
    Qi, Jingshan
    Guo, Liqiang
    2014 TENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), 2014, : 39 - 43
  • [23] Flood stage forecasting with support vector machines
    Liong, SY
    Sivapragasam, C
    JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 2002, 38 (01): : 173 - 186
  • [24] Forecasting IBEX-35 moves using support vector machines
    Dunis, Christian L.
    Rosillo, Rafael
    de la Fuente, David
    Pino, Raul
    NEURAL COMPUTING & APPLICATIONS, 2013, 23 (01): : 229 - 236
  • [25] Forecasting Electric Vehicle Charging Demand using Support Vector Machines
    Xydas, E. S.
    Marmaras, C. E.
    Cipcigan, L. M.
    Hassan, A. S.
    Jenkins, N.
    2013 48TH INTERNATIONAL UNIVERSITIES' POWER ENGINEERING CONFERENCE (UPEC), 2013,
  • [26] Electric Load Forecasting using Support Vector Machines for Robust Regression
    De Cosmis, Sonia
    De Leone, Renato
    Kropat, Erik
    Meyer-Nieberg, Silja
    Pickl, Stefan
    EMERGING M&S APPLICATIONS IN INDUSTRY AND ACADEMIA SYMPOSIUM AND THE MODELING AND HUMANITIES SYMPOSIUM 2013 (EAIA AND MATH 2013) - 2013 SPRING SIMULATION MULTI-CONFERENCE (SPRINGSIM'13), 2013, 45 (05): : 72 - 79
  • [27] Streamflow forecasting using least-squares support vector machines
    Shabri, Ani
    Suhartono
    HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2012, 57 (07): : 1275 - 1293
  • [28] Electrical Load Forecasting Using Support Vector Machines: a Case Study
    Turkay, Belgin Emre
    Demren, Dilara
    INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, 2011, 6 (05): : 2411 - 2418
  • [29] Forecasting IBEX-35 moves using support vector machines
    Christian L. Dunis
    Rafael Rosillo
    David de la Fuente
    Raúl Pino
    Neural Computing and Applications, 2013, 23 : 229 - 236
  • [30] Effective forecasting of hourly typhoon rainfall using support vector machines
    Lin, Gwo-Fong
    Chen, Guo-Rong
    Wu, Ming-Chang
    Chou, Yang-Ching
    WATER RESOURCES RESEARCH, 2009, 45
12345