Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction
International Journal of Intelligent Computing and Cybernetics
Article publication date: 15 May 2019
Issue publication date: 15 May 2019
Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.
A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.
Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.
The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.
Rath, S., Sahu, B.K. and Nayak, M.R. (2019), "Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction", International Journal of Intelligent Computing and Cybernetics, Vol. 12 No. 2, pp. 175-193. https://doi.org/10.1108/IJICC-10-2018-0145
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