13,183 research outputs found
A forecasting of indices and corresponding investment decision making application
Student Number : 9702018F -
MSc(Eng) Dissertation -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built EnvironmentDue to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future
financial necessities. This research proposes an application, which employs computational intelligent methods that could
assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is
employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection
Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in
possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial
Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading
strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg
Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been
determined that the MLP neural network architecture is particularly suited in the prediction of closing index price
performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the
Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree
designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as
scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of
the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16
classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural
networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4
classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of
concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System
implementation of this design performed equally well
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
An Improved Stock Price Prediction using Hybrid Market Indicators
In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained
with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
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