Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Multi-dimensional Function Approximation and Regression Estimation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
iMLP: Applying Multi-Layer Perceptrons to Interval-Valued Data
Neural Processing Letters
Forecasting models for interval-valued time series
Neurocomputing
Long-term prediction of time series by combining direct and MIMO strategies
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A soft computing system for day-ahead electricity price forecasting
Applied Soft Computing
Expert Systems with Applications: An International Journal
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
MISMIS - A comprehensive decision support system for stock market investment
Knowledge-Based Systems
Forecasting Taiwan's major stock indices by the Nash nonlinear grey Bernoulli model
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Different Approaches to Forecast Interval Time Series: A Comparison in Finance
Computational Economics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems
IEEE Transactions on Signal Processing
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
IEEE Transactions on Neural Networks
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Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally traded broad market indices are used to compare the performance of the proposed FA-MSVR method with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of statistical criteria, economic criteria, and computational cost. In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches. In terms of economic criteria, we assess the relative forecast performance with a simple trading strategy. The results obtained in this study indicate that the proposed FA-MSVR method is a promising alternative for forecasting interval-valued financial time series.