Estimating stock closing indices using a GA-weighted condensed polynomial neural network

Sarat Chandra Nayak () and Bijan Bihari Misra ()
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Sarat Chandra Nayak: CMR College of Engineering &Technology (Autonomous)
Bijan Bihari Misra: Silicon Institute of Technology

Financial Innovation, 2018, vol. 4, issue 1, 1-22

Abstract: 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.

Keywords: Stock market forecasting; Polynomial neural network; Partial description; Genetic algorithm; Multilayer perceptron (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (12)

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DOI: 10.1186/s40854-018-0104-2

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