Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237181 
Year of Publication: 
2019
Citation: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 5 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2019 [Pages:] 1-34
Publisher: 
Springer, Heidelberg
Abstract: 
Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.
Subjects: 
Artificial neural network
Neuro-fuzzy network
Multilayer perceptron
Chemical reaction optimization
Stock market forecasting
Financial time series forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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