Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/237137 
Year of Publication: 
2018
Citation: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 4 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2018 [Pages:] 1-22
Publisher: 
Springer, Heidelberg
Abstract: 
Accurate forecasting of changes in stock market indices can provide financial managers and individual investors with strategically valuable information. However, predicting the closing prices of stock indices remains a challenging task because stock price movements are characterized by high volatility and nonlinearity. This paper proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices. We developed a model that uses partial descriptions (PDs) and is limited to only two layers for the PNN architecture. The outputs of these PDs along with the original features are fed to a single output neuron, and the synaptic weight values and biases of the CPNN are optimized by a genetic algorithm. The proposed model was evaluated by predicting the next day's closing price of five fast-growing stock indices: the BSE, DJIA, NASDAQ, FTSE, and TAIEX. In comparative testing, the proposed model proved its ability to provide closing price predictions with superior accuracy. Further, the Deibold-Mariano test justified the statistical significance of the model, establishing that this approach can be adopted as a competent financial forecasting tool.
Subjects: 
Stock market forecasting
Polynomial neural network
Partial description
Genetic algorithm
Multilayer perceptron
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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