18,561 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
An empirical methodology for developing stockmarket trading systems using artificial neural networks
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Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
Finding kernel function for stock market prediction with support vector regression
Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction
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