Application of support vector machines in financial time series forecasting
OmegaPublished 1 August 2001Open access
Francis E. H. Tay, Lijuan Cao
Citations1,242
SJR quartileQ4
SJR score0.11
SNIP0.06
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast !
Abstract
10.1016/S0305-0483(01)00026-3
Keywords
Computer ScienceDecision SciencesEngineering
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
Neural Networks: A Comprehensive Foundation
29,816 Citations1998Simon Haykin
Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
2,702 Citations1996Vladimir Vapnik, Steven E. Golowich +1 more
This presentation reports results of applying the Support Vector method to problems of estimating regressions, constructing multidimensional splines, and solving linear operator equations.
Lecture notes in computer sciencePredicting time series with support vector machines
977 Citations1997K. Müller, Alexander J. Smola +4 more
Two different cost functions for Support Vectors are made use: training with an e insensitive loss and Huber's robust loss function and how to choose the regularization parameters in these models are discussed.
Stock market prediction system with modular neural networks
652 Citations1990Tsunenobu Kimoto, Kazuo Asakawa +2 more
The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system, which achieved accurate predictions, and the simulation on stocks trading showed an excellent profit.
Nonlinear prediction of chaotic time series using support vector machines
525 Citations2002Sayan Mukherjee, E. Osuna +1 more
The SVM is implemented and tested on a database of chaotic time series previously used to compare the performances of different approximation techniques, including polynomial and rational approximation, localPolynomial techniques, radial basis functions, and neural networks; the SVM performs better than the other approaches.
Technische Universität Dortmund Eldorado (Technische Universität Dortmund)Text categorization with support vector machines
507 Citations1997Thorsten Joachims
Applied IntelligenceIntroduction to financial forecasting
400 Citations1996Yaser S. Abu‐Mostafa, Amir F. Atiya
Using neural networks as a learning paradigm, different techniques for choosing the inputs, outputs, and error function are described and the learning from hints technique that augments the standard learning from examples method is described.
OmegaNeural network forecasting of the British Pound/US Dollar exchange rate
288 Citations1998Gioqinang Zhang, Michael Y. Hu
The effects of the number of input and hidden nodes as well as the size of the training sample on the in-sample and out-of-sample performance of neural network forecasting are examined.
Journal of Intelligent ManufacturingConnectionist approach to time series prediction: an empirical test
181 Citations1992Ramesh Sharda, Rajendra B. Patil
The results show that the simple neural net model tested on this set of time series could forecast about as well as the Box-Jenkins automatic forecasting expert system.
OmegaA neural network approach to mutual fund net asset value forecasting
172 Citations1996Wen‐Chyuan Chiang, Timothy L. Urban +1 more
It is shown that neural networks significantly outperform regression models in situations with limited data availability and are compared to those of traditional econometric techniques.
HAL (Le Centre pour la Communication Scientifique Directe)Wavelet-based feature extraction and decomposition strategies for financial forecasting
159 Citations1998Alexandre Aussem, Jonathan Campbell +1 more
"Forecasting the 30-year U.S. Treasury Bond with a System of Neural Networks"
31 Citations2000Wei Cheng, Chien-Hua Mike Lin
The methods used for data selection, training and testing, the basic system architecture, and how the decision model improved the total system accuracy as compared to individual networks are described.
Microsoft C/C++ 7: The Complete Reference
7 Citations1992William H. Murray, Chris H. Pappas
This book teaches the user to use Microsoft C/C++ 7 by teaching basic concepts and advanced techniques and applications that the reader need to know to be able to code in this environment.