The Experts below are selected from a list of 32373 Experts worldwide ranked by ideXlab platform
Kyoungjae Kim - One of the best experts on this subject based on the ideXlab platform.
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simultaneous optimization of artificial neural networks for Financial Forecasting
Applied Intelligence, 2012Co-Authors: Kyoungjae Kim, Hyunchul AhnAbstract:Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. However, they often result in inconsistent and unpredictable performance in the prediction of noisy Financial data due to the problems of determining factors involved in design. Prior studies have suggested genetic algorithm (GA) to mitigate the problems, but most of them are designed to optimize only one or two architectural factors of ANN. With this background, the paper presents a global optimization approach of ANN to predict the stock price index. In this study, GA optimizes multiple architectural factors and feature transformations of ANN to relieve the limitations of the conventional backpropagation algorithm synergistically. Experiments show our proposed model outperforms conventional approaches in the prediction of the stock price index.
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artificial neural networks with evolutionary instance selection for Financial Forecasting
Expert Systems With Applications, 2006Co-Authors: Kyoungjae KimAbstract:In this paper, I propose a genetic algorithm (GA) approach to instance selection in artificial neural networks (ANNs) for Financial data mining. ANN has preeminent learning ability, but often exhibit inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large. In this paper, the GA optimizes simultaneously the connection weights between layers and a selection task for relevant instances. The globally evolved weights mitigate the well-known limitations of gradient descent algorithm. In addition, genetically selected instances shorten the learning time and enhance prediction performance. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in ANN.
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Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence, 2004Co-Authors: Kyoungjae KimAbstract:This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for Financial Forecasting. Prior research proposed many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most research used the GA for improving only a part of architectural factors of the CBR model. However, the performance of the CBR model may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of the CBR model outperforms other conventional approaches for Financial Forecasting.
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Financial time series Forecasting using support vector machines
Neurocomputing, 2003Co-Authors: Kyoungjae KimAbstract:Abstract Support vector machines (SVMs) are promising methods for the prediction of Financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in Financial Forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
Francis E. H. Tay - One of the best experts on this subject based on the ideXlab platform.
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support vector machine with adaptive parameters in Financial time series Forecasting
IEEE Transactions on Neural Networks, 2003Co-Authors: L J Cao, Francis E. H. TayAbstract:A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in Financial time series Forecasting. The feasibility of applying SVM in Financial Forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of Financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in Financial Forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in Financial Forecasting.
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Financial Forecasting Using Support Vector Machines
Neural Computing & Applications, 2001Co-Authors: Lijuan Cao, Francis E. H. TayAbstract: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 time series.
Hüseyin Ince - One of the best experts on this subject based on the ideXlab platform.
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support vector machine for regression and applications to Financial Forecasting
International Joint Conference on Neural Network, 2000Co-Authors: Theodore B. Trafalis, Hüseyin InceAbstract:The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for Financial Forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.
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IJCNN (6) - Support vector machine for regression and applications to Financial Forecasting
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for, 2000Co-Authors: Theodore B. Trafalis, Hüseyin InceAbstract:The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for Financial Forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.
Theodore B. Trafalis - One of the best experts on this subject based on the ideXlab platform.
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support vector machine for regression and applications to Financial Forecasting
International Joint Conference on Neural Network, 2000Co-Authors: Theodore B. Trafalis, Hüseyin InceAbstract:The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for Financial Forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.
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IJCNN (6) - Support vector machine for regression and applications to Financial Forecasting
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for, 2000Co-Authors: Theodore B. Trafalis, Hüseyin InceAbstract:The main purpose of the paper is to compare the support vector machine (SVM) developed by Cortes and Vapnik (1995) with other techniques such as backpropagation and radial basis function (RBF) networks for Financial Forecasting applications. The theory of the SVM algorithm is based on statistical learning theory. Training of SVMs leads to a quadratic programming (QP) problem. Preliminary computational results for stock price prediction are also presented.
Edward Tsang - One of the best experts on this subject based on the ideXlab platform.
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CIFEr - Guided Fast Local Search for speeding up a Financial Forecasting algorithm
2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014Co-Authors: Ming Shao, Michael Kampouridis, Dafni Smonou, Edward TsangAbstract:Guided Local Search is a powerful meta-heuristic algorithm that has been applied to a successful Genetic Programming Financial Forecasting tool called EDDIE. Although previous research has shown that it has significantly improved the performance of EDDIE, it also increased its computational cost to a high extent. This paper presents an attempt to deal with this issue by combining Guided Local Search with Fast Local Search, an algorithm that has shown in the past to be able to significantly reduce the computational cost of Guided Local Search. Results show that EDDIE's computational cost has been reduced by an impressive 77%, while at the same time there is no cost to the predictive performance of the algorithm.
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CIFEr - Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm
2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014Co-Authors: Babatunde Aluko, Michael Kampouridis, Dafni Smonou, Edward TsangAbstract:Hyper-heuristics have successfully been applied to a vast number of search and optimization problems. One of the novelties of hyper-heuristics is the fact that they manage and automate the meta-heuristic's selection process. In this paper, we implemented and analyzed a hyper-heuristic framework on three meta-heuristics namely Simulated Annealing, Tabu Search, and Guided Local Search, which had successfully been applied in the past to a Financial Forecasting algorithm called EDDIE. EDDIE uses Genetic Programming to extract and learn from historical data in order to predict future Financial market movements. Results show that the algorithm's effectiveness has been improved, thus making the combination of meta-heuristics under a hyper-heuristic framework an effective Financial Forecasting approach.
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IEEE Congress on Evolutionary Computation - Metaheuristics application on a Financial Forecasting problem
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Dafni Smonou, Michael Kampouridis, Edward TsangAbstract:EDDIE is a Genetic Programming (GP) tool, which is used to tackle problems in the field of Financial Forecasting. The novelty of EDDIE is in its grammar, which allows the GP to look in the space of technical analysis indicators, instead of using prespecified ones, as it normally happens in the literature. The advantage of this is that EDDIE is not constrained to use prespecified indicators; instead, thanks to its grammar, it can choose any indicators within a pre-defined range, leading to new solutions that might have never been discovered before. However, a disadvantage of the above approach is that the algorithm's search space is dramatically larger, and as a result good solutions can sometimes be missed due to ineffective search. This paper presents an attempt to deal with this issue by applying to the GP three different meta-heuristics, namely Simulated Annealing, Tabu Search, and Guided Local Search. Results show that the algorithm's performance significantly improves, thus making the combination of Genetic Programming and meta-heuristics an effective Financial Forecasting approach.
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On the investigation of hyper-heuristics on a Financial Forecasting problem
Annals of Mathematics and Artificial Intelligence, 2012Co-Authors: Michael Kampouridis, Abdullah Alsheddy, Edward TsangAbstract:Financial Forecasting is a really important area in computational finance, with numerous works in the literature. This importance can be reflected in the literature by the continuous development of new algorithms. Hyper-heuristics have been successfully used in the past for a number of search and optimization problems, and have shown very promising results. To the best of our knowledge, they have not been used for Financial Forecasting. In this paper we present pioneer work, where we use different hyper-heuristics frameworks to investigate whether we can improve the performance of a Financial Forecasting tool called EDDIE 8. EDDIE 8 allows the GP (Genetic Programming) to search in the search space of indicators for solutions, instead of using pre-specified ones; as a result, its search area has dramatically increased and sometimes solutions can be missed due to ineffective search. We apply 14 different low-level heuristics to EDDIE 8, to 30 different datasets, and examine their effect to the algorithm's performance. We then select the most prominent heuristics and combine them into three different hyper-heuristics frameworks. Results show that all three frameworks are competitive, and are able to show significantly improved results, especially in the case of best results. Lastly, analysis on the weights of the heuristics shows that there can be a constant swinging among some of the low-level heuristics, which denotes that the hyper-heuristics frameworks are able to `know' the appropriate time to switch from one heuristic to the other, based on their effectiveness.
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LION - Using hyperheuristics under a GP framework for Financial Forecasting
Lecture Notes in Computer Science, 2011Co-Authors: Michael Kampouridis, Edward TsangAbstract:Hyperheuristics have successfully been used in the past for a number of search and optimization problems. To the best of our knowledge, they have not been used for Financial Forecasting. In this paper we use a simple hyperheuristics framework to investigate whether we can improve the performance of a Financial Forecasting tool called EDDIE 8. EDDIE 8 allows the GP (Genetic Programming) to search in the search space of indicators for solutions, instead of using pre-specified ones; as a result, its search area is quite big and sometimes solutions can be missed due to ineffective search. We thus use two different heuristics and two different mutators combined under a simple hyperheuristics framework. We run experiments under five datasets from FTSE 100 and discover that on average, the new version can return improved solutions. In addition, the rate of missing opportunities reaches it's minimum value, under all datasets tested in this paper. This is a very important finding, because it indicates that thanks to the hyperheuristics EDDIE 8 has the potential of missing less Forecasting opportunities. Finally, results suggest that thanks to the introduction of hyperheuristics, the search has become more effective and more areas of the space have been explored.